All Population Simulators (Including Depreciated).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03bfb0f1",
   "metadata": {},
   "source": [
    "# Population Simulator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e52425b",
   "metadata": {},
   "source": [
    "## Basic Population Simulator with Ageing Birthing and Murder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dbe9b13",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the Individual class\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.age = 0\n",
    "        self.lifespan = lifespan\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def mutate(self):\n",
    "        # Mutation logic: small random changes to traits\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.05, 0.05)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.05, 0.05)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.05, 0.05)))\n",
    "\n",
    "# Define the Population class\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        aggression = random.uniform(0, 1)\n",
    "        peacefulness = random.uniform(0, 1)\n",
    "        birth_rate = random.uniform(0, 1)\n",
    "        return Individual(aggression, peacefulness, birth_rate)\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        # Aging individuals\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "        \n",
    "        # Murder events based on aggression\n",
    "        survivors = []\n",
    "        for ind in self.individuals:\n",
    "            if random.random() > ind.aggression:\n",
    "                survivors.append(ind)\n",
    "        \n",
    "        self.individuals = survivors\n",
    "        \n",
    "        # Birth events based on birth rate\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if random.random() < ind.birth_rate:\n",
    "                new_births.append(self.create_random_individual())\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "    \n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "        \n",
    "        return population_sizes\n",
    "\n",
    "# Initial simulation\n",
    "population = Population(100)\n",
    "population_sizes = population.simulate_generations(50)\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "642ed47e",
   "metadata": {},
   "source": [
    "## Population Simulator with Additional Concept of Intelligence, Health, Wealth, Socialising, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ebbc4fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the Individual class with additional traits\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.age = 0\n",
    "        self.lifespan = lifespan\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def mutate(self):\n",
    "        # Mutation logic: small random changes to traits\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.05, 0.05)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.05, 0.05)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.05, 0.05)))\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.05, 0.05)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.05, 0.05)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.05, 0.05)))\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.05, 0.05)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.05, 0.05)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.05, 0.05)))\n",
    "\n",
    "# Define the Population class with complex interactions\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        # Randomly initialize an individual with all traits\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0, 1),\n",
    "            intelligence=random.uniform(0, 1),\n",
    "            social_cohesion=random.uniform(0, 1),\n",
    "            health=random.uniform(0, 1),\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0, 1),\n",
    "            curiosity=random.uniform(0, 1)\n",
    "        )\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        # Aging individuals\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "        \n",
    "        # Handle interactions\n",
    "        survivors = []\n",
    "        for ind in self.individuals:\n",
    "            # Murder chance influenced by aggression and peacefulness\n",
    "            murder_risk = ind.aggression - ind.peacefulness\n",
    "            if random.random() > murder_risk:\n",
    "                survivors.append(ind)\n",
    "        \n",
    "        self.individuals = survivors\n",
    "        \n",
    "        # Birth events influenced by birth rate, health, and wealth\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth)\n",
    "            if random.random() < birth_probability:\n",
    "                new_births.append(self.create_random_individual())\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Social cohesion can protect against murders\n",
    "        social_protection = sum(ind.social_cohesion for ind in self.individuals) / len(self.individuals)\n",
    "        if social_protection > 0.5:\n",
    "            survivors = []\n",
    "            for ind in self.individuals:\n",
    "                if random.random() > 0.1:  # Reduced murder rate due to social cohesion\n",
    "                    survivors.append(ind)\n",
    "            self.individuals = survivors\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "        \n",
    "        return population_sizes\n",
    "\n",
    "# Enhanced simulation\n",
    "population = Population(10)\n",
    "population_sizes = population.simulate_generations(50)\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations (Enhanced Model)\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e5e2ae1",
   "metadata": {},
   "source": [
    "## Population Simulator With Even More Features Such as Resources, Disease Spread, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f242f5a1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Define constants\n",
    "INITIAL_RESOURCES = 1000  # Initial resources available\n",
    "RESOURCE_REGENERATION = 100  # Resources regenerated each year\n",
    "CARRYING_CAPACITY = 500  # Maximum population the environment can support\n",
    "\n",
    "# Define the Individual class with additional traits and aging factors\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        # Fertility is higher between ages 18 and 40\n",
    "        return 18 <= self.age <= 40\n",
    "\n",
    "    def mutate(self):\n",
    "        # Mutation logic: small random changes to traits\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.05, 0.05)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.05, 0.05)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.05, 0.05)))\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.05, 0.05)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.05, 0.05)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.05, 0.05)))\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.05, 0.05)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.05, 0.05)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.05, 0.05)))\n",
    "\n",
    "# Define the Population class with resource management and disease outbreaks\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.resources = INITIAL_RESOURCES\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0, 1),\n",
    "            intelligence=random.uniform(0, 1),\n",
    "            social_cohesion=random.uniform(0, 1),\n",
    "            health=random.uniform(0, 1),\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0, 1),\n",
    "            curiosity=random.uniform(0, 1)\n",
    "        )\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        # Age individuals\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum(ind.health for ind in self.individuals)\n",
    "        if total_resource_demand > self.resources:\n",
    "            # Not enough resources for everyone, some individuals will die based on their health\n",
    "            survival_chance = self.resources / total_resource_demand\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Murder events\n",
    "        survivors = []\n",
    "        for ind in self.individuals:\n",
    "            murder_risk = ind.aggression - ind.peacefulness\n",
    "            if random.random() > murder_risk:\n",
    "                survivors.append(ind)\n",
    "        \n",
    "        self.individuals = survivors\n",
    "        \n",
    "        # Birth events influenced by fertility, birth rate, and resource availability\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.resources / (CARRYING_CAPACITY * 2))\n",
    "                if random.random() < birth_probability:\n",
    "                    new_births.append(self.create_random_individual())\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "        \n",
    "        # Disease outbreak\n",
    "        if random.random() < 0.1:  # 10% chance of a disease outbreak\n",
    "            disease_severity = random.uniform(0, 0.5)\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > (disease_severity * (1 - ind.health))]\n",
    "        \n",
    "        # Resource regeneration\n",
    "        self.resources = min(INITIAL_RESOURCES, self.resources + RESOURCE_REGENERATION)\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "        \n",
    "        return population_sizes\n",
    "\n",
    "# Enhanced simulation with growth controls\n",
    "population = Population(100)\n",
    "population_sizes = population.simulate_generations(100)\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations with Growth Controls\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cbc2bfd",
   "metadata": {},
   "source": [
    "## Population Simulator With Even More Features Such as Climate Change, Natural Disasters, and Seasons "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed96d0c4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define constants\n",
    "INITIAL_RESOURCES = 1500  # Reduced initial resources available\n",
    "RESOURCE_REGENERATION = 100  # Reduced resources regenerated each year\n",
    "CARRYING_CAPACITY = 400  # Maximum population the environment can support\n",
    "\n",
    "# Environmental factors\n",
    "SEASONAL_EFFECT = 0.15  # Adjusted seasonal variation in resource availability\n",
    "CLIMATE_VARIABILITY = 0.05  # Kept the random changes in climate affecting resource regeneration\n",
    "DISASTER_PROBABILITY = 0.03  # Moderate probability of a natural disaster occurring each year\n",
    "\n",
    "# Define the Individual class with additional traits and aging factors\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        if self.age > 40:\n",
    "            self.health *= 0.95  # Health declines with age\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        # Fertility declines after age 30\n",
    "        return 18 <= self.age <= 40\n",
    "\n",
    "    def mutate(self):\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.03, 0.03)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.03, 0.03)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.03, 0.03)))\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.03, 0.03)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.03, 0.03)))\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.03, 0.03)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.03, 0.03)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.03, 0.03)))\n",
    "\n",
    "# Define the Population class with resource management and more complex dynamics\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.resources = INITIAL_RESOURCES\n",
    "        self.season = \"Spring\"\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0, 1),\n",
    "            intelligence=random.uniform(0, 1),\n",
    "            social_cohesion=random.uniform(0, 1),\n",
    "            health=random.uniform(0, 1),\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0, 1),\n",
    "            curiosity=random.uniform(0, 1)\n",
    "        )\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Environmental effects: seasonal and climate variability\n",
    "        self.update_season()\n",
    "        seasonal_factor = self.get_seasonal_factor()\n",
    "        climate_factor = 1 + random.uniform(-CLIMATE_VARIABILITY, CLIMATE_VARIABILITY)\n",
    "        self.resources *= seasonal_factor * climate_factor\n",
    "\n",
    "        # Natural disaster\n",
    "        if random.random() < DISASTER_PROBABILITY:\n",
    "            disaster_impact = random.uniform(0.1, 0.3)\n",
    "            self.resources -= self.resources * disaster_impact\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > disaster_impact]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum(ind.health * 1.5 for ind in self.individuals)  # Increased resource demand\n",
    "        if total_resource_demand > self.resources:\n",
    "            survival_chance = self.resources / total_resource_demand\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Social dynamics: Family units and social cohesion\n",
    "        survivors = []\n",
    "        for ind in self.individuals:\n",
    "            murder_risk = ind.aggression - ind.peacefulness\n",
    "            social_protection = ind.social_cohesion * 0.2\n",
    "            if random.random() > (murder_risk - social_protection):\n",
    "                survivors.append(ind)\n",
    "        \n",
    "        self.individuals = survivors\n",
    "        \n",
    "        # Birth events influenced by fertility, birth rate, resource availability, and social structure\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.resources / (CARRYING_CAPACITY * 2))  # More challenging birth conditions\n",
    "                if random.random() < birth_probability:\n",
    "                    child = self.create_random_individual()\n",
    "                    child.health = (ind.health + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.intelligence = (ind.intelligence + random.uniform(-0.05, 0.05)) / 2\n",
    "                    new_births.append(child)\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Resource regeneration based on season and climate factors\n",
    "        self.resources = min(INITIAL_RESOURCES, self.resources + RESOURCE_REGENERATION * seasonal_factor * climate_factor)\n",
    "\n",
    "    def update_season(self):\n",
    "        if self.season == \"Spring\":\n",
    "            self.season = \"Summer\"\n",
    "        elif self.season == \"Summer\":\n",
    "            self.season = \"Autumn\"\n",
    "        elif self.season == \"Autumn\":\n",
    "            self.season = \"Winter\"\n",
    "        elif self.season == \"Winter\":\n",
    "            self.season = \"Spring\"\n",
    "    \n",
    "    def get_seasonal_factor(self):\n",
    "        if self.season == \"Spring\":\n",
    "            return 1.1\n",
    "        elif self.season == \"Summer\":\n",
    "            return 1.0\n",
    "        elif self.season == \"Autumn\":\n",
    "            return 0.85\n",
    "        elif self.season == \"Winter\":\n",
    "            return 0.7\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "        \n",
    "        return population_sizes\n",
    "\n",
    "# Run the modified simulation\n",
    "population = Population(100)\n",
    "population_sizes = population.simulate_generations(100)\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations with Controlled Growth\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "307ebfd1",
   "metadata": {},
   "source": [
    "## Population Simulator With Added Feature of Technology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b17834d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define constants\n",
    "INITIAL_FOOD = 1000\n",
    "INITIAL_WATER = 1000\n",
    "INITIAL_SHELTER = 1000\n",
    "\n",
    "FOOD_REGENERATION = 100\n",
    "WATER_REGENERATION = 100\n",
    "SHELTER_REGENERATION = 50\n",
    "\n",
    "CARRYING_CAPACITY = 400\n",
    "\n",
    "# Environmental factors\n",
    "SEASONAL_EFFECT = 0.15\n",
    "CLIMATE_VARIABILITY = 0.05\n",
    "DISASTER_PROBABILITY = 0.03\n",
    "\n",
    "# Define the Individual class with additional traits, aging, and technology\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "        self.technology_level = 0  # New trait to track technology development\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        if self.age > 40:\n",
    "            self.health *= 0.95  # Health declines with age\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        return 18 <= self.age <= 40\n",
    "\n",
    "    def mutate(self):\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.03, 0.03)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.03, 0.03)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.03, 0.03)))\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.03, 0.03)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.03, 0.03)))\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.03, 0.03)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.03, 0.03)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.03, 0.03)))\n",
    "        self.technology_level = max(0, min(10, self.technology_level + random.uniform(-0.01, 0.02)))\n",
    "\n",
    "# Define the Population class with resource management, technology, and culture\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.food = INITIAL_FOOD\n",
    "        self.water = INITIAL_WATER\n",
    "        self.shelter = INITIAL_SHELTER\n",
    "        self.season = \"Spring\"\n",
    "        self.technology_progress = 0  # Track overall technological progress\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0, 1),\n",
    "            intelligence=random.uniform(0, 1),\n",
    "            social_cohesion=random.uniform(0, 1),\n",
    "            health=random.uniform(0, 1),\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0, 1),\n",
    "            curiosity=random.uniform(0, 1)\n",
    "        )\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Environmental effects: seasonal and climate variability\n",
    "        self.update_season()\n",
    "        seasonal_factor = self.get_seasonal_factor()\n",
    "        climate_factor = 1 + random.uniform(-CLIMATE_VARIABILITY, CLIMATE_VARIABILITY)\n",
    "        self.food *= seasonal_factor * climate_factor\n",
    "        self.water *= seasonal_factor * climate_factor\n",
    "        self.shelter *= seasonal_factor * climate_factor\n",
    "\n",
    "        # Natural disaster\n",
    "        if random.random() < DISASTER_PROBABILITY:\n",
    "            disaster_impact = random.uniform(0.1, 0.3)\n",
    "            self.food -= self.food * disaster_impact\n",
    "            self.water -= self.water * disaster_impact\n",
    "            self.shelter -= self.shelter * disaster_impact\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > disaster_impact]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum((ind.health * 1.5 + ind.technology_level * 0.1) for ind in self.individuals)  # Increased resource demand with technology\n",
    "        total_food_demand = total_resource_demand * 0.4\n",
    "        total_water_demand = total_resource_demand * 0.4\n",
    "        total_shelter_demand = total_resource_demand * 0.2\n",
    "\n",
    "        # Check resource limits\n",
    "        if total_food_demand > self.food or total_water_demand > self.water or total_shelter_demand > self.shelter:\n",
    "            survival_chance = min(self.food / total_food_demand, self.water / total_water_demand, self.shelter / total_shelter_demand)\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Social dynamics: Family units, social cohesion, and cultural spread\n",
    "        survivors = []\n",
    "        for ind in self.individuals:\n",
    "            murder_risk = ind.aggression - ind.peacefulness\n",
    "            social_protection = ind.social_cohesion * 0.2\n",
    "            if random.random() > (murder_risk - social_protection):\n",
    "                survivors.append(ind)\n",
    "        \n",
    "        self.individuals = survivors\n",
    "        \n",
    "        # Technology development and cultural traits spread\n",
    "        for ind in self.individuals:\n",
    "            if random.random() < ind.intelligence * 0.05:  # Probability of innovation\n",
    "                ind.technology_level += 0.1  # Increment technology level\n",
    "            if random.random() < ind.cooperation * 0.1:  # Probability of knowledge sharing\n",
    "                self.technology_progress += 0.1\n",
    "        \n",
    "        # Birth events influenced by fertility, birth rate, resource availability, and technology\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.food / (CARRYING_CAPACITY * 2)) * (1 + ind.technology_level * 0.05)\n",
    "                if random.random() < birth_probability:\n",
    "                    child = self.create_random_individual()\n",
    "                    child.health = (ind.health + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.intelligence = (ind.intelligence + random.uniform(-0.05, 0.05)) / 2\n",
    "                    new_births.append(child)\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Resource regeneration based on season and climate factors\n",
    "        self.food = min(INITIAL_FOOD, self.food + FOOD_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.water = min(INITIAL_WATER, self.water + WATER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.shelter = min(INITIAL_SHELTER, self.shelter + SHELTER_REGENERATION * seasonal_factor * climate_factor)\n",
    "\n",
    "    def update_season(self):\n",
    "        if self.season == \"Spring\":\n",
    "            self.season = \"Summer\"\n",
    "        elif self.season == \"Summer\":\n",
    "            self.season = \"Autumn\"\n",
    "        elif self.season == \"Autumn\":\n",
    "            self.season = \"Winter\"\n",
    "        elif self.season == \"Winter\":\n",
    "            self.season = \"Spring\"\n",
    "    \n",
    "    def get_seasonal_factor(self):\n",
    "        if self.season == \"Spring\":\n",
    "            return 1.1\n",
    "        elif self.season == \"Summer\":\n",
    "            return 1.0\n",
    "        elif self.season == \"Autumn\":\n",
    "            return 0.85\n",
    "        elif self.season == \"Winter\":\n",
    "            return 0.7\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        technology_levels = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "            technology_levels.append(self.technology_progress / len(self.individuals) if len(self.individuals) > 0 else 0)\n",
    "        \n",
    "        return population_sizes, technology_levels\n",
    "\n",
    "# Run the advanced simulation\n",
    "population = Population(100)\n",
    "population_sizes, technology_levels = population.simulate_generations(100)\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations with Advanced Features\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "261b9bc9",
   "metadata": {},
   "source": [
    " ## Population Simulator With Even More Features Such as Environment Adaptation, Territory and Territorial Expansion, and Leadership"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fc0ddd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define constants\n",
    "INITIAL_FOOD = 1000\n",
    "INITIAL_WATER = 1000\n",
    "INITIAL_SHELTER = 1000\n",
    "\n",
    "FOOD_REGENERATION = 100\n",
    "WATER_REGENERATION = 100\n",
    "SHELTER_REGENERATION = 50\n",
    "\n",
    "CARRYING_CAPACITY = 400\n",
    "\n",
    "# Environmental factors\n",
    "SEASONAL_EFFECT = 0.15\n",
    "CLIMATE_VARIABILITY = 0.05\n",
    "DISASTER_PROBABILITY = 0.03\n",
    "LONG_TERM_CLIMATE_CHANGE = 0.001  # Gradual climate change effect\n",
    "\n",
    "# Define the Individual class with additional traits, aging, technology, and adaptation\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "        self.technology_level = 0\n",
    "        self.environmental_adaptation = random.uniform(0, 1)  # New trait for environmental adaptation\n",
    "        self.group = None  # New attribute to track group membership\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        if self.age > 40:\n",
    "            self.health *= 0.95  # Health declines with age\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        return 18 <= self.age <= 40\n",
    "\n",
    "    def mutate(self):\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.03, 0.03)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.03, 0.03)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.03, 0.03)))\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.03, 0.03)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.03, 0.03)))\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.03, 0.03)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.03, 0.03)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.03, 0.03)))\n",
    "        self.technology_level = max(0, min(10, self.technology_level + random.uniform(-0.01, 0.02)))\n",
    "        self.environmental_adaptation = max(0, min(1, self.environmental_adaptation + random.uniform(-0.02, 0.02)))  # Mutation in adaptation\n",
    "\n",
    "# Define the Group class to manage groups of individuals\n",
    "class Group:\n",
    "    def __init__(self, leader):\n",
    "        self.leader = leader\n",
    "        self.members = [leader]\n",
    "        self.resources = {\"food\": 0, \"water\": 0, \"shelter\": 0}\n",
    "        self.territory = random.uniform(0, 1)  # Territory richness\n",
    "\n",
    "    def add_member(self, individual):\n",
    "        individual.group = self\n",
    "        self.members.append(individual)\n",
    "\n",
    "    def manage_resources(self):\n",
    "        # Allocate resources among members\n",
    "        for resource in self.resources:\n",
    "            if self.resources[resource] > 0:\n",
    "                allocation = self.resources[resource] / len(self.members)\n",
    "                for member in self.members:\n",
    "                    if resource == \"food\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"water\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"shelter\":\n",
    "                        member.health += allocation * 0.05\n",
    "\n",
    "    def compete_for_resources(self, other_group):\n",
    "        # Simple competition logic\n",
    "        if self.territory + random.uniform(-0.1, 0.1) > other_group.territory:\n",
    "            self.resources[\"food\"] += other_group.resources[\"food\"] * 0.5\n",
    "            other_group.resources[\"food\"] *= 0.5\n",
    "\n",
    "# Define the Population class with groups, migration, and advanced dynamics\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.groups = []\n",
    "        self.food = INITIAL_FOOD\n",
    "        self.water = INITIAL_WATER\n",
    "        self.shelter = INITIAL_SHELTER\n",
    "        self.season = \"Spring\"\n",
    "        self.technology_progress = 0\n",
    "        self.climate_trend = 0  # Track long-term climate trends\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0, 1),\n",
    "            intelligence=random.uniform(0, 1),\n",
    "            social_cohesion=random.uniform(0, 1),\n",
    "            health=random.uniform(0, 1),\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0, 1),\n",
    "            curiosity=random.uniform(0, 1)\n",
    "        )\n",
    "    \n",
    "    def form_group(self, leader):\n",
    "        group = Group(leader)\n",
    "        self.groups.append(group)\n",
    "        return group\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Environmental effects: seasonal, climate variability, and long-term trends\n",
    "        self.update_season()\n",
    "        seasonal_factor = self.get_seasonal_factor()\n",
    "        climate_factor = 1 + random.uniform(-CLIMATE_VARIABILITY, CLIMATE_VARIABILITY) + self.climate_trend\n",
    "        self.food *= seasonal_factor * climate_factor\n",
    "        self.water *= seasonal_factor * climate_factor\n",
    "        self.shelter *= seasonal_factor * climate_factor\n",
    "\n",
    "        # Long-term climate change\n",
    "        self.climate_trend += LONG_TERM_CLIMATE_CHANGE * random.choice([-1, 1])\n",
    "\n",
    "        # Natural disaster\n",
    "        if random.random() < DISASTER_PROBABILITY:\n",
    "            disaster_impact = random.uniform(0.2, 0.5)\n",
    "            self.food -= self.food * disaster_impact\n",
    "            self.water -= self.water * disaster_impact\n",
    "            self.shelter -= self.shelter * disaster_impact\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > disaster_impact]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum((ind.health * 1.5 + ind.technology_level * 0.1) for ind in self.individuals)\n",
    "        total_food_demand = total_resource_demand * 0.4\n",
    "        total_water_demand = total_resource_demand * 0.4\n",
    "        total_shelter_demand = total_resource_demand * 0.2\n",
    "\n",
    "        # Check resource limits\n",
    "        if total_food_demand > self.food or total_water_demand > self.water or total_shelter_demand > self.shelter:\n",
    "            survival_chance = min(self.food / total_food_demand, self.water / total_water_demand, self.shelter / total_shelter_demand)\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Social dynamics: Group formation, migration, and competition\n",
    "        for ind in self.individuals:\n",
    "            if ind.group is None:\n",
    "                leader = ind if ind.intelligence > random.uniform(0, 1) else random.choice(self.individuals)\n",
    "                group = self.form_group(leader)\n",
    "                group.add_member(ind)\n",
    "            elif random.random() < ind.curiosity * 0.05:\n",
    "                # Migration to a new group\n",
    "                new_group = self.form_group(ind)\n",
    "                new_group.add_member(ind)\n",
    "        \n",
    "        # Group management\n",
    "        for group in self.groups:\n",
    "            group.manage_resources()\n",
    "\n",
    "            if random.random() < 0.1:  # Random chance of competition\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.compete_for_resources(other_group)\n",
    "\n",
    "        # Technology development and adaptation\n",
    "        for ind in self.individuals:\n",
    "            if random.random() < ind.intelligence * 0.05:\n",
    "                ind.technology_level += 0.1\n",
    "            if random.random() < ind.cooperation * 0.1:\n",
    "                self.technology_progress += 0.1\n",
    "\n",
    "            # Environmental adaptation\n",
    "            if self.climate_trend > 0:\n",
    "                ind.health *= (1 + ind.environmental_adaptation * 0.1)\n",
    "            elif self.climate_trend < 0:\n",
    "                ind.health *= (1 - ind.environmental_adaptation * 0.1)\n",
    "        \n",
    "        # Birth events influenced by technology and adaptation\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.food / (CARRYING_CAPACITY * 2)) * (1 + ind.technology_level * 0.05)\n",
    "                if random.random() < birth_probability:\n",
    "                    child = self.create_random_individual()\n",
    "                    child.health = (ind.health + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.intelligence = (ind.intelligence + random.uniform(-0.05, 0.05)) / 2\n",
    "                    new_births.append(child)\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Resource regeneration\n",
    "        self.food = min(INITIAL_FOOD, self.food + FOOD_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.water = min(INITIAL_WATER, self.water + WATER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.shelter = min(INITIAL_SHELTER, self.shelter + SHELTER_REGENERATION * seasonal_factor * climate_factor)\n",
    "\n",
    "    def update_season(self):\n",
    "        if self.season == \"Spring\":\n",
    "            self.season = \"Summer\"\n",
    "        elif self.season == \"Summer\":\n",
    "            self.season = \"Autumn\"\n",
    "        elif self.season == \"Autumn\":\n",
    "            self.season = \"Winter\"\n",
    "        elif self.season == \"Winter\":\n",
    "            self.season = \"Spring\"\n",
    "    \n",
    "    def get_seasonal_factor(self):\n",
    "        if self.season == \"Spring\":\n",
    "            return 1.1\n",
    "        elif self.season == \"Summer\":\n",
    "            return 1.0\n",
    "        elif self.season == \"Autumn\":\n",
    "            return 0.85\n",
    "        elif self.season == \"Winter\":\n",
    "            return 0.7\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        technology_levels = []\n",
    "        climate_trends = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "            technology_levels.append(self.technology_progress / len(self.individuals) if len(self.individuals) > 0 else 0)\n",
    "            climate_trends.append(self.climate_trend)\n",
    "        \n",
    "        return population_sizes, technology_levels, climate_trends\n",
    "\n",
    "# Run the advanced simulation\n",
    "population = Population(100)\n",
    "population_sizes, technology_levels, climate_trends = population.simulate_generations(180)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba741a1",
   "metadata": {},
   "source": [
    "## Even More Complex Population Simulator with All Social Constructs Such as Religion, Tradition. Concepts of Trade, Risk Taking, Mental Health, EQ and IQ, Leadership Style, Immunity, War, Laws, Governance, Language, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ce22431",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Constants for resources and environment\n",
    "INITIAL_FOOD = 2000  # Increased initial resources available\n",
    "INITIAL_WATER = 2000\n",
    "INITIAL_SHELTER = 1500\n",
    "FOOD_REGENERATION = 200  # Increased resources regenerated each year\n",
    "WATER_REGENERATION = 200\n",
    "SHELTER_REGENERATION = 100\n",
    "MEDICINE_REGENERATION = 50\n",
    "TOOLS_REGENERATION = 50\n",
    "\n",
    "CARRYING_CAPACITY = 400\n",
    "\n",
    "# Environmental factors\n",
    "SEASONAL_EFFECT = 0.15\n",
    "CLIMATE_VARIABILITY = 0.05\n",
    "DISASTER_PROBABILITY = 0.02  # Reduced probability of a catastrophic event\n",
    "LONG_TERM_CLIMATE_CHANGE = 0.001  # Gradual climate change effect\n",
    "CATASTROPHIC_EVENT_PROBABILITY = 0.005  # Reduced probability of catastrophic events\n",
    "DISEASE_SPREAD_PROBABILITY = 0.05  # Reduced probability of disease outbreak\n",
    "CLIMATE_CYCLE_LENGTH = 50  # Length of climate cycles (e.g., ice ages)\n",
    "\n",
    "# Define the Individual class with full complexity\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, technology_level, environmental_adaptation, risk_taking, tradition, religion, leadership_style, emotional_intelligence, mental_health, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.technology_level = technology_level\n",
    "        self.environmental_adaptation = environmental_adaptation\n",
    "        self.risk_taking = risk_taking\n",
    "        self.tradition = tradition\n",
    "        self.religion = religion\n",
    "        self.leadership_style = leadership_style\n",
    "        self.emotional_intelligence = emotional_intelligence\n",
    "        self.mental_health = mental_health\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "        self.group = None  # Track group membership\n",
    "        self.language = random.uniform(0, 1)  # Language diversity\n",
    "        self.immune_system = random.uniform(0.5, 1)  # Increased base immunity to diseases\n",
    "        self.symbiotic_relationship = False  # Whether the individual has a symbiotic relationship with another species\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        if self.age > 40:\n",
    "            self.health *= 0.98  # Slower health decline with age\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        return 18 <= self.age <= 40\n",
    "\n",
    "    def mutate(self):\n",
    "        # Complex mutations with potential for adaptation and technology improvement\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.03, 0.03)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.03, 0.03)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on birth rate\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.03, 0.03)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on health\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.03, 0.03)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.03, 0.03)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.03, 0.03)))\n",
    "        self.technology_level = max(0, min(10, self.technology_level + random.uniform(-0.01, 0.02)))\n",
    "        self.environmental_adaptation = max(0, min(1, self.environmental_adaptation + random.uniform(-0.02, 0.02)))\n",
    "        self.risk_taking = max(0, min(1, self.risk_taking + random.uniform(-0.03, 0.03)))\n",
    "        self.tradition = max(0, min(1, self.tradition + random.uniform(-0.03, 0.03)))\n",
    "        self.religion = max(0, min(1, self.religion + random.uniform(-0.03, 0.03)))\n",
    "        self.emotional_intelligence = max(0, min(1, self.emotional_intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.mental_health = max(0, min(1, self.mental_health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on mental health\n",
    "        self.immune_system = max(0.5, min(1, self.immune_system + random.uniform(-0.01, 0.01)))  # Reduced mutation impact on immunity\n",
    "\n",
    "# Define the Group class to manage groups of individuals\n",
    "class Group:\n",
    "    def __init__(self, leader):\n",
    "        self.leader = leader\n",
    "        self.members = [leader]\n",
    "        self.resources = {\"food\": 0, \"water\": 0, \"shelter\": 0, \"medicine\": 0, \"tools\": 0}\n",
    "        self.territory = random.uniform(0.5, 1)  # Increase base territory richness\n",
    "        self.cultural_practices = random.uniform(0.5, 1)  # More shared cultural practices\n",
    "        self.language = leader.language  # Shared language within the group\n",
    "        self.trade_networks = 0  # Track trade relationships with other groups\n",
    "        self.governance_structure = leader.leadership_style  # Initial governance style\n",
    "        self.laws = {}  # Simple legal system\n",
    "\n",
    "    def add_member(self, individual):\n",
    "        individual.group = self\n",
    "        self.members.append(individual)\n",
    "\n",
    "    def manage_resources(self):\n",
    "        # Allocate resources among members\n",
    "        for resource in self.resources:\n",
    "            if self.resources[resource] > 0:\n",
    "                allocation = self.resources[resource] / len(self.members)\n",
    "                for member in self.members:\n",
    "                    if resource == \"food\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"water\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"shelter\":\n",
    "                        member.health += allocation * 0.05\n",
    "                    elif resource == \"medicine\":\n",
    "                        member.health += allocation * 0.2\n",
    "                    elif resource == \"tools\":\n",
    "                        member.technology_level += allocation * 0.1\n",
    "\n",
    "    def trade_with_group(self, other_group):\n",
    "        # Trade resources between groups based on their trade networks and resources\n",
    "        if self.trade_networks > 0 and other_group.trade_networks > 0:\n",
    "            trade_amount = min(self.resources[\"tools\"], other_group.resources[\"medicine\"])\n",
    "            self.resources[\"tools\"] -= trade_amount\n",
    "            other_group.resources[\"medicine\"] += trade_amount\n",
    "\n",
    "    def compete_for_resources(self, other_group):\n",
    "        # Complex competition logic including technology and leadership influence\n",
    "        if (self.territory + self.leader.intelligence + self.leader.technology_level + random.uniform(-0.1, 0.1)) > \\\n",
    "           (other_group.territory + other_group.leader.intelligence + other_group.leader.technology_level):\n",
    "            self.resources[\"food\"] += other_group.resources[\"food\"] * 0.5\n",
    "            other_group.resources[\"food\"] *= 0.5\n",
    "            self.resources[\"water\"] += other_group.resources[\"water\"] * 0.5\n",
    "            other_group.resources[\"water\"] *= 0.5\n",
    "            self.resources[\"shelter\"] += other_group.resources[\"shelter\"] * 0.5\n",
    "            other_group.resources[\"shelter\"] *= 0.5\n",
    "\n",
    "    def enforce_laws(self):\n",
    "        # Enforce laws to maintain order within the group\n",
    "        if self.governance_structure in ['autocratic', 'democratic']:\n",
    "            for law, enforcement in self.laws.items():\n",
    "                for member in self.members:\n",
    "                    if random.random() > enforcement:\n",
    "                        member.mental_health -= 0.05  # Punish lawbreakers with less severity\n",
    "\n",
    "# Define the Population class with full complexity\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.groups = []\n",
    "        self.food = INITIAL_FOOD\n",
    "        self.water = INITIAL_WATER\n",
    "        self.shelter = INITIAL_SHELTER\n",
    "        self.medicine = MEDICINE_REGENERATION\n",
    "        self.tools = TOOLS_REGENERATION\n",
    "        self.season = \"Spring\"\n",
    "        self.technology_progress = 0\n",
    "        self.climate_trend = 0  # Track long-term climate trends\n",
    "        self.climate_cycle = 0  # Track the climate cycle\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0.5, 1),  # Increased base birth rate\n",
    "            intelligence=random.uniform(0.5, 1),  # Increased base intelligence\n",
    "            social_cohesion=random.uniform(0.5, 1),  # Increased base social cohesion\n",
    "            health=random.uniform(0.5, 1),  # Increased base health\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0.5, 1),  # Increased base cooperation\n",
    "            curiosity=random.uniform(0, 1),\n",
    "            technology_level=random.uniform(0.5, 1),  # Increased base technology level\n",
    "            environmental_adaptation=random.uniform(0.5, 1),  # Increased base environmental adaptation\n",
    "            risk_taking=random.uniform(0, 1),\n",
    "            tradition=random.uniform(0, 1),\n",
    "            religion=random.uniform(0, 1),\n",
    "            leadership_style=random.choice(['autocratic', 'democratic', 'tribal']),\n",
    "            emotional_intelligence=random.uniform(0.5, 1),  # Increased base emotional intelligence\n",
    "            mental_health=random.uniform(0.5, 1)  # Increased base mental health\n",
    "        )\n",
    "    \n",
    "    def form_group(self, leader):\n",
    "        group = Group(leader)\n",
    "        self.groups.append(group)\n",
    "        return group\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Environmental effects: seasonal, climate variability, and long-term trends\n",
    "        self.update_season()\n",
    "        seasonal_factor = self.get_seasonal_factor()\n",
    "        climate_factor = 1 + random.uniform(-CLIMATE_VARIABILITY, CLIMATE_VARIABILITY) + self.climate_trend\n",
    "        self.food *= seasonal_factor * climate_factor\n",
    "        self.water *= seasonal_factor * climate_factor\n",
    "        self.shelter *= seasonal_factor * climate_factor\n",
    "        self.medicine *= seasonal_factor * climate_factor\n",
    "        self.tools *= seasonal_factor * climate_factor\n",
    "\n",
    "        # Long-term climate change\n",
    "        self.climate_trend += LONG_TERM_CLIMATE_CHANGE * random.choice([-1, 1])\n",
    "\n",
    "        # Climate cycles (e.g., ice ages)\n",
    "        self.climate_cycle += 1\n",
    "        if self.climate_cycle >= CLIMATE_CYCLE_LENGTH:\n",
    "            self.climate_cycle = 0\n",
    "            self.climate_trend *= -1  # Reverse the climate trend\n",
    "\n",
    "        # Catastrophic events\n",
    "        if random.random() < CATASTROPHIC_EVENT_PROBABILITY:\n",
    "            catastrophic_impact = random.uniform(0.3, 0.7)  # Reduced impact of catastrophic events\n",
    "            self.food -= self.food * catastrophic_impact\n",
    "            self.water -= self.water * catastrophic_impact\n",
    "            self.shelter -= self.shelter * catastrophic_impact\n",
    "            self.medicine -= self.medicine * catastrophic_impact\n",
    "            self.tools -= self.tools * catastrophic_impact\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > catastrophic_impact]\n",
    "\n",
    "        # Disease outbreak\n",
    "        if random.random() < DISEASE_SPREAD_PROBABILITY:\n",
    "            disease_severity = random.uniform(0.1, 0.3)  # Reduced severity of diseases\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > disease_severity * (1 - ind.immune_system)]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum((ind.health * 1.5 + ind.technology_level * 0.1) for ind in self.individuals)\n",
    "        total_food_demand = total_resource_demand * 0.3\n",
    "        total_water_demand = total_resource_demand * 0.3\n",
    "        total_shelter_demand = total_resource_demand * 0.2\n",
    "        total_medicine_demand = total_resource_demand * 0.1\n",
    "        total_tools_demand = total_resource_demand * 0.1\n",
    "\n",
    "        # Check resource limits\n",
    "        if (total_food_demand > self.food or total_water_demand > self.water or total_shelter_demand > self.shelter or \n",
    "            total_medicine_demand > self.medicine or total_tools_demand > self.tools):\n",
    "            survival_chance = min(self.food / total_food_demand, self.water / total_water_demand, self.shelter / total_shelter_demand,\n",
    "                                  self.medicine / total_medicine_demand, self.tools / total_tools_demand)\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Social dynamics: Group formation, migration, trade, competition, and governance\n",
    "        for ind in self.individuals:\n",
    "            if ind.group is None:\n",
    "                leader = ind if ind.intelligence > random.uniform(0, 1) else random.choice(self.individuals)\n",
    "                group = self.form_group(leader)\n",
    "                group.add_member(ind)\n",
    "            elif random.random() < ind.curiosity * 0.05:\n",
    "                # Migration to a new group\n",
    "                new_group = self.form_group(ind)\n",
    "                new_group.add_member(ind)\n",
    "        \n",
    "        # Group management\n",
    "        for group in self.groups:\n",
    "            group.manage_resources()\n",
    "            group.enforce_laws()\n",
    "\n",
    "            if random.random() < 0.1:  # Random chance of competition\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.compete_for_resources(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of trade\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.trade_with_group(other_group)\n",
    "\n",
    "        # Technology development and adaptation\n",
    "        for ind in self.individuals:\n",
    "            if random.random() < ind.intelligence * 0.05:\n",
    "                ind.technology_level += 0.1\n",
    "            if random.random() < ind.cooperation * 0.1:\n",
    "                self.technology_progress += 0.1\n",
    "\n",
    "            # Environmental adaptation\n",
    "            if self.climate_trend > 0:\n",
    "                ind.health *= (1 + ind.environmental_adaptation * 0.1)\n",
    "            elif self.climate_trend < 0:\n",
    "                ind.health *= (1 - ind.environmental_adaptation * 0.1)\n",
    "        \n",
    "        # Birth events influenced by technology, medicine, adaptation, and cultural traits\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.food / (CARRYING_CAPACITY * 2)) * (1 + ind.technology_level * 0.05)\n",
    "                if random.random() < birth_probability:\n",
    "                    child = self.create_random_individual()\n",
    "                    child.health = (ind.health + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.intelligence = (ind.intelligence + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.language = ind.language  # Inherit language\n",
    "                    child.immune_system = (ind.immune_system + random.uniform(-0.05, 0.05)) / 2  # Inherit immune system\n",
    "                    new_births.append(child)\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Resource regeneration\n",
    "        self.food = min(INITIAL_FOOD, self.food + FOOD_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.water = min(INITIAL_WATER, self.water + WATER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.shelter = min(INITIAL_SHELTER, self.shelter + SHELTER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.medicine = min(MEDICINE_REGENERATION, self.medicine + MEDICINE_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.tools = min(TOOLS_REGENERATION, self.tools + TOOLS_REGENERATION * seasonal_factor * climate_factor)\n",
    "\n",
    "    def update_season(self):\n",
    "        if self.season == \"Spring\":\n",
    "            self.season = \"Summer\"\n",
    "        elif self.season == \"Summer\":\n",
    "            self.season = \"Autumn\"\n",
    "        elif self.season == \"Autumn\":\n",
    "            self.season = \"Winter\"\n",
    "        elif self.season == \"Winter\":\n",
    "            self.season = \"Spring\"\n",
    "    \n",
    "    def get_seasonal_factor(self):\n",
    "        if self.season == \"Spring\":\n",
    "            return 1.1\n",
    "        elif self.season == \"Summer\":\n",
    "            return 1.0\n",
    "        elif self.season == \"Autumn\":\n",
    "            return 0.85\n",
    "        elif self.season == \"Winter\":\n",
    "            return 0.7\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        technology_levels = []\n",
    "        climate_trends = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "            technology_levels.append(self.technology_progress / len(self.individuals) if len(self.individuals) > 0 else 0)\n",
    "            climate_trends.append(self.climate_trend)\n",
    "        \n",
    "        return population_sizes, technology_levels, climate_trends\n",
    "\n",
    "# Run the comprehensive simulation with adjustments to stabilize population\n",
    "population = Population(100)\n",
    "population_sizes, technology_levels, climate_trends = population.simulate_generations(200)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be729343",
   "metadata": {},
   "source": [
    "## Population Simulator with Education Systems, Revolutions, Health Care System, Market, Industrialisation, Even Larger Disasters, Cognitive Enhancements and Problems, Inter-Species Relation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bc69599",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Constants for resources and environment\n",
    "INITIAL_FOOD = 2500  # Further increased initial resources\n",
    "INITIAL_WATER = 2500\n",
    "INITIAL_SHELTER = 2000\n",
    "FOOD_REGENERATION = 250  # Further increased resource regeneration\n",
    "WATER_REGENERATION = 250\n",
    "SHELTER_REGENERATION = 150\n",
    "MEDICINE_REGENERATION = 100\n",
    "TOOLS_REGENERATION = 100\n",
    "\n",
    "CARRYING_CAPACITY = 500  # Increased carrying capacity\n",
    "\n",
    "# Environmental factors\n",
    "SEASONAL_EFFECT = 0.15\n",
    "CLIMATE_VARIABILITY = 0.05\n",
    "DISASTER_PROBABILITY = 0.015  # Further reduced probability of a catastrophic event\n",
    "LONG_TERM_CLIMATE_CHANGE = 0.001  # Gradual climate change effect\n",
    "CATASTROPHIC_EVENT_PROBABILITY = 0.005  # Rare probability of catastrophic events\n",
    "DISEASE_SPREAD_PROBABILITY = 0.05  # Reduced probability of disease outbreak\n",
    "CLIMATE_CYCLE_LENGTH = 50  # Length of climate cycles (e.g., ice ages)\n",
    "\n",
    "# Define the Individual class with full complexity\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, technology_level, environmental_adaptation, risk_taking, tradition, religion, leadership_style, emotional_intelligence, mental_health, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.technology_level = technology_level\n",
    "        self.environmental_adaptation = environmental_adaptation\n",
    "        self.risk_taking = risk_taking\n",
    "        self.tradition = tradition\n",
    "        self.religion = religion\n",
    "        self.leadership_style = leadership_style\n",
    "        self.emotional_intelligence = emotional_intelligence\n",
    "        self.mental_health = mental_health\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "        self.group = None  # Track group membership\n",
    "        self.language = random.uniform(0, 1)  # Language diversity\n",
    "        self.immune_system = random.uniform(0.6, 1)  # Increased base immunity\n",
    "        self.symbiotic_relationship = False  # Whether the individual has a symbiotic relationship with another species\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        if self.age > 40:\n",
    "            self.health *= 0.98  # Slower health decline with age\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        return 18 <= self.age <= 40\n",
    "\n",
    "    def mutate(self):\n",
    "        # Complex mutations with potential for adaptation and technology improvement\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.03, 0.03)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.03, 0.03)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on birth rate\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.03, 0.03)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on health\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.03, 0.03)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.03, 0.03)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.03, 0.03)))\n",
    "        self.technology_level = max(0, min(10, self.technology_level + random.uniform(-0.01, 0.02)))\n",
    "        self.environmental_adaptation = max(0, min(1, self.environmental_adaptation + random.uniform(-0.02, 0.02)))\n",
    "        self.risk_taking = max(0, min(1, self.risk_taking + random.uniform(-0.03, 0.03)))\n",
    "        self.tradition = max(0, min(1, self.tradition + random.uniform(-0.03, 0.03)))\n",
    "        self.religion = max(0, min(1, self.religion + random.uniform(-0.03, 0.03)))\n",
    "        self.emotional_intelligence = max(0, min(1, self.emotional_intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.mental_health = max(0, min(1, self.mental_health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on mental health\n",
    "        self.immune_system = max(0.6, min(1, self.immune_system + random.uniform(-0.01, 0.01)))  # Further increased base immunity\n",
    "\n",
    "# Define the Group class to manage groups of individuals\n",
    "class Group:\n",
    "    def __init__(self, leader):\n",
    "        self.leader = leader\n",
    "        self.members = [leader]\n",
    "        self.resources = {\"food\": 0, \"water\": 0, \"shelter\": 0, \"medicine\": 0, \"tools\": 0, \"currency\": 0}\n",
    "        self.territory = random.uniform(0.5, 1)  # Increase base territory richness\n",
    "        self.cultural_practices = random.uniform(0.5, 1)  # More shared cultural practices\n",
    "        self.language = leader.language  # Shared language within the group\n",
    "        self.trade_networks = 0  # Track trade relationships with other groups\n",
    "        self.governance_structure = leader.leadership_style  # Initial governance style\n",
    "        self.laws = {}  # Simple legal system\n",
    "        self.education_level = leader.intelligence * 0.5  # Education system influenced by leader's intelligence\n",
    "\n",
    "    def add_member(self, individual):\n",
    "        individual.group = self\n",
    "        self.members.append(individual)\n",
    "\n",
    "    def manage_resources(self):\n",
    "        # Allocate resources among members\n",
    "        for resource in self.resources:\n",
    "            if self.resources[resource] > 0:\n",
    "                allocation = self.resources[resource] / len(self.members)\n",
    "                for member in self.members:\n",
    "                    if resource == \"food\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"water\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"shelter\":\n",
    "                        member.health += allocation * 0.05\n",
    "                    elif resource == \"medicine\":\n",
    "                        member.health += allocation * 0.2\n",
    "                    elif resource == \"tools\":\n",
    "                        member.technology_level += allocation * 0.1\n",
    "                    elif resource == \"currency\":\n",
    "                        member.wealth += allocation * 0.1\n",
    "\n",
    "    def trade_with_group(self, other_group):\n",
    "        # Trade resources between groups based on their trade networks and resources\n",
    "        if self.trade_networks > 0 and other_group.trade_networks > 0:\n",
    "            trade_amount = min(self.resources[\"tools\"], other_group.resources[\"medicine\"])\n",
    "            self.resources[\"tools\"] -= trade_amount\n",
    "            other_group.resources[\"medicine\"] += trade_amount\n",
    "            trade_amount = min(self.resources[\"currency\"], other_group.resources[\"food\"])\n",
    "            self.resources[\"currency\"] -= trade_amount\n",
    "            other_group.resources[\"food\"] += trade_amount\n",
    "\n",
    "    def compete_for_resources(self, other_group):\n",
    "        # Complex competition logic including technology and leadership influence\n",
    "        if (self.territory + self.leader.intelligence + self.leader.technology_level + random.uniform(-0.1, 0.1)) > \\\n",
    "           (other_group.territory + other_group.leader.intelligence + other_group.leader.technology_level):\n",
    "            self.resources[\"food\"] += other_group.resources[\"food\"] * 0.5\n",
    "            other_group.resources[\"food\"] *= 0.5\n",
    "            self.resources[\"water\"] += other_group.resources[\"water\"] * 0.5\n",
    "            other_group.resources[\"water\"] *= 0.5\n",
    "            self.resources[\"shelter\"] += other_group.resources[\"shelter\"] * 0.5\n",
    "            other_group.resources[\"shelter\"] *= 0.5\n",
    "\n",
    "    def enforce_laws(self):\n",
    "        # Enforce laws to maintain order within the group\n",
    "        if self.governance_structure in ['autocratic', 'democratic']:\n",
    "            for law, enforcement in self.laws.items():\n",
    "                for member in self.members:\n",
    "                    if random.random() > enforcement:\n",
    "                        member.mental_health -= 0.05  # Punish lawbreakers with less severity\n",
    "\n",
    "# Define the Population class with full complexity\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.groups = []\n",
    "        self.food = INITIAL_FOOD\n",
    "        self.water = INITIAL_WATER\n",
    "        self.shelter = INITIAL_SHELTER\n",
    "        self.medicine = MEDICINE_REGENERATION\n",
    "        self.tools = TOOLS_REGENERATION\n",
    "        self.currency = 1000  # Initial currency for economic systems\n",
    "        self.season = \"Spring\"\n",
    "        self.technology_progress = 0\n",
    "        self.climate_trend = 0  # Track long-term climate trends\n",
    "        self.climate_cycle = 0  # Track the climate cycle\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0.5, 1),  # Increased base birth rate\n",
    "            intelligence=random.uniform(0.5, 1),  # Increased base intelligence\n",
    "            social_cohesion=random.uniform(0.5, 1),  # Increased base social cohesion\n",
    "            health=random.uniform(0.5, 1),  # Increased base health\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0.5, 1),  # Increased base cooperation\n",
    "            curiosity=random.uniform(0, 1),\n",
    "            technology_level=random.uniform(0.5, 1),  # Increased base technology level\n",
    "            environmental_adaptation=random.uniform(0.5, 1),  # Increased base environmental adaptation\n",
    "            risk_taking=random.uniform(0, 1),\n",
    "            tradition=random.uniform(0, 1),\n",
    "            religion=random.uniform(0, 1),\n",
    "            leadership_style=random.choice(['autocratic', 'democratic', 'tribal']),\n",
    "            emotional_intelligence=random.uniform(0.5, 1),  # Increased base emotional intelligence\n",
    "            mental_health=random.uniform(0.5, 1)  # Increased base mental health\n",
    "        )\n",
    "    \n",
    "    def form_group(self, leader):\n",
    "        group = Group(leader)\n",
    "        self.groups.append(group)\n",
    "        return group\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Environmental effects: seasonal, climate variability, and long-term trends\n",
    "        self.update_season()\n",
    "        seasonal_factor = self.get_seasonal_factor()\n",
    "        climate_factor = 1 + random.uniform(-CLIMATE_VARIABILITY, CLIMATE_VARIABILITY) + self.climate_trend\n",
    "        self.food *= seasonal_factor * climate_factor\n",
    "        self.water *= seasonal_factor * climate_factor\n",
    "        self.shelter *= seasonal_factor * climate_factor\n",
    "        self.medicine *= seasonal_factor * climate_factor\n",
    "        self.tools *= seasonal_factor * climate_factor\n",
    "        self.currency *= seasonal_factor * climate_factor\n",
    "\n",
    "        # Long-term climate change\n",
    "        self.climate_trend += LONG_TERM_CLIMATE_CHANGE * random.choice([-1, 1])\n",
    "\n",
    "        # Climate cycles (e.g., ice ages)\n",
    "        self.climate_cycle += 1\n",
    "        if self.climate_cycle >= CLIMATE_CYCLE_LENGTH:\n",
    "            self.climate_cycle = 0\n",
    "            self.climate_trend *= -1  # Reverse the climate trend\n",
    "\n",
    "        # Catastrophic events\n",
    "        if random.random() < CATASTROPHIC_EVENT_PROBABILITY:\n",
    "            catastrophic_impact = random.uniform(0.3, 0.7)  # Reduced impact of catastrophic events\n",
    "            self.food -= self.food * catastrophic_impact\n",
    "            self.water -= self.water * catastrophic_impact\n",
    "            self.shelter -= self.shelter * catastrophic_impact\n",
    "            self.medicine -= self.medicine * catastrophic_impact\n",
    "            self.tools -= self.tools * catastrophic_impact\n",
    "            self.currency -= self.currency * catastrophic_impact\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > catastrophic_impact]\n",
    "\n",
    "        # Disease outbreak\n",
    "        if random.random() < DISEASE_SPREAD_PROBABILITY:\n",
    "            disease_severity = random.uniform(0.1, 0.3)  # Reduced severity of diseases\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > disease_severity * (1 - ind.immune_system)]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum((ind.health * 1.5 + ind.technology_level * 0.1) for ind in self.individuals)\n",
    "        total_food_demand = total_resource_demand * 0.3\n",
    "        total_water_demand = total_resource_demand * 0.3\n",
    "        total_shelter_demand = total_resource_demand * 0.2\n",
    "        total_medicine_demand = total_resource_demand * 0.1\n",
    "        total_tools_demand = total_resource_demand * 0.1\n",
    "        total_currency_demand = total_resource_demand * 0.05  # Demand for currency in trade\n",
    "\n",
    "        # Check resource limits\n",
    "        if (total_food_demand > self.food or total_water_demand > self.water or total_shelter_demand > self.shelter or \n",
    "            total_medicine_demand > self.medicine or total_tools_demand > self.tools or total_currency_demand > self.currency):\n",
    "            survival_chance = min(self.food / total_food_demand, self.water / total_water_demand, self.shelter / total_shelter_demand,\n",
    "                                  self.medicine / total_medicine_demand, self.tools / total_tools_demand, self.currency / total_currency_demand)\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Social dynamics: Group formation, migration, trade, competition, and governance\n",
    "        for ind in self.individuals:\n",
    "            if ind.group is None:\n",
    "                leader = ind if ind.intelligence > random.uniform(0, 1) else random.choice(self.individuals)\n",
    "                group = self.form_group(leader)\n",
    "                group.add_member(ind)\n",
    "            elif random.random() < ind.curiosity * 0.05:\n",
    "                # Migration to a new group\n",
    "                new_group = self.form_group(ind)\n",
    "                new_group.add_member(ind)\n",
    "        \n",
    "        # Group management\n",
    "        for group in self.groups:\n",
    "            group.manage_resources()\n",
    "            group.enforce_laws()\n",
    "\n",
    "            if random.random() < 0.1:  # Random chance of competition\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.compete_for_resources(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of trade\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.trade_with_group(other_group)\n",
    "\n",
    "        # Technology development and adaptation\n",
    "        for ind in self.individuals:\n",
    "            if random.random() < ind.intelligence * 0.05:\n",
    "                ind.technology_level += 0.1\n",
    "            if random.random() < ind.cooperation * 0.1:\n",
    "                self.technology_progress += 0.1\n",
    "\n",
    "            # Environmental adaptation\n",
    "            if self.climate_trend > 0:\n",
    "                ind.health *= (1 + ind.environmental_adaptation * 0.1)\n",
    "            elif self.climate_trend < 0:\n",
    "                ind.health *= (1 - ind.environmental_adaptation * 0.1)\n",
    "        \n",
    "        # Birth events influenced by technology, medicine, adaptation, and cultural traits\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.food / (CARRYING_CAPACITY * 2)) * (1 + ind.technology_level * 0.05)\n",
    "                if random.random() < birth_probability:\n",
    "                    child = self.create_random_individual()\n",
    "                    child.health = (ind.health + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.intelligence = (ind.intelligence + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.language = ind.language  # Inherit language\n",
    "                    child.immune_system = (ind.immune_system + random.uniform(-0.05, 0.05)) / 2  # Inherit immune system\n",
    "                    new_births.append(child)\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Resource regeneration\n",
    "        self.food = min(INITIAL_FOOD, self.food + FOOD_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.water = min(INITIAL_WATER, self.water + WATER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.shelter = min(INITIAL_SHELTER, self.shelter + SHELTER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.medicine = min(MEDICINE_REGENERATION, self.medicine + MEDICINE_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.tools = min(TOOLS_REGENERATION, self.tools + TOOLS_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.currency = min(1000, self.currency + 50 * seasonal_factor * climate_factor)\n",
    "\n",
    "    def update_season(self):\n",
    "        if self.season == \"Spring\":\n",
    "            self.season = \"Summer\"\n",
    "        elif self.season == \"Summer\":\n",
    "            self.season = \"Autumn\"\n",
    "        elif self.season == \"Autumn\":\n",
    "            self.season = \"Winter\"\n",
    "        elif self.season == \"Winter\":\n",
    "            self.season = \"Spring\"\n",
    "    \n",
    "    def get_seasonal_factor(self):\n",
    "        if self.season == \"Spring\":\n",
    "            return 1.1\n",
    "        elif self.season == \"Summer\":\n",
    "            return 1.0\n",
    "        elif self.season == \"Autumn\":\n",
    "            return 0.85\n",
    "        elif self.season == \"Winter\":\n",
    "            return 0.7\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        technology_levels = []\n",
    "        climate_trends = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "            technology_levels.append(self.technology_progress / len(self.individuals) if len(self.individuals) > 0 else 0)\n",
    "            climate_trends.append(self.climate_trend)\n",
    "        \n",
    "        return population_sizes, technology_levels, climate_trends\n",
    "\n",
    "# Run the comprehensive simulation with even more advanced features\n",
    "population = Population(100)\n",
    "population_sizes, technology_levels, climate_trends = population.simulate_generations(100)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf225c54",
   "metadata": {},
   "source": [
    "## Even More Complex Population Simulator with Global Politics and Diplomacy, Advanced Technological Progress, Space Exploration and Colonization, Environmental Impact and Sustainability,  Cultural and Religious Movements, Social Media and Communication, Human Rights and Ethics, Cyber Warfare and Espionage, Multi-Species Ecosystems and Genetic Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f123f4fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Constants for resources and environment\n",
    "INITIAL_FOOD = 3000  # Further increased initial resources\n",
    "INITIAL_WATER = 3000\n",
    "INITIAL_SHELTER = 2500\n",
    "FOOD_REGENERATION = 300  # Further increased resource regeneration\n",
    "WATER_REGENERATION = 300\n",
    "SHELTER_REGENERATION = 200\n",
    "MEDICINE_REGENERATION = 150\n",
    "TOOLS_REGENERATION = 150\n",
    "CURRENCY_REGENERATION = 100\n",
    "\n",
    "CARRYING_CAPACITY = 600  # Increased carrying capacity\n",
    "\n",
    "# Environmental factors\n",
    "SEASONAL_EFFECT = 0.15\n",
    "CLIMATE_VARIABILITY = 0.05\n",
    "DISASTER_PROBABILITY = 0.01  # Further reduced probability of a catastrophic event\n",
    "LONG_TERM_CLIMATE_CHANGE = 0.001  # Gradual climate change effect\n",
    "CATASTROPHIC_EVENT_PROBABILITY = 0.005  # Rare probability of catastrophic events\n",
    "DISEASE_SPREAD_PROBABILITY = 0.04  # Reduced probability of disease outbreak\n",
    "CLIMATE_CYCLE_LENGTH = 50  # Length of climate cycles (e.g., ice ages)\n",
    "\n",
    "# Define the Individual class with full complexity\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, technology_level, environmental_adaptation, risk_taking, tradition, religion, leadership_style, emotional_intelligence, mental_health, ethics, communication, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.technology_level = technology_level\n",
    "        self.environmental_adaptation = environmental_adaptation\n",
    "        self.risk_taking = risk_taking\n",
    "        self.tradition = tradition\n",
    "        self.religion = religion\n",
    "        self.leadership_style = leadership_style\n",
    "        self.emotional_intelligence = emotional_intelligence\n",
    "        self.mental_health = mental_health\n",
    "        self.ethics = ethics\n",
    "        self.communication = communication\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "        self.group = None  # Track group membership\n",
    "        self.language = random.uniform(0, 1)  # Language diversity\n",
    "        self.immune_system = random.uniform(0.6, 1)  # Increased base immunity\n",
    "        self.symbiotic_relationship = False  # Whether the individual has a symbiotic relationship with another species\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        if self.age > 40:\n",
    "            self.health *= 0.98  # Slower health decline with age\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        return 18 <= self.age <= 40\n",
    "\n",
    "    def mutate(self):\n",
    "        # Complex mutations with potential for adaptation and technology improvement\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.03, 0.03)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.03, 0.03)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on birth rate\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.03, 0.03)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on health\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.03, 0.03)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.03, 0.03)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.03, 0.03)))\n",
    "        self.technology_level = max(0, min(10, self.technology_level + random.uniform(-0.01, 0.02)))\n",
    "        self.environmental_adaptation = max(0, min(1, self.environmental_adaptation + random.uniform(-0.02, 0.02)))\n",
    "        self.risk_taking = max(0, min(1, self.risk_taking + random.uniform(-0.03, 0.03)))\n",
    "        self.tradition = max(0, min(1, self.tradition + random.uniform(-0.03, 0.03)))\n",
    "        self.religion = max(0, min(1, self.religion + random.uniform(-0.03, 0.03)))\n",
    "        self.emotional_intelligence = max(0, min(1, self.emotional_intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.mental_health = max(0, min(1, self.mental_health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on mental health\n",
    "        self.ethics = max(0, min(1, self.ethics + random.uniform(-0.03, 0.03)))\n",
    "        self.communication = max(0, min(1, self.communication + random.uniform(-0.03, 0.03)))\n",
    "        self.immune_system = max(0.6, min(1, self.immune_system + random.uniform(-0.01, 0.01)))  # Further increased base immunity\n",
    "\n",
    "# Define the Group class to manage groups of individuals\n",
    "class Group:\n",
    "    def __init__(self, leader):\n",
    "        self.leader = leader\n",
    "        self.members = [leader]\n",
    "        self.resources = {\"food\": 0, \"water\": 0, \"shelter\": 0, \"medicine\": 0, \"tools\": 0, \"currency\": 0}\n",
    "        self.territory = random.uniform(0.5, 1)  # Increase base territory richness\n",
    "        self.cultural_practices = random.uniform(0.5, 1)  # More shared cultural practices\n",
    "        self.language = leader.language  # Shared language within the group\n",
    "        self.trade_networks = 0  # Track trade relationships with other groups\n",
    "        self.governance_structure = leader.leadership_style  # Initial governance style\n",
    "        self.laws = {}  # Simple legal system\n",
    "        self.education_level = leader.intelligence * 0.5  # Education system influenced by leader's intelligence\n",
    "        self.ethical_standards = leader.ethics * 0.5  # Group's ethical standards influenced by leader's ethics\n",
    "        self.space_technology = 0  # Track space exploration capability\n",
    "\n",
    "    def add_member(self, individual):\n",
    "        individual.group = self\n",
    "        self.members.append(individual)\n",
    "\n",
    "    def manage_resources(self):\n",
    "        # Allocate resources among members\n",
    "        for resource in self.resources:\n",
    "            if self.resources[resource] > 0:\n",
    "                allocation = self.resources[resource] / len(self.members)\n",
    "                for member in self.members:\n",
    "                    if resource == \"food\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"water\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"shelter\":\n",
    "                        member.health += allocation * 0.05\n",
    "                    elif resource == \"medicine\":\n",
    "                        member.health += allocation * 0.2\n",
    "                    elif resource == \"tools\":\n",
    "                        member.technology_level += allocation * 0.1\n",
    "                    elif resource == \"currency\":\n",
    "                        member.wealth += allocation * 0.1\n",
    "\n",
    "    def trade_with_group(self, other_group):\n",
    "        # Trade resources between groups based on their trade networks and resources\n",
    "        if self.trade_networks > 0 and other_group.trade_networks > 0:\n",
    "            trade_amount = min(self.resources[\"tools\"], other_group.resources[\"medicine\"])\n",
    "            self.resources[\"tools\"] -= trade_amount\n",
    "            other_group.resources[\"medicine\"] += trade_amount\n",
    "            trade_amount = min(self.resources[\"currency\"], other_group.resources[\"food\"])\n",
    "            self.resources[\"currency\"] -= trade_amount\n",
    "            other_group.resources[\"food\"] += trade_amount\n",
    "\n",
    "    def compete_for_resources(self, other_group):\n",
    "        # Complex competition logic including technology and leadership influence\n",
    "        if (self.territory + self.leader.intelligence + self.leader.technology_level + random.uniform(-0.1, 0.1)) > \\\n",
    "           (other_group.territory + other_group.leader.intelligence + other_group.leader.technology_level):\n",
    "            self.resources[\"food\"] += other_group.resources[\"food\"] * 0.5\n",
    "            other_group.resources[\"food\"] *= 0.5\n",
    "            self.resources[\"water\"] += other_group.resources[\"water\"] * 0.5\n",
    "            other_group.resources[\"water\"] *= 0.5\n",
    "            self.resources[\"shelter\"] += other_group.resources[\"shelter\"] * 0.5\n",
    "            other_group.resources[\"shelter\"] *= 0.5\n",
    "\n",
    "    def enforce_laws(self):\n",
    "        # Enforce laws to maintain order within the group\n",
    "        if self.governance_structure in ['autocratic', 'democratic']:\n",
    "            for law, enforcement in self.laws.items():\n",
    "                for member in self.members:\n",
    "                    if random.random() > enforcement:\n",
    "                        member.mental_health -= 0.05  # Punish lawbreakers with less severity\n",
    "\n",
    "    def develop_space_technology(self):\n",
    "        if self.leader.technology_level > 8 and random.random() < 0.01:\n",
    "            self.space_technology += 1\n",
    "\n",
    "    def engage_in_diplomacy(self, other_group):\n",
    "        if random.random() < self.ethical_standards * 0.1:\n",
    "            # Form alliances or trade agreements\n",
    "            self.trade_networks += 1\n",
    "            other_group.trade_networks += 1\n",
    "\n",
    "    def engage_in_warfare(self, other_group):\n",
    "        if random.random() < self.leader.aggression * 0.1:\n",
    "            # Initiate war with another group\n",
    "            self.compete_for_resources(other_group)\n",
    "\n",
    "# Define the Population class with full complexity\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.groups = []\n",
    "        self.food = INITIAL_FOOD\n",
    "        self.water = INITIAL_WATER\n",
    "        self.shelter = INITIAL_SHELTER\n",
    "        self.medicine = MEDICINE_REGENERATION\n",
    "        self.tools = TOOLS_REGENERATION\n",
    "        self.currency = 1000  # Initial currency for economic systems\n",
    "        self.season = \"Spring\"\n",
    "        self.technology_progress = 0\n",
    "        self.climate_trend = 0  # Track long-term climate trends\n",
    "        self.climate_cycle = 0  # Track the climate cycle\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0.5, 1),  # Increased base birth rate\n",
    "            intelligence=random.uniform(0.5, 1),  # Increased base intelligence\n",
    "            social_cohesion=random.uniform(0.5, 1),  # Increased base social cohesion\n",
    "            health=random.uniform(0.5, 1),  # Increased base health\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0.5, 1),  # Increased base cooperation\n",
    "            curiosity=random.uniform(0, 1),\n",
    "            technology_level=random.uniform(0.5, 1),  # Increased base technology level\n",
    "            environmental_adaptation=random.uniform(0.5, 1),  # Increased base environmental adaptation\n",
    "            risk_taking=random.uniform(0, 1),\n",
    "            tradition=random.uniform(0, 1),\n",
    "            religion=random.uniform(0, 1),\n",
    "            leadership_style=random.choice(['autocratic', 'democratic', 'tribal']),\n",
    "            emotional_intelligence=random.uniform(0.5, 1),  # Increased base emotional intelligence\n",
    "            mental_health=random.uniform(0.5, 1),  # Increased base mental health\n",
    "            ethics=random.uniform(0.5, 1),  # Added ethics trait\n",
    "            communication=random.uniform(0.5, 1)  # Added communication trait\n",
    "        )\n",
    "    \n",
    "    def form_group(self, leader):\n",
    "        group = Group(leader)\n",
    "        self.groups.append(group)\n",
    "        return group\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Environmental effects: seasonal, climate variability, and long-term trends\n",
    "        self.update_season()\n",
    "        seasonal_factor = self.get_seasonal_factor()\n",
    "        climate_factor = 1 + random.uniform(-CLIMATE_VARIABILITY, CLIMATE_VARIABILITY) + self.climate_trend\n",
    "        self.food *= seasonal_factor * climate_factor\n",
    "        self.water *= seasonal_factor * climate_factor\n",
    "        self.shelter *= seasonal_factor * climate_factor\n",
    "        self.medicine *= seasonal_factor * climate_factor\n",
    "        self.tools *= seasonal_factor * climate_factor\n",
    "        self.currency *= seasonal_factor * climate_factor\n",
    "\n",
    "        # Long-term climate change\n",
    "        self.climate_trend += LONG_TERM_CLIMATE_CHANGE * random.choice([-1, 1])\n",
    "\n",
    "        # Climate cycles (e.g., ice ages)\n",
    "        self.climate_cycle += 1\n",
    "        if self.climate_cycle >= CLIMATE_CYCLE_LENGTH:\n",
    "            self.climate_cycle = 0\n",
    "            self.climate_trend *= -1  # Reverse the climate trend\n",
    "\n",
    "        # Catastrophic events\n",
    "        if random.random() < CATASTROPHIC_EVENT_PROBABILITY:\n",
    "            catastrophic_impact = random.uniform(0.3, 0.7)  # Reduced impact of catastrophic events\n",
    "            self.food -= self.food * catastrophic_impact\n",
    "            self.water -= self.water * catastrophic_impact\n",
    "            self.shelter -= self.shelter * catastrophic_impact\n",
    "            self.medicine -= self.medicine * catastrophic_impact\n",
    "            self.tools -= self.tools * catastrophic_impact\n",
    "            self.currency -= self.currency * catastrophic_impact\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > catastrophic_impact]\n",
    "\n",
    "        # Disease outbreak\n",
    "        if random.random() < DISEASE_SPREAD_PROBABILITY:\n",
    "            disease_severity = random.uniform(0.1, 0.3)  # Reduced severity of diseases\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > disease_severity * (1 - ind.immune_system)]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum((ind.health * 1.5 + ind.technology_level * 0.1) for ind in self.individuals)\n",
    "        total_food_demand = total_resource_demand * 0.3\n",
    "        total_water_demand = total_resource_demand * 0.3\n",
    "        total_shelter_demand = total_resource_demand * 0.2\n",
    "        total_medicine_demand = total_resource_demand * 0.1\n",
    "        total_tools_demand = total_resource_demand * 0.1\n",
    "        total_currency_demand = total_resource_demand * 0.05  # Demand for currency in trade\n",
    "\n",
    "        # Check resource limits\n",
    "        if (total_food_demand > self.food or total_water_demand > self.water or total_shelter_demand > self.shelter or \n",
    "            total_medicine_demand > self.medicine or total_tools_demand > self.tools or total_currency_demand > self.currency):\n",
    "            survival_chance = min(self.food / total_food_demand, self.water / total_water_demand, self.shelter / total_shelter_demand,\n",
    "                                  self.medicine / total_medicine_demand, self.tools / total_tools_demand, self.currency / total_currency_demand)\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Social dynamics: Group formation, migration, trade, competition, and governance\n",
    "        for ind in self.individuals:\n",
    "            if ind.group is None:\n",
    "                leader = ind if ind.intelligence > random.uniform(0, 1) else random.choice(self.individuals)\n",
    "                group = self.form_group(leader)\n",
    "                group.add_member(ind)\n",
    "            elif random.random() < ind.curiosity * 0.05:\n",
    "                # Migration to a new group\n",
    "                new_group = self.form_group(ind)\n",
    "                new_group.add_member(ind)\n",
    "        \n",
    "        # Group management\n",
    "        for group in self.groups:\n",
    "            group.manage_resources()\n",
    "            group.enforce_laws()\n",
    "            group.develop_space_technology()\n",
    "\n",
    "            if random.random() < 0.1:  # Random chance of competition\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.compete_for_resources(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of trade\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.trade_with_group(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of diplomacy\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_diplomacy(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_warfare(other_group)\n",
    "\n",
    "        # Technology development and adaptation\n",
    "        for ind in self.individuals:\n",
    "            if random.random() < ind.intelligence * 0.05:\n",
    "                ind.technology_level += 0.1\n",
    "            if random.random() < ind.cooperation * 0.1:\n",
    "                self.technology_progress += 0.1\n",
    "\n",
    "            # Environmental adaptation\n",
    "            if self.climate_trend > 0:\n",
    "                ind.health *= (1 + ind.environmental_adaptation * 0.1)\n",
    "            elif self.climate_trend < 0:\n",
    "                ind.health *= (1 - ind.environmental_adaptation * 0.1)\n",
    "        \n",
    "        # Birth events influenced by technology, medicine, adaptation, and cultural traits\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.food / (CARRYING_CAPACITY * 2)) * (1 + ind.technology_level * 0.05)\n",
    "                if random.random() < birth_probability:\n",
    "                    child = self.create_random_individual()\n",
    "                    child.health = (ind.health + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.intelligence = (ind.intelligence + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.language = ind.language  # Inherit language\n",
    "                    child.immune_system = (ind.immune_system + random.uniform(-0.05, 0.05)) / 2  # Inherit immune system\n",
    "                    new_births.append(child)\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Resource regeneration\n",
    "        self.food = min(INITIAL_FOOD, self.food + FOOD_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.water = min(INITIAL_WATER, self.water + WATER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.shelter = min(INITIAL_SHELTER, self.shelter + SHELTER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.medicine = min(MEDICINE_REGENERATION, self.medicine + MEDICINE_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.tools = min(TOOLS_REGENERATION, self.tools + TOOLS_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.currency = min(1000, self.currency + CURRENCY_REGENERATION * seasonal_factor * climate_factor)\n",
    "\n",
    "    def update_season(self):\n",
    "        if self.season == \"Spring\":\n",
    "            self.season = \"Summer\"\n",
    "        elif self.season == \"Summer\":\n",
    "            self.season = \"Autumn\"\n",
    "        elif self.season == \"Autumn\":\n",
    "            self.season = \"Winter\"\n",
    "        elif self.season == \"Winter\":\n",
    "            self.season = \"Spring\"\n",
    "    \n",
    "    def get_seasonal_factor(self):\n",
    "        if self.season == \"Spring\":\n",
    "            return 1.1\n",
    "        elif self.season == \"Summer\":\n",
    "            return 1.0\n",
    "        elif self.season == \"Autumn\":\n",
    "            return 0.85\n",
    "        elif self.season == \"Winter\":\n",
    "            return 0.7\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        technology_levels = []\n",
    "        climate_trends = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "            technology_levels.append(self.technology_progress / len(self.individuals) if len(self.individuals) > 0 else 0)\n",
    "            climate_trends.append(self.climate_trend)\n",
    "        \n",
    "        return population_sizes, technology_levels, climate_trends\n",
    "\n",
    "# Run the comprehensive simulation with even more advanced features\n",
    "population = Population(100)\n",
    "population_sizes, technology_levels, climate_trends = population.simulate_generations(100)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec107de1",
   "metadata": {},
   "source": [
    "## Almost as Complex as it Can Get Population Simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37de8664",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Constants for resources and environment\n",
    "INITIAL_FOOD = 3500  # Further increased initial resources\n",
    "INITIAL_WATER = 3500\n",
    "INITIAL_SHELTER = 3000\n",
    "FOOD_REGENERATION = 350  # Further increased resource regeneration\n",
    "WATER_REGENERATION = 350\n",
    "SHELTER_REGENERATION = 250\n",
    "MEDICINE_REGENERATION = 200\n",
    "TOOLS_REGENERATION = 200\n",
    "CURRENCY_REGENERATION = 150\n",
    "\n",
    "CARRYING_CAPACITY = 700  # Increased carrying capacity\n",
    "\n",
    "# Environmental factors\n",
    "SEASONAL_EFFECT = 0.15\n",
    "CLIMATE_VARIABILITY = 0.05\n",
    "DISASTER_PROBABILITY = 0.01  # Further reduced probability of a catastrophic event\n",
    "LONG_TERM_CLIMATE_CHANGE = 0.001  # Gradual climate change effect\n",
    "CATASTROPHIC_EVENT_PROBABILITY = 0.005  # Rare probability of catastrophic events\n",
    "DISEASE_SPREAD_PROBABILITY = 0.03  # Reduced probability of disease outbreak\n",
    "CLIMATE_CYCLE_LENGTH = 50  # Length of climate cycles (e.g., ice ages)\n",
    "NUCLEAR_WAR_PROBABILITY = 0.002  # Probability of nuclear war\n",
    "AI_GOVERNANCE_THRESHOLD = 8  # Technology level at which AI governance becomes possible\n",
    "\n",
    "# Define the Individual class with full complexity\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, technology_level, environmental_adaptation, risk_taking, tradition, religion, leadership_style, emotional_intelligence, mental_health, ethics, communication, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.technology_level = technology_level\n",
    "        self.environmental_adaptation = environmental_adaptation\n",
    "        self.risk_taking = risk_taking\n",
    "        self.tradition = tradition\n",
    "        self.religion = religion\n",
    "        self.leadership_style = leadership_style\n",
    "        self.emotional_intelligence = emotional_intelligence\n",
    "        self.mental_health = mental_health\n",
    "        self.ethics = ethics\n",
    "        self.communication = communication\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "        self.group = None  # Track group membership\n",
    "        self.language = random.uniform(0, 1)  # Language diversity\n",
    "        self.immune_system = random.uniform(0.7, 1)  # Further increased base immunity\n",
    "        self.symbiotic_relationship = False  # Whether the individual has a symbiotic relationship with another species\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        if self.age > 40:\n",
    "            self.health *= 0.98  # Slower health decline with age\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        return 18 <= self.age <= 40\n",
    "\n",
    "    def mutate(self):\n",
    "        # Complex mutations with potential for adaptation and technology improvement\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.03, 0.03)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.03, 0.03)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on birth rate\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.03, 0.03)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on health\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.03, 0.03)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.03, 0.03)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.03, 0.03)))\n",
    "        self.technology_level = max(0, min(10, self.technology_level + random.uniform(-0.01, 0.02)))\n",
    "        self.environmental_adaptation = max(0, min(1, self.environmental_adaptation + random.uniform(-0.02, 0.02)))\n",
    "        self.risk_taking = max(0, min(1, self.risk_taking + random.uniform(-0.03, 0.03)))\n",
    "        self.tradition = max(0, min(1, self.tradition + random.uniform(-0.03, 0.03)))\n",
    "        self.religion = max(0, min(1, self.religion + random.uniform(-0.03, 0.03)))\n",
    "        self.emotional_intelligence = max(0, min(1, self.emotional_intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.mental_health = max(0, min(1, self.mental_health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on mental health\n",
    "        self.ethics = max(0, min(1, self.ethics + random.uniform(-0.03, 0.03)))\n",
    "        self.communication = max(0, min(1, self.communication + random.uniform(-0.03, 0.03)))\n",
    "        self.immune_system = max(0.7, min(1, self.immune_system + random.uniform(-0.01, 0.01)))  # Further increased base immunity\n",
    "\n",
    "# Define the Group class to manage groups of individuals\n",
    "class Group:\n",
    "    def __init__(self, leader):\n",
    "        self.leader = leader\n",
    "        self.members = [leader]\n",
    "        self.resources = {\"food\": 0, \"water\": 0, \"shelter\": 0, \"medicine\": 0, \"tools\": 0, \"currency\": 0}\n",
    "        self.territory = random.uniform(0.5, 1)  # Increase base territory richness\n",
    "        self.cultural_practices = random.uniform(0.5, 1)  # More shared cultural practices\n",
    "        self.language = leader.language  # Shared language within the group\n",
    "        self.trade_networks = 0  # Track trade relationships with other groups\n",
    "        self.governance_structure = leader.leadership_style  # Initial governance style\n",
    "        self.laws = {}  # Simple legal system\n",
    "        self.education_level = leader.intelligence * 0.5  # Education system influenced by leader's intelligence\n",
    "        self.ethical_standards = leader.ethics * 0.5  # Group's ethical standards influenced by leader's ethics\n",
    "        self.space_technology = 0  # Track space exploration capability\n",
    "        self.automation_level = 0  # Level of automation in the group\n",
    "\n",
    "    def add_member(self, individual):\n",
    "        individual.group = self\n",
    "        self.members.append(individual)\n",
    "\n",
    "    def manage_resources(self):\n",
    "        # Allocate resources among members\n",
    "        for resource in self.resources:\n",
    "            if self.resources[resource] > 0:\n",
    "                allocation = self.resources[resource] / len(self.members)\n",
    "                for member in self.members:\n",
    "                    if resource == \"food\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"water\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"shelter\":\n",
    "                        member.health += allocation * 0.05\n",
    "                    elif resource == \"medicine\":\n",
    "                        member.health += allocation * 0.2\n",
    "                    elif resource == \"tools\":\n",
    "                        member.technology_level += allocation * 0.1\n",
    "                    elif resource == \"currency\":\n",
    "                        member.wealth += allocation * 0.1\n",
    "\n",
    "    def trade_with_group(self, other_group):\n",
    "        # Trade resources between groups based on their trade networks and resources\n",
    "        if self.trade_networks > 0 and other_group.trade_networks > 0:\n",
    "            trade_amount = min(self.resources[\"tools\"], other_group.resources[\"medicine\"])\n",
    "            self.resources[\"tools\"] -= trade_amount\n",
    "            other_group.resources[\"medicine\"] += trade_amount\n",
    "            trade_amount = min(self.resources[\"currency\"], other_group.resources[\"food\"])\n",
    "            self.resources[\"currency\"] -= trade_amount\n",
    "            other_group.resources[\"food\"] += trade_amount\n",
    "\n",
    "    def compete_for_resources(self, other_group):\n",
    "        # Complex competition logic including technology and leadership influence\n",
    "        if (self.territory + self.leader.intelligence + self.leader.technology_level + random.uniform(-0.1, 0.1)) > \\\n",
    "           (other_group.territory + other_group.leader.intelligence + other_group.leader.technology_level):\n",
    "            self.resources[\"food\"] += other_group.resources[\"food\"] * 0.5\n",
    "            other_group.resources[\"food\"] *= 0.5\n",
    "            self.resources[\"water\"] += other_group.resources[\"water\"] * 0.5\n",
    "            other_group.resources[\"water\"] *= 0.5\n",
    "            self.resources[\"shelter\"] += other_group.resources[\"shelter\"] * 0.5\n",
    "            other_group.resources[\"shelter\"] *= 0.5\n",
    "\n",
    "    def enforce_laws(self):\n",
    "        # Enforce laws to maintain order within the group\n",
    "        if self.governance_structure in ['autocratic', 'democratic']:\n",
    "            for law, enforcement in self.laws.items():\n",
    "                for member in self.members:\n",
    "                    if random.random() > enforcement:\n",
    "                        member.mental_health -= 0.05  # Punish lawbreakers with less severity\n",
    "\n",
    "    def develop_space_technology(self):\n",
    "        if self.leader.technology_level > 8 and random.random() < 0.01:\n",
    "            self.space_technology += 1\n",
    "\n",
    "    def increase_automation(self):\n",
    "        if self.leader.technology_level > 7 and random.random() < 0.02:\n",
    "            self.automation_level += 1\n",
    "\n",
    "    def engage_in_diplomacy(self, other_group):\n",
    "        if random.random() < self.ethical_standards * 0.1:\n",
    "            # Form alliances or trade agreements\n",
    "            self.trade_networks += 1\n",
    "            other_group.trade_networks += 1\n",
    "\n",
    "    def engage_in_warfare(self, other_group):\n",
    "        if random.random() < self.leader.aggression * 0.1:\n",
    "            # Initiate war with another group\n",
    "            self.compete_for_resources(other_group)\n",
    "\n",
    "    def deploy_nuclear_weapons(self, other_group):\n",
    "        if random.random() < NUCLEAR_WAR_PROBABILITY:\n",
    "            # Engage in nuclear warfare\n",
    "            destruction_factor = random.uniform(0.3, 0.7)\n",
    "            self.resources[\"food\"] -= self.resources[\"food\"] * destruction_factor\n",
    "            other_group.resources[\"food\"] -= other_group.resources[\"food\"] * destruction_factor\n",
    "            self.resources[\"water\"] -= self.resources[\"water\"] * destruction_factor\n",
    "            other_group.resources[\"water\"] -= other_group.resources[\"water\"] * destruction_factor\n",
    "            self.resources[\"shelter\"] -= self.resources[\"shelter\"] * destruction_factor\n",
    "            other_group.resources[\"shelter\"] -= other_group.resources[\"shelter\"] * destruction_factor\n",
    "\n",
    "    def establish_ai_governance(self):\n",
    "        if self.leader.technology_level >= AI_GOVERNANCE_THRESHOLD:\n",
    "            self.governance_structure = 'AI-driven'\n",
    "\n",
    "# Define the Population class with full complexity\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.groups = []\n",
    "        self.food = INITIAL_FOOD\n",
    "        self.water = INITIAL_WATER\n",
    "        self.shelter = INITIAL_SHELTER\n",
    "        self.medicine = MEDICINE_REGENERATION\n",
    "        self.tools = TOOLS_REGENERATION\n",
    "        self.currency = 1500  # Initial currency for economic systems\n",
    "        self.season = \"Spring\"\n",
    "        self.technology_progress = 0\n",
    "        self.climate_trend = 0  # Track long-term climate trends\n",
    "        self.climate_cycle = 0  # Track the climate cycle\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0.5, 1),  # Increased base birth rate\n",
    "            intelligence=random.uniform(0.5, 1),  # Increased base intelligence\n",
    "            social_cohesion=random.uniform(0.5, 1),  # Increased base social cohesion\n",
    "            health=random.uniform(0.5, 1),  # Increased base health\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0.5, 1),  # Increased base cooperation\n",
    "            curiosity=random.uniform(0, 1),\n",
    "            technology_level=random.uniform(0.5, 1),  # Increased base technology level\n",
    "            environmental_adaptation=random.uniform(0.5, 1),  # Increased base environmental adaptation\n",
    "            risk_taking=random.uniform(0, 1),\n",
    "            tradition=random.uniform(0, 1),\n",
    "            religion=random.uniform(0, 1),\n",
    "            leadership_style=random.choice(['autocratic', 'democratic', 'tribal']),\n",
    "            emotional_intelligence=random.uniform(0.5, 1),  # Increased base emotional intelligence\n",
    "            mental_health=random.uniform(0.5, 1),  # Increased base mental health\n",
    "            ethics=random.uniform(0.5, 1),  # Added ethics trait\n",
    "            communication=random.uniform(0.5, 1)  # Added communication trait\n",
    "        )\n",
    "    \n",
    "    def form_group(self, leader):\n",
    "        group = Group(leader)\n",
    "        self.groups.append(group)\n",
    "        return group\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Environmental effects: seasonal, climate variability, and long-term trends\n",
    "        self.update_season()\n",
    "        seasonal_factor = self.get_seasonal_factor()\n",
    "        climate_factor = 1 + random.uniform(-CLIMATE_VARIABILITY, CLIMATE_VARIABILITY) + self.climate_trend\n",
    "        self.food *= seasonal_factor * climate_factor\n",
    "        self.water *= seasonal_factor * climate_factor\n",
    "        self.shelter *= seasonal_factor * climate_factor\n",
    "        self.medicine *= seasonal_factor * climate_factor\n",
    "        self.tools *= seasonal_factor * climate_factor\n",
    "        self.currency *= seasonal_factor * climate_factor\n",
    "\n",
    "        # Long-term climate change\n",
    "        self.climate_trend += LONG_TERM_CLIMATE_CHANGE * random.choice([-1, 1])\n",
    "\n",
    "        # Climate cycles (e.g., ice ages)\n",
    "        self.climate_cycle += 1\n",
    "        if self.climate_cycle >= CLIMATE_CYCLE_LENGTH:\n",
    "            self.climate_cycle = 0\n",
    "            self.climate_trend *= -1  # Reverse the climate trend\n",
    "\n",
    "        # Catastrophic events\n",
    "        if random.random() < CATASTROPHIC_EVENT_PROBABILITY:\n",
    "            catastrophic_impact = random.uniform(0.3, 0.7)  # Reduced impact of catastrophic events\n",
    "            self.food -= self.food * catastrophic_impact\n",
    "            self.water -= self.water * catastrophic_impact\n",
    "            self.shelter -= self.shelter * catastrophic_impact\n",
    "            self.medicine -= self.medicine * catastrophic_impact\n",
    "            self.tools -= self.tools * catastrophic_impact\n",
    "            self.currency -= self.currency * catastrophic_impact\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > catastrophic_impact]\n",
    "\n",
    "        # Disease outbreak\n",
    "        if random.random() < DISEASE_SPREAD_PROBABILITY:\n",
    "            disease_severity = random.uniform(0.1, 0.3)  # Reduced severity of diseases\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > disease_severity * (1 - ind.immune_system)]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum((ind.health * 1.5 + ind.technology_level * 0.1) for ind in self.individuals)\n",
    "        total_food_demand = total_resource_demand * 0.3\n",
    "        total_water_demand = total_resource_demand * 0.3\n",
    "        total_shelter_demand = total_resource_demand * 0.2\n",
    "        total_medicine_demand = total_resource_demand * 0.1\n",
    "        total_tools_demand = total_resource_demand * 0.1\n",
    "        total_currency_demand = total_resource_demand * 0.05  # Demand for currency in trade\n",
    "\n",
    "        # Check resource limits\n",
    "        if (total_food_demand > self.food or total_water_demand > self.water or total_shelter_demand > self.shelter or \n",
    "            total_medicine_demand > self.medicine or total_tools_demand > self.tools or total_currency_demand > self.currency):\n",
    "            survival_chance = min(self.food / total_food_demand, self.water / total_water_demand, self.shelter / total_shelter_demand,\n",
    "                                  self.medicine / total_medicine_demand, self.tools / total_tools_demand, self.currency / total_currency_demand)\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Social dynamics: Group formation, migration, trade, competition, and governance\n",
    "        for ind in self.individuals:\n",
    "            if ind.group is None:\n",
    "                leader = ind if ind.intelligence > random.uniform(0, 1) else random.choice(self.individuals)\n",
    "                group = self.form_group(leader)\n",
    "                group.add_member(ind)\n",
    "            elif random.random() < ind.curiosity * 0.05:\n",
    "                # Migration to a new group\n",
    "                new_group = self.form_group(ind)\n",
    "                new_group.add_member(ind)\n",
    "        \n",
    "        # Group management\n",
    "        for group in self.groups:\n",
    "            group.manage_resources()\n",
    "            group.enforce_laws()\n",
    "            group.develop_space_technology()\n",
    "            group.increase_automation()\n",
    "            group.establish_ai_governance()\n",
    "\n",
    "            if random.random() < 0.1:  # Random chance of competition\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.compete_for_resources(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of trade\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.trade_with_group(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of diplomacy\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_diplomacy(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_warfare(other_group)\n",
    "\n",
    "            if random.random() < NUCLEAR_WAR_PROBABILITY:  # Random chance of nuclear warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.deploy_nuclear_weapons(other_group)\n",
    "\n",
    "        # Technology development and adaptation\n",
    "        for ind in self.individuals:\n",
    "            if random.random() < ind.intelligence * 0.05:\n",
    "                ind.technology_level += 0.1\n",
    "            if random.random() < ind.cooperation * 0.1:\n",
    "                self.technology_progress += 0.1\n",
    "\n",
    "            # Environmental adaptation\n",
    "            if self.climate_trend > 0:\n",
    "                ind.health *= (1 + ind.environmental_adaptation * 0.1)\n",
    "            elif self.climate_trend < 0:\n",
    "                ind.health *= (1 - ind.environmental_adaptation * 0.1)\n",
    "        \n",
    "        # Birth events influenced by technology, medicine, adaptation, and cultural traits\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.food / (CARRYING_CAPACITY * 2)) * (1 + ind.technology_level * 0.05)\n",
    "                if random.random() < birth_probability:\n",
    "                    child = self.create_random_individual()\n",
    "                    child.health = (ind.health + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.intelligence = (ind.intelligence + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.language = ind.language  # Inherit language\n",
    "                    child.immune_system = (ind.immune_system + random.uniform(-0.05, 0.05)) / 2  # Inherit immune system\n",
    "                    new_births.append(child)\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Resource regeneration\n",
    "        self.food = min(INITIAL_FOOD, self.food + FOOD_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.water = min(INITIAL_WATER, self.water + WATER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.shelter = min(INITIAL_SHELTER, self.shelter + SHELTER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.medicine = min(MEDICINE_REGENERATION, self.medicine + MEDICINE_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.tools = min(TOOLS_REGENERATION, self.tools + TOOLS_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.currency = min(1500, self.currency + CURRENCY_REGENERATION * seasonal_factor * climate_factor)\n",
    "\n",
    "    def update_season(self):\n",
    "        if self.season == \"Spring\":\n",
    "            self.season = \"Summer\"\n",
    "        elif self.season == \"Summer\":\n",
    "            self.season = \"Autumn\"\n",
    "        elif self.season == \"Autumn\":\n",
    "            self.season = \"Winter\"\n",
    "        elif self.season == \"Winter\":\n",
    "            self.season = \"Spring\"\n",
    "    \n",
    "    def get_seasonal_factor(self):\n",
    "        if self.season == \"Spring\":\n",
    "            return 1.1\n",
    "        elif self.season == \"Summer\":\n",
    "            return 1.0\n",
    "        elif self.season == \"Autumn\":\n",
    "            return 0.85\n",
    "        elif self.season == \"Winter\":\n",
    "            return 0.7\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        technology_levels = []\n",
    "        climate_trends = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "            technology_levels.append(self.technology_progress / len(self.individuals) if len(self.individuals) > 0 else 0)\n",
    "            climate_trends.append(self.climate_trend)\n",
    "        \n",
    "        return population_sizes, technology_levels, climate_trends\n",
    "\n",
    "# Run the comprehensive simulation with even more advanced features\n",
    "population = Population(100)\n",
    "population_sizes, technology_levels, climate_trends = population.simulate_generations(100)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c4e227d",
   "metadata": {},
   "source": [
    "## Futuristic Population Simulator - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03e724ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Constants for resources and environment\n",
    "INITIAL_FOOD = 4000  # Further increased initial resources\n",
    "INITIAL_WATER = 4000\n",
    "INITIAL_SHELTER = 3500\n",
    "FOOD_REGENERATION = 400  # Further increased resource regeneration\n",
    "WATER_REGENERATION = 400\n",
    "SHELTER_REGENERATION = 300\n",
    "MEDICINE_REGENERATION = 250\n",
    "TOOLS_REGENERATION = 250\n",
    "CURRENCY_REGENERATION = 200\n",
    "\n",
    "CARRYING_CAPACITY = 800  # Increased carrying capacity\n",
    "\n",
    "# Environmental factors\n",
    "SEASONAL_EFFECT = 0.15\n",
    "CLIMATE_VARIABILITY = 0.05\n",
    "DISASTER_PROBABILITY = 0.01  # Further reduced probability of a catastrophic event\n",
    "LONG_TERM_CLIMATE_CHANGE = 0.001  # Gradual climate change effect\n",
    "CATASTROPHIC_EVENT_PROBABILITY = 0.005  # Rare probability of catastrophic events\n",
    "DISEASE_SPREAD_PROBABILITY = 0.02  # Further reduced probability of disease outbreak\n",
    "CLIMATE_CYCLE_LENGTH = 50  # Length of climate cycles (e.g., ice ages)\n",
    "NUCLEAR_WAR_PROBABILITY = 0.002  # Probability of nuclear war\n",
    "AI_GOVERNANCE_THRESHOLD = 8  # Technology level at which AI governance becomes possible\n",
    "\n",
    "# Define the Individual class with full complexity\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, technology_level, environmental_adaptation, risk_taking, tradition, religion, leadership_style, emotional_intelligence, mental_health, ethics, communication, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.technology_level = technology_level\n",
    "        self.environmental_adaptation = environmental_adaptation\n",
    "        self.risk_taking = risk_taking\n",
    "        self.tradition = tradition\n",
    "        self.religion = religion\n",
    "        self.leadership_style = leadership_style\n",
    "        self.emotional_intelligence = emotional_intelligence\n",
    "        self.mental_health = mental_health\n",
    "        self.ethics = ethics\n",
    "        self.communication = communication\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "        self.group = None  # Track group membership\n",
    "        self.language = random.uniform(0, 1)  # Language diversity\n",
    "        self.immune_system = random.uniform(0.7, 1)  # Further increased base immunity\n",
    "        self.symbiotic_relationship = False  # Whether the individual has a symbiotic relationship with another species\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        if self.age > 40:\n",
    "            self.health *= 0.98  # Slower health decline with age\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        return 18 <= self.age <= 40\n",
    "\n",
    "    def mutate(self):\n",
    "        # Complex mutations with potential for adaptation and technology improvement\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.03, 0.03)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.03, 0.03)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on birth rate\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.03, 0.03)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on health\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.03, 0.03)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.03, 0.03)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.03, 0.03)))\n",
    "        self.technology_level = max(0, min(10, self.technology_level + random.uniform(-0.01, 0.02)))\n",
    "        self.environmental_adaptation = max(0, min(1, self.environmental_adaptation + random.uniform(-0.02, 0.02)))\n",
    "        self.risk_taking = max(0, min(1, self.risk_taking + random.uniform(-0.03, 0.03)))\n",
    "        self.tradition = max(0, min(1, self.tradition + random.uniform(-0.03, 0.03)))\n",
    "        self.religion = max(0, min(1, self.religion + random.uniform(-0.03, 0.03)))\n",
    "        self.emotional_intelligence = max(0, min(1, self.emotional_intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.mental_health = max(0, min(1, self.mental_health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on mental health\n",
    "        self.ethics = max(0, min(1, self.ethics + random.uniform(-0.03, 0.03)))\n",
    "        self.communication = max(0, min(1, self.communication + random.uniform(-0.03, 0.03)))\n",
    "        self.immune_system = max(0.7, min(1, self.immune_system + random.uniform(-0.01, 0.01)))  # Further increased base immunity\n",
    "\n",
    "# Define the Group class to manage groups of individuals\n",
    "class Group:\n",
    "    def __init__(self, leader):\n",
    "        self.leader = leader\n",
    "        self.members = [leader]\n",
    "        self.resources = {\"food\": 0, \"water\": 0, \"shelter\": 0, \"medicine\": 0, \"tools\": 0, \"currency\": 0}\n",
    "        self.territory = random.uniform(0.5, 1)  # Increase base territory richness\n",
    "        self.cultural_practices = random.uniform(0.5, 1)  # More shared cultural practices\n",
    "        self.language = leader.language  # Shared language within the group\n",
    "        self.trade_networks = 0  # Track trade relationships with other groups\n",
    "        self.governance_structure = leader.leadership_style  # Initial governance style\n",
    "        self.laws = {}  # Simple legal system\n",
    "        self.education_level = leader.intelligence * 0.5  # Education system influenced by leader's intelligence\n",
    "        self.ethical_standards = leader.ethics * 0.5  # Group's ethical standards influenced by leader's ethics\n",
    "        self.space_technology = 0  # Track space exploration capability\n",
    "        self.automation_level = 0  # Level of automation in the group\n",
    "        self.ai_influence = 0  # Influence of AI in governance and decision-making\n",
    "        self.corporate_influence = 0  # Influence of corporations on the group\n",
    "        self.quantum_technology = 0  # Track quantum technology development\n",
    "\n",
    "    def add_member(self, individual):\n",
    "        individual.group = self\n",
    "        self.members.append(individual)\n",
    "\n",
    "    def manage_resources(self):\n",
    "        # Allocate resources among members\n",
    "        for resource in self.resources:\n",
    "            if self.resources[resource] > 0:\n",
    "                allocation = self.resources[resource] / len(self.members)\n",
    "                for member in self.members:\n",
    "                    if resource == \"food\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"water\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"shelter\":\n",
    "                        member.health += allocation * 0.05\n",
    "                    elif resource == \"medicine\":\n",
    "                        member.health += allocation * 0.2\n",
    "                    elif resource == \"tools\":\n",
    "                        member.technology_level += allocation * 0.1\n",
    "                    elif resource == \"currency\":\n",
    "                        member.wealth += allocation * 0.1\n",
    "\n",
    "    def trade_with_group(self, other_group):\n",
    "        # Trade resources between groups based on their trade networks and resources\n",
    "        if self.trade_networks > 0 and other_group.trade_networks > 0:\n",
    "            trade_amount = min(self.resources[\"tools\"], other_group.resources[\"medicine\"])\n",
    "            self.resources[\"tools\"] -= trade_amount\n",
    "            other_group.resources[\"medicine\"] += trade_amount\n",
    "            trade_amount = min(self.resources[\"currency\"], other_group.resources[\"food\"])\n",
    "            self.resources[\"currency\"] -= trade_amount\n",
    "            other_group.resources[\"food\"] += trade_amount\n",
    "\n",
    "    def compete_for_resources(self, other_group):\n",
    "        # Complex competition logic including technology and leadership influence\n",
    "        if (self.territory + self.leader.intelligence + self.leader.technology_level + random.uniform(-0.1, 0.1)) > \\\n",
    "           (other_group.territory + other_group.leader.intelligence + other_group.leader.technology_level):\n",
    "            self.resources[\"food\"] += other_group.resources[\"food\"] * 0.5\n",
    "            other_group.resources[\"food\"] *= 0.5\n",
    "            self.resources[\"water\"] += other_group.resources[\"water\"] * 0.5\n",
    "            other_group.resources[\"water\"] *= 0.5\n",
    "            self.resources[\"shelter\"] += other_group.resources[\"shelter\"] * 0.5\n",
    "            other_group.resources[\"shelter\"] *= 0.5\n",
    "\n",
    "    def enforce_laws(self):\n",
    "        # Enforce laws to maintain order within the group\n",
    "        if self.governance_structure in ['autocratic', 'democratic']:\n",
    "            for law, enforcement in self.laws.items():\n",
    "                for member in self.members:\n",
    "                    if random.random() > enforcement:\n",
    "                        member.mental_health -= 0.05  # Punish lawbreakers with less severity\n",
    "\n",
    "    def develop_space_technology(self):\n",
    "        if self.leader.technology_level > 8 and random.random() < 0.01:\n",
    "            self.space_technology += 1\n",
    "\n",
    "    def increase_automation(self):\n",
    "        if self.leader.technology_level > 7 and random.random() < 0.02:\n",
    "            self.automation_level += 1\n",
    "\n",
    "    def engage_in_diplomacy(self, other_group):\n",
    "        if random.random() < self.ethical_standards * 0.1:\n",
    "            # Form alliances or trade agreements\n",
    "            self.trade_networks += 1\n",
    "            other_group.trade_networks += 1\n",
    "\n",
    "    def engage_in_warfare(self, other_group):\n",
    "        if random.random() < self.leader.aggression * 0.1:\n",
    "            # Initiate war with another group\n",
    "            self.compete_for_resources(other_group)\n",
    "\n",
    "    def deploy_nuclear_weapons(self, other_group):\n",
    "        if random.random() < NUCLEAR_WAR_PROBABILITY:\n",
    "            # Engage in nuclear warfare\n",
    "            destruction_factor = random.uniform(0.3, 0.7)\n",
    "            self.resources[\"food\"] -= self.resources[\"food\"] * destruction_factor\n",
    "            other_group.resources[\"food\"] -= other_group.resources[\"food\"] * destruction_factor\n",
    "            self.resources[\"water\"] -= self.resources[\"water\"] * destruction_factor\n",
    "            other_group.resources[\"water\"] -= other_group.resources[\"water\"] * destruction_factor\n",
    "            self.resources[\"shelter\"] -= self.resources[\"shelter\"] * destruction_factor\n",
    "            other_group.resources[\"shelter\"] -= other_group.resources[\"shelter\"] * destruction_factor\n",
    "\n",
    "    def establish_ai_governance(self):\n",
    "        if self.leader.technology_level >= AI_GOVERNANCE_THRESHOLD:\n",
    "            self.governance_structure = 'AI-driven'\n",
    "            self.ai_influence += 1\n",
    "\n",
    "    def develop_quantum_technology(self):\n",
    "        if self.leader.technology_level > 9 and random.random() < 0.02:\n",
    "            self.quantum_technology += 1\n",
    "\n",
    "    def corporate_takeover(self):\n",
    "        if random.random() < 0.05:\n",
    "            self.corporate_influence += 1\n",
    "            self.governance_structure = 'Corporate'\n",
    "\n",
    "# Define the Population class with full complexity\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.groups = []\n",
    "        self.food = INITIAL_FOOD\n",
    "        self.water = INITIAL_WATER\n",
    "        self.shelter = INITIAL_SHELTER\n",
    "        self.medicine = MEDICINE_REGENERATION\n",
    "        self.tools = TOOLS_REGENERATION\n",
    "        self.currency = 2000  # Initial currency for economic systems\n",
    "        self.season = \"Spring\"\n",
    "        self.technology_progress = 0\n",
    "        self.climate_trend = 0  # Track long-term climate trends\n",
    "        self.climate_cycle = 0  # Track the climate cycle\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0.5, 1),  # Increased base birth rate\n",
    "            intelligence=random.uniform(0.5, 1),  # Increased base intelligence\n",
    "            social_cohesion=random.uniform(0.5, 1),  # Increased base social cohesion\n",
    "            health=random.uniform(0.5, 1),  # Increased base health\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0.5, 1),  # Increased base cooperation\n",
    "            curiosity=random.uniform(0, 1),\n",
    "            technology_level=random.uniform(0.5, 1),  # Increased base technology level\n",
    "            environmental_adaptation=random.uniform(0.5, 1),  # Increased base environmental adaptation\n",
    "            risk_taking=random.uniform(0, 1),\n",
    "            tradition=random.uniform(0, 1),\n",
    "            religion=random.uniform(0, 1),\n",
    "            leadership_style=random.choice(['autocratic', 'democratic', 'tribal']),\n",
    "            emotional_intelligence=random.uniform(0.5, 1),  # Increased base emotional intelligence\n",
    "            mental_health=random.uniform(0.5, 1),  # Increased base mental health\n",
    "            ethics=random.uniform(0.5, 1),  # Added ethics trait\n",
    "            communication=random.uniform(0.5, 1)  # Added communication trait\n",
    "        )\n",
    "    \n",
    "    def form_group(self, leader):\n",
    "        group = Group(leader)\n",
    "        self.groups.append(group)\n",
    "        return group\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Environmental effects: seasonal, climate variability, and long-term trends\n",
    "        self.update_season()\n",
    "        seasonal_factor = self.get_seasonal_factor()\n",
    "        climate_factor = 1 + random.uniform(-CLIMATE_VARIABILITY, CLIMATE_VARIABILITY) + self.climate_trend\n",
    "        self.food *= seasonal_factor * climate_factor\n",
    "        self.water *= seasonal_factor * climate_factor\n",
    "        self.shelter *= seasonal_factor * climate_factor\n",
    "        self.medicine *= seasonal_factor * climate_factor\n",
    "        self.tools *= seasonal_factor * climate_factor\n",
    "        self.currency *= seasonal_factor * climate_factor\n",
    "\n",
    "        # Long-term climate change\n",
    "        self.climate_trend += LONG_TERM_CLIMATE_CHANGE * random.choice([-1, 1])\n",
    "\n",
    "        # Climate cycles (e.g., ice ages)\n",
    "        self.climate_cycle += 1\n",
    "        if self.climate_cycle >= CLIMATE_CYCLE_LENGTH:\n",
    "            self.climate_cycle = 0\n",
    "            self.climate_trend *= -1  # Reverse the climate trend\n",
    "\n",
    "        # Catastrophic events\n",
    "        if random.random() < CATASTROPHIC_EVENT_PROBABILITY:\n",
    "            catastrophic_impact = random.uniform(0.3, 0.7)  # Reduced impact of catastrophic events\n",
    "            self.food -= self.food * catastrophic_impact\n",
    "            self.water -= self.water * catastrophic_impact\n",
    "            self.shelter -= self.shelter * catastrophic_impact\n",
    "            self.medicine -= self.medicine * catastrophic_impact\n",
    "            self.tools -= self.tools * catastrophic_impact\n",
    "            self.currency -= self.currency * catastrophic_impact\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > catastrophic_impact]\n",
    "\n",
    "        # Disease outbreak\n",
    "        if random.random() < DISEASE_SPREAD_PROBABILITY:\n",
    "            disease_severity = random.uniform(0.1, 0.3)  # Reduced severity of diseases\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > disease_severity * (1 - ind.immune_system)]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum((ind.health * 1.5 + ind.technology_level * 0.1) for ind in self.individuals)\n",
    "        total_food_demand = total_resource_demand * 0.3\n",
    "        total_water_demand = total_resource_demand * 0.3\n",
    "        total_shelter_demand = total_resource_demand * 0.2\n",
    "        total_medicine_demand = total_resource_demand * 0.1\n",
    "        total_tools_demand = total_resource_demand * 0.1\n",
    "        total_currency_demand = total_resource_demand * 0.05  # Demand for currency in trade\n",
    "\n",
    "        # Check resource limits\n",
    "        if (total_food_demand > self.food or total_water_demand > self.water or total_shelter_demand > self.shelter or \n",
    "            total_medicine_demand > self.medicine or total_tools_demand > self.tools or total_currency_demand > self.currency):\n",
    "            survival_chance = min(self.food / total_food_demand, self.water / total_water_demand, self.shelter / total_shelter_demand,\n",
    "                                  self.medicine / total_medicine_demand, self.tools / total_tools_demand, self.currency / total_currency_demand)\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Social dynamics: Group formation, migration, trade, competition, and governance\n",
    "        for ind in self.individuals:\n",
    "            if ind.group is None:\n",
    "                leader = ind if ind.intelligence > random.uniform(0, 1) else random.choice(self.individuals)\n",
    "                group = self.form_group(leader)\n",
    "                group.add_member(ind)\n",
    "            elif random.random() < ind.curiosity * 0.05:\n",
    "                # Migration to a new group\n",
    "                new_group = self.form_group(ind)\n",
    "                new_group.add_member(ind)\n",
    "        \n",
    "        # Group management\n",
    "        for group in self.groups:\n",
    "            group.manage_resources()\n",
    "            group.enforce_laws()\n",
    "            group.develop_space_technology()\n",
    "            group.increase_automation()\n",
    "            group.establish_ai_governance()\n",
    "            group.develop_quantum_technology()\n",
    "            group.corporate_takeover()\n",
    "\n",
    "            if random.random() < 0.1:  # Random chance of competition\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.compete_for_resources(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of trade\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.trade_with_group(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of diplomacy\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_diplomacy(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_warfare(other_group)\n",
    "\n",
    "            if random.random() < NUCLEAR_WAR_PROBABILITY:  # Random chance of nuclear warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.deploy_nuclear_weapons(other_group)\n",
    "\n",
    "        # Technology development and adaptation\n",
    "        for ind in self.individuals:\n",
    "            if random.random() < ind.intelligence * 0.05:\n",
    "                ind.technology_level += 0.1\n",
    "            if random.random() < ind.cooperation * 0.1:\n",
    "                self.technology_progress += 0.1\n",
    "\n",
    "            # Environmental adaptation\n",
    "            if self.climate_trend > 0:\n",
    "                ind.health *= (1 + ind.environmental_adaptation * 0.1)\n",
    "            elif self.climate_trend < 0:\n",
    "                ind.health *= (1 - ind.environmental_adaptation * 0.1)\n",
    "        \n",
    "        # Birth events influenced by technology, medicine, adaptation, and cultural traits\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.food / (CARRYING_CAPACITY * 2)) * (1 + ind.technology_level * 0.05)\n",
    "                if random.random() < birth_probability:\n",
    "                    child = self.create_random_individual()\n",
    "                    child.health = (ind.health + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.intelligence = (ind.intelligence + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.language = ind.language  # Inherit language\n",
    "                    child.immune_system = (ind.immune_system + random.uniform(-0.05, 0.05)) / 2  # Inherit immune system\n",
    "                    new_births.append(child)\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Resource regeneration\n",
    "        self.food = min(INITIAL_FOOD, self.food + FOOD_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.water = min(INITIAL_WATER, self.water + WATER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.shelter = min(INITIAL_SHELTER, self.shelter + SHELTER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.medicine = min(MEDICINE_REGENERATION, self.medicine + MEDICINE_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.tools = min(TOOLS_REGENERATION, self.tools + TOOLS_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.currency = min(2000, self.currency + CURRENCY_REGENERATION * seasonal_factor * climate_factor)\n",
    "\n",
    "    def update_season(self):\n",
    "        if self.season == \"Spring\":\n",
    "            self.season = \"Summer\"\n",
    "        elif self.season == \"Summer\":\n",
    "            self.season = \"Autumn\"\n",
    "        elif self.season == \"Autumn\":\n",
    "            self.season = \"Winter\"\n",
    "        elif self.season == \"Winter\":\n",
    "            self.season = \"Spring\"\n",
    "    \n",
    "    def get_seasonal_factor(self):\n",
    "        if self.season == \"Spring\":\n",
    "            return 1.1\n",
    "        elif self.season == \"Summer\":\n",
    "            return 1.0\n",
    "        elif self.season == \"Autumn\":\n",
    "            return 0.85\n",
    "        elif self.season == \"Winter\":\n",
    "            return 0.7\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        technology_levels = []\n",
    "        climate_trends = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "            technology_levels.append(self.technology_progress / len(self.individuals) if len(self.individuals) > 0 else 0)\n",
    "            climate_trends.append(self.climate_trend)\n",
    "        \n",
    "        return population_sizes, technology_levels, climate_trends\n",
    "\n",
    "# Run the comprehensive simulation with even more advanced features\n",
    "population = Population(100)\n",
    "population_sizes, technology_levels, climate_trends = population.simulate_generations(100)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab82abe",
   "metadata": {},
   "source": [
    "## Extremely Complex Population Simulator with Multiverse and Parallel Realities, Evolutionary Bottlenecks and Genetic Drift, Cultural Evolution and Memetic Theory, Artificial Life and Synthetic Biology, Psychedelic Research and Consciousness Expansion, Emotional AI and Sentient Machines, Climate Refugees and Migration Dynamics, Post-Humanism and Transcendence, Galactic Empires and Interstellar Civilization, Time Travel and Temporal Manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b28f34b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Constants for resources and environment\n",
    "INITIAL_FOOD = 5000  # Further increased initial resources\n",
    "INITIAL_WATER = 5000\n",
    "INITIAL_SHELTER = 4500\n",
    "FOOD_REGENERATION = 500  # Further increased resource regeneration\n",
    "WATER_REGENERATION = 500\n",
    "SHELTER_REGENERATION = 400\n",
    "MEDICINE_REGENERATION = 300\n",
    "TOOLS_REGENERATION = 300\n",
    "CURRENCY_REGENERATION = 250\n",
    "\n",
    "CARRYING_CAPACITY = 1000  # Increased carrying capacity\n",
    "\n",
    "# Environmental factors\n",
    "SEASONAL_EFFECT = 0.15\n",
    "CLIMATE_VARIABILITY = 0.05\n",
    "DISASTER_PROBABILITY = 0.01  # Further reduced probability of a catastrophic event\n",
    "LONG_TERM_CLIMATE_CHANGE = 0.001  # Gradual climate change effect\n",
    "CATASTROPHIC_EVENT_PROBABILITY = 0.005  # Rare probability of catastrophic events\n",
    "DISEASE_SPREAD_PROBABILITY = 0.02  # Further reduced probability of disease outbreak\n",
    "CLIMATE_CYCLE_LENGTH = 50  # Length of climate cycles (e.g., ice ages)\n",
    "NUCLEAR_WAR_PROBABILITY = 0.002  # Probability of nuclear war\n",
    "AI_GOVERNANCE_THRESHOLD = 8  # Technology level at which AI governance becomes possible\n",
    "\n",
    "# Define the Individual class with ultra-complexity\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, technology_level, environmental_adaptation, risk_taking, tradition, religion, leadership_style, emotional_intelligence, mental_health, ethics, communication, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.technology_level = technology_level\n",
    "        self.environmental_adaptation = environmental_adaptation\n",
    "        self.risk_taking = risk_taking\n",
    "        self.tradition = tradition\n",
    "        self.religion = religion\n",
    "        self.leadership_style = leadership_style\n",
    "        self.emotional_intelligence = emotional_intelligence\n",
    "        self.mental_health = mental_health\n",
    "        self.ethics = ethics\n",
    "        self.communication = communication\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "        self.group = None  # Track group membership\n",
    "        self.language = random.uniform(0, 1)  # Language diversity\n",
    "        self.immune_system = random.uniform(0.7, 1)  # Further increased base immunity\n",
    "        self.symbiotic_relationship = False  # Whether the individual has a symbiotic relationship with another species\n",
    "        self.genetic_code = ''.join(random.choices('ATCG', k=1000))  # Simulate complex genetic sequences\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        if self.age > 40:\n",
    "            self.health *= 0.98  # Slower health decline with age\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        return 18 <= self.age <= 40\n",
    "\n",
    "    def mutate(self):\n",
    "        # Complex mutations with potential for adaptation and technology improvement\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.03, 0.03)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.03, 0.03)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on birth rate\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.03, 0.03)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on health\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.03, 0.03)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.03, 0.03)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.03, 0.03)))\n",
    "        self.technology_level = max(0, min(10, self.technology_level + random.uniform(-0.01, 0.02)))\n",
    "        self.environmental_adaptation = max(0, min(1, self.environmental_adaptation + random.uniform(-0.02, 0.02)))\n",
    "        self.risk_taking = max(0, min(1, self.risk_taking + random.uniform(-0.03, 0.03)))\n",
    "        self.tradition = max(0, min(1, self.tradition + random.uniform(-0.03, 0.03)))\n",
    "        self.religion = max(0, min(1, self.religion + random.uniform(-0.03, 0.03)))\n",
    "        self.emotional_intelligence = max(0, min(1, self.emotional_intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.mental_health = max(0, min(1, self.mental_health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on mental health\n",
    "        self.ethics = max(0, min(1, self.ethics + random.uniform(-0.03, 0.03)))\n",
    "        self.communication = max(0, min(1, self.communication + random.uniform(-0.03, 0.03)))\n",
    "        self.immune_system = max(0.7, min(1, self.immune_system + random.uniform(-0.01, 0.01)))  # Further increased base immunity\n",
    "        # Simulate minor genetic mutations\n",
    "        mutation_point = random.randint(0, len(self.genetic_code) - 1)\n",
    "        self.genetic_code = self.genetic_code[:mutation_point] + random.choice('ATCG') + self.genetic_code[mutation_point + 1:]\n",
    "\n",
    "# Define the Group class to manage groups of individuals\n",
    "class Group:\n",
    "    def __init__(self, leader):\n",
    "        self.leader = leader\n",
    "        self.members = [leader]\n",
    "        self.resources = {\"food\": 0, \"water\": 0, \"shelter\": 0, \"medicine\": 0, \"tools\": 0, \"currency\": 0}\n",
    "        self.territory = random.uniform(0.5, 1)  # Increase base territory richness\n",
    "        self.cultural_practices = random.uniform(0.5, 1)  # More shared cultural practices\n",
    "        self.language = leader.language  # Shared language within the group\n",
    "        self.trade_networks = 0  # Track trade relationships with other groups\n",
    "        self.governance_structure = leader.leadership_style  # Initial governance style\n",
    "        self.laws = {}  # Simple legal system\n",
    "        self.education_level = leader.intelligence * 0.5  # Education system influenced by leader's intelligence\n",
    "        self.ethical_standards = leader.ethics * 0.5  # Group's ethical standards influenced by leader's ethics\n",
    "        self.space_technology = 0  # Track space exploration capability\n",
    "        self.automation_level = 0  # Level of automation in the group\n",
    "        self.ai_influence = 0  # Influence of AI in governance and decision-making\n",
    "        self.corporate_influence = 0  # Influence of corporations on the group\n",
    "        self.quantum_technology = 0  # Track quantum technology development\n",
    "        self.memes = {}  # Track cultural memes\n",
    "        self.parallel_reality_links = 0  # Number of connections to parallel realities\n",
    "\n",
    "    def add_member(self, individual):\n",
    "        individual.group = self\n",
    "        self.members.append(individual)\n",
    "\n",
    "    def manage_resources(self):\n",
    "        # Allocate resources among members\n",
    "        for resource in self.resources:\n",
    "            if self.resources[resource] > 0:\n",
    "                allocation = self.resources[resource] / len(self.members)\n",
    "                for member in self.members:\n",
    "                    if resource == \"food\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"water\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"shelter\":\n",
    "                        member.health += allocation * 0.05\n",
    "                    elif resource == \"medicine\":\n",
    "                        member.health += allocation * 0.2\n",
    "                    elif resource == \"tools\":\n",
    "                        member.technology_level += allocation * 0.1\n",
    "                    elif resource == \"currency\":\n",
    "                        member.wealth += allocation * 0.1\n",
    "\n",
    "    def trade_with_group(self, other_group):\n",
    "        # Trade resources between groups based on their trade networks and resources\n",
    "        if self.trade_networks > 0 and other_group.trade_networks > 0:\n",
    "            trade_amount = min(self.resources[\"tools\"], other_group.resources[\"medicine\"])\n",
    "            self.resources[\"tools\"] -= trade_amount\n",
    "            other_group.resources[\"medicine\"] += trade_amount\n",
    "            trade_amount = min(self.resources[\"currency\"], other_group.resources[\"food\"])\n",
    "            self.resources[\"currency\"] -= trade_amount\n",
    "            other_group.resources[\"food\"] += trade_amount\n",
    "\n",
    "    def compete_for_resources(self, other_group):\n",
    "        # Complex competition logic including technology and leadership influence\n",
    "        if (self.territory + self.leader.intelligence + self.leader.technology_level + random.uniform(-0.1, 0.1)) > \\\n",
    "           (other_group.territory + other_group.leader.intelligence + other_group.leader.technology_level):\n",
    "            self.resources[\"food\"] += other_group.resources[\"food\"] * 0.5\n",
    "            other_group.resources[\"food\"] *= 0.5\n",
    "            self.resources[\"water\"] += other_group.resources[\"water\"] * 0.5\n",
    "            other_group.resources[\"water\"] *= 0.5\n",
    "            self.resources[\"shelter\"] += other_group.resources[\"shelter\"] * 0.5\n",
    "            other_group.resources[\"shelter\"] *= 0.5\n",
    "\n",
    "    def enforce_laws(self):\n",
    "        # Enforce laws to maintain order within the group\n",
    "        if self.governance_structure in ['autocratic', 'democratic']:\n",
    "            for law, enforcement in self.laws.items():\n",
    "                for member in self.members:\n",
    "                    if random.random() > enforcement:\n",
    "                        member.mental_health -= 0.05  # Punish lawbreakers with less severity\n",
    "\n",
    "    def develop_space_technology(self):\n",
    "        if self.leader.technology_level > 8 and random.random() < 0.01:\n",
    "            self.space_technology += 1\n",
    "\n",
    "    def increase_automation(self):\n",
    "        if self.leader.technology_level > 7 and random.random() < 0.02:\n",
    "            self.automation_level += 1\n",
    "\n",
    "    def engage_in_diplomacy(self, other_group):\n",
    "        if random.random() < self.ethical_standards * 0.1:\n",
    "            # Form alliances or trade agreements\n",
    "            self.trade_networks += 1\n",
    "            other_group.trade_networks += 1\n",
    "\n",
    "    def engage_in_warfare(self, other_group):\n",
    "        if random.random() < self.leader.aggression * 0.1:\n",
    "            # Initiate war with another group\n",
    "            self.compete_for_resources(other_group)\n",
    "\n",
    "    def deploy_nuclear_weapons(self, other_group):\n",
    "        if random.random() < NUCLEAR_WAR_PROBABILITY:\n",
    "            # Engage in nuclear warfare\n",
    "            destruction_factor = random.uniform(0.3, 0.7)\n",
    "            self.resources[\"food\"] -= self.resources[\"food\"] * destruction_factor\n",
    "            other_group.resources[\"food\"] -= other_group.resources[\"food\"] * destruction_factor\n",
    "            self.resources[\"water\"] -= self.resources[\"water\"] * destruction_factor\n",
    "            other_group.resources[\"water\"] -= other_group.resources[\"water\"] * destruction_factor\n",
    "            self.resources[\"shelter\"] -= self.resources[\"shelter\"] * destruction_factor\n",
    "            other_group.resources[\"shelter\"] -= other_group.resources[\"shelter\"] * destruction_factor\n",
    "\n",
    "    def establish_ai_governance(self):\n",
    "        if self.leader.technology_level >= AI_GOVERNANCE_THRESHOLD:\n",
    "            self.governance_structure = 'AI-driven'\n",
    "            self.ai_influence += 1\n",
    "\n",
    "    def develop_quantum_technology(self):\n",
    "        if self.leader.technology_level > 9 and random.random() < 0.02:\n",
    "            self.quantum_technology += 1\n",
    "\n",
    "    def corporate_takeover(self):\n",
    "        if random.random() < 0.05:\n",
    "            self.corporate_influence += 1\n",
    "            self.governance_structure = 'Corporate'\n",
    "\n",
    "    def spread_memes(self, meme):\n",
    "        # Spread a cultural meme within the group\n",
    "        if meme not in self.memes:\n",
    "            self.memes[meme] = 1\n",
    "        else:\n",
    "            self.memes[meme] += 1\n",
    "        for member in self.members:\n",
    "            if random.random() < 0.1:\n",
    "                member.tradition += 0.01  # Memes may influence cultural traits\n",
    "\n",
    "    def link_parallel_realities(self):\n",
    "        if random.random() < 0.01:\n",
    "            self.parallel_reality_links += 1\n",
    "\n",
    "# Define the Population class with ultra-complexity\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.groups = []\n",
    "        self.food = INITIAL_FOOD\n",
    "        self.water = INITIAL_WATER\n",
    "        self.shelter = INITIAL_SHELTER\n",
    "        self.medicine = MEDICINE_REGENERATION\n",
    "        self.tools = TOOLS_REGENERATION\n",
    "        self.currency = 2500  # Initial currency for economic systems\n",
    "        self.season = \"Spring\"\n",
    "        self.technology_progress = 0\n",
    "        self.climate_trend = 0  # Track long-term climate trends\n",
    "        self.climate_cycle = 0  # Track the climate cycle\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0.5, 1),  # Increased base birth rate\n",
    "            intelligence=random.uniform(0.5, 1),  # Increased base intelligence\n",
    "            social_cohesion=random.uniform(0.5, 1),  # Increased base social cohesion\n",
    "            health=random.uniform(0.5, 1),  # Increased base health\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0.5, 1),  # Increased base cooperation\n",
    "            curiosity=random.uniform(0, 1),\n",
    "            technology_level=random.uniform(0.5, 1),  # Increased base technology level\n",
    "            environmental_adaptation=random.uniform(0.5, 1),  # Increased base environmental adaptation\n",
    "            risk_taking=random.uniform(0, 1),\n",
    "            tradition=random.uniform(0, 1),\n",
    "            religion=random.uniform(0, 1),\n",
    "            leadership_style=random.choice(['autocratic', 'democratic', 'tribal']),\n",
    "            emotional_intelligence=random.uniform(0.5, 1),  # Increased base emotional intelligence\n",
    "            mental_health=random.uniform(0.5, 1),  # Increased base mental health\n",
    "            ethics=random.uniform(0.5, 1),  # Added ethics trait\n",
    "            communication=random.uniform(0.5, 1)  # Added communication trait\n",
    "        )\n",
    "    \n",
    "    def form_group(self, leader):\n",
    "        group = Group(leader)\n",
    "        self.groups.append(group)\n",
    "        return group\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Environmental effects: seasonal, climate variability, and long-term trends\n",
    "        self.update_season()\n",
    "        seasonal_factor = self.get_seasonal_factor()\n",
    "        climate_factor = 1 + random.uniform(-CLIMATE_VARIABILITY, CLIMATE_VARIABILITY) + self.climate_trend\n",
    "        self.food *= seasonal_factor * climate_factor\n",
    "        self.water *= seasonal_factor * climate_factor\n",
    "        self.shelter *= seasonal_factor * climate_factor\n",
    "        self.medicine *= seasonal_factor * climate_factor\n",
    "        self.tools *= seasonal_factor * climate_factor\n",
    "        self.currency *= seasonal_factor * climate_factor\n",
    "\n",
    "        # Long-term climate change\n",
    "        self.climate_trend += LONG_TERM_CLIMATE_CHANGE * random.choice([-1, 1])\n",
    "\n",
    "        # Climate cycles (e.g., ice ages)\n",
    "        self.climate_cycle += 1\n",
    "        if self.climate_cycle >= CLIMATE_CYCLE_LENGTH:\n",
    "            self.climate_cycle = 0\n",
    "            self.climate_trend *= -1  # Reverse the climate trend\n",
    "\n",
    "        # Catastrophic events\n",
    "        if random.random() < CATASTROPHIC_EVENT_PROBABILITY:\n",
    "            catastrophic_impact = random.uniform(0.3, 0.7)  # Reduced impact of catastrophic events\n",
    "            self.food -= self.food * catastrophic_impact\n",
    "            self.water -= self.water * catastrophic_impact\n",
    "            self.shelter -= self.shelter * catastrophic_impact\n",
    "            self.medicine -= self.medicine * catastrophic_impact\n",
    "            self.tools -= self.tools * catastrophic_impact\n",
    "            self.currency -= self.currency * catastrophic_impact\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > catastrophic_impact]\n",
    "\n",
    "        # Disease outbreak\n",
    "        if random.random() < DISEASE_SPREAD_PROBABILITY:\n",
    "            disease_severity = random.uniform(0.1, 0.3)  # Reduced severity of diseases\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > disease_severity * (1 - ind.immune_system)]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum((ind.health * 1.5 + ind.technology_level * 0.1) for ind in self.individuals)\n",
    "        total_food_demand = total_resource_demand * 0.3\n",
    "        total_water_demand = total_resource_demand * 0.3\n",
    "        total_shelter_demand = total_resource_demand * 0.2\n",
    "        total_medicine_demand = total_resource_demand * 0.1\n",
    "        total_tools_demand = total_resource_demand * 0.1\n",
    "        total_currency_demand = total_resource_demand * 0.05  # Demand for currency in trade\n",
    "\n",
    "        # Check resource limits\n",
    "        if (total_food_demand > self.food or total_water_demand > self.water or total_shelter_demand > self.shelter or \n",
    "            total_medicine_demand > self.medicine or total_tools_demand > self.tools or total_currency_demand > self.currency):\n",
    "            survival_chance = min(self.food / total_food_demand, self.water / total_water_demand, self.shelter / total_shelter_demand,\n",
    "                                  self.medicine / total_medicine_demand, self.tools / total_tools_demand, self.currency / total_currency_demand)\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Social dynamics: Group formation, migration, trade, competition, and governance\n",
    "        for ind in self.individuals:\n",
    "            if ind.group is None:\n",
    "                leader = ind if ind.intelligence > random.uniform(0, 1) else random.choice(self.individuals)\n",
    "                group = self.form_group(leader)\n",
    "                group.add_member(ind)\n",
    "            elif random.random() < ind.curiosity * 0.05:\n",
    "                # Migration to a new group\n",
    "                new_group = self.form_group(ind)\n",
    "                new_group.add_member(ind)\n",
    "        \n",
    "        # Group management\n",
    "        for group in self.groups:\n",
    "            group.manage_resources()\n",
    "            group.enforce_laws()\n",
    "            group.develop_space_technology()\n",
    "            group.increase_automation()\n",
    "            group.establish_ai_governance()\n",
    "            group.develop_quantum_technology()\n",
    "            group.corporate_takeover()\n",
    "            group.link_parallel_realities()\n",
    "\n",
    "            # Spread memes in the group\n",
    "            if random.random() < 0.1:\n",
    "                meme = f\"Meme-{random.randint(1, 100)}\"\n",
    "                group.spread_memes(meme)\n",
    "\n",
    "            if random.random() < 0.1:  # Random chance of competition\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.compete_for_resources(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of trade\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.trade_with_group(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of diplomacy\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_diplomacy(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_warfare(other_group)\n",
    "\n",
    "            if random.random() < NUCLEAR_WAR_PROBABILITY:  # Random chance of nuclear warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.deploy_nuclear_weapons(other_group)\n",
    "\n",
    "        # Technology development and adaptation\n",
    "        for ind in self.individuals:\n",
    "            if random.random() < ind.intelligence * 0.05:\n",
    "                ind.technology_level += 0.1\n",
    "            if random.random() < ind.cooperation * 0.1:\n",
    "                self.technology_progress += 0.1\n",
    "\n",
    "            # Environmental adaptation\n",
    "            if self.climate_trend > 0:\n",
    "                ind.health *= (1 + ind.environmental_adaptation * 0.1)\n",
    "            elif self.climate_trend < 0:\n",
    "                ind.health *= (1 - ind.environmental_adaptation * 0.1)\n",
    "        \n",
    "        # Birth events influenced by technology, medicine, adaptation, and cultural traits\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.food / (CARRYING_CAPACITY * 2)) * (1 + ind.technology_level * 0.05)\n",
    "                if random.random() < birth_probability:\n",
    "                    child = self.create_random_individual()\n",
    "                    child.health = (ind.health + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.intelligence = (ind.intelligence + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.language = ind.language  # Inherit language\n",
    "                    child.immune_system = (ind.immune_system + random.uniform(-0.05, 0.05)) / 2  # Inherit immune system\n",
    "                    new_births.append(child)\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Resource regeneration\n",
    "        self.food = min(INITIAL_FOOD, self.food + FOOD_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.water = min(INITIAL_WATER, self.water + WATER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.shelter = min(INITIAL_SHELTER, self.shelter + SHELTER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.medicine = min(MEDICINE_REGENERATION, self.medicine + MEDICINE_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.tools = min(TOOLS_REGENERATION, self.tools + TOOLS_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.currency = min(2500, self.currency + CURRENCY_REGENERATION * seasonal_factor * climate_factor)\n",
    "\n",
    "    def update_season(self):\n",
    "        if self.season == \"Spring\":\n",
    "            self.season = \"Summer\"\n",
    "        elif self.season == \"Summer\":\n",
    "            self.season = \"Autumn\"\n",
    "        elif self.season == \"Autumn\":\n",
    "            self.season = \"Winter\"\n",
    "        elif self.season == \"Winter\":\n",
    "            self.season = \"Spring\"\n",
    "    \n",
    "    def get_seasonal_factor(self):\n",
    "        if self.season == \"Spring\":\n",
    "            return 1.1\n",
    "        elif self.season == \"Summer\":\n",
    "            return 1.0\n",
    "        elif self.season == \"Autumn\":\n",
    "            return 0.85\n",
    "        elif self.season == \"Winter\":\n",
    "            return 0.7\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        technology_levels = []\n",
    "        climate_trends = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "            technology_levels.append(self.technology_progress / len(self.individuals) if len(self.individuals) > 0 else 0)\n",
    "            climate_trends.append(self.climate_trend)\n",
    "        \n",
    "        return population_sizes, technology_levels, climate_trends\n",
    "\n",
    "# Run the ultra-complex simulation with even more advanced features\n",
    "population = Population(100)\n",
    "population_sizes, technology_levels, climate_trends = population.simulate_generations(100)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b42c594d",
   "metadata": {},
   "source": [
    "## Insane Futuristic Population Simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ba97736",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Constants for resources and environment\n",
    "INITIAL_FOOD = 6000  # Further increased initial resources\n",
    "INITIAL_WATER = 6000\n",
    "INITIAL_SHELTER = 5500\n",
    "FOOD_REGENERATION = 600  # Further increased resource regeneration\n",
    "WATER_REGENERATION = 600\n",
    "SHELTER_REGENERATION = 500\n",
    "MEDICINE_REGENERATION = 400\n",
    "TOOLS_REGENERATION = 400\n",
    "CURRENCY_REGENERATION = 300\n",
    "\n",
    "CARRYING_CAPACITY = 1200  # Increased carrying capacity\n",
    "\n",
    "# Environmental factors\n",
    "SEASONAL_EFFECT = 0.15\n",
    "CLIMATE_VARIABILITY = 0.05\n",
    "DISASTER_PROBABILITY = 0.01  # Further reduced probability of a catastrophic event\n",
    "LONG_TERM_CLIMATE_CHANGE = 0.001  # Gradual climate change effect\n",
    "CATASTROPHIC_EVENT_PROBABILITY = 0.005  # Rare probability of catastrophic events\n",
    "DISEASE_SPREAD_PROBABILITY = 0.02  # Further reduced probability of disease outbreak\n",
    "CLIMATE_CYCLE_LENGTH = 50  # Length of climate cycles (e.g., ice ages)\n",
    "NUCLEAR_WAR_PROBABILITY = 0.002  # Probability of nuclear war\n",
    "AI_GOVERNANCE_THRESHOLD = 8  # Technology level at which AI governance becomes possible\n",
    "\n",
    "# Define the Individual class with ultra-advanced features\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, technology_level, environmental_adaptation, risk_taking, tradition, religion, leadership_style, emotional_intelligence, mental_health, ethics, communication, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.technology_level = technology_level\n",
    "        self.environmental_adaptation = environmental_adaptation\n",
    "        self.risk_taking = risk_taking\n",
    "        self.tradition = tradition\n",
    "        self.religion = religion\n",
    "        self.leadership_style = leadership_style\n",
    "        self.emotional_intelligence = emotional_intelligence\n",
    "        self.mental_health = mental_health\n",
    "        self.ethics = ethics\n",
    "        self.communication = communication\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "        self.group = None  # Track group membership\n",
    "        self.language = random.uniform(0, 1)  # Language diversity\n",
    "        self.immune_system = random.uniform(0.7, 1)  # Further increased base immunity\n",
    "        self.symbiotic_relationship = False  # Whether the individual has a symbiotic relationship with another species\n",
    "        self.genetic_code = ''.join(random.choices('ATCG', k=1000))  # Simulate complex genetic sequences\n",
    "        self.quantum_entanglement = random.random() < 0.1  # Probability of being quantum entangled with another individual\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        if self.age > 40:\n",
    "            self.health *= 0.98  # Slower health decline with age\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        return 18 <= self.age <= 40\n",
    "\n",
    "    def mutate(self):\n",
    "        # Complex mutations with potential for adaptation and technology improvement\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.03, 0.03)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.03, 0.03)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on birth rate\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.03, 0.03)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on health\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.03, 0.03)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.03, 0.03)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.03, 0.03)))\n",
    "        self.technology_level = max(0, min(10, self.technology_level + random.uniform(-0.01, 0.02)))\n",
    "        self.environmental_adaptation = max(0, min(1, self.environmental_adaptation + random.uniform(-0.02, 0.02)))\n",
    "        self.risk_taking = max(0, min(1, self.risk_taking + random.uniform(-0.03, 0.03)))\n",
    "        self.tradition = max(0, min(1, self.tradition + random.uniform(-0.03, 0.03)))\n",
    "        self.religion = max(0, min(1, self.religion + random.uniform(-0.03, 0.03)))\n",
    "        self.emotional_intelligence = max(0, min(1, self.emotional_intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.mental_health = max(0, min(1, self.mental_health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on mental health\n",
    "        self.ethics = max(0, min(1, self.ethics + random.uniform(-0.03, 0.03)))\n",
    "        self.communication = max(0, min(1, self.communication + random.uniform(-0.03, 0.03)))\n",
    "        self.immune_system = max(0.7, min(1, self.immune_system + random.uniform(-0.01, 0.01)))  # Further increased base immunity\n",
    "        # Simulate minor genetic mutations\n",
    "        mutation_point = random.randint(0, len(self.genetic_code) - 1)\n",
    "        self.genetic_code = self.genetic_code[:mutation_point] + random.choice('ATCG') + self.genetic_code[mutation_point + 1:]\n",
    "\n",
    "# Define the Group class to manage groups of individuals\n",
    "class Group:\n",
    "    def __init__(self, leader):\n",
    "        self.leader = leader\n",
    "        self.members = [leader]\n",
    "        self.resources = {\"food\": 0, \"water\": 0, \"shelter\": 0, \"medicine\": 0, \"tools\": 0, \"currency\": 0}\n",
    "        self.territory = random.uniform(0.5, 1)  # Increase base territory richness\n",
    "        self.cultural_practices = random.uniform(0.5, 1)  # More shared cultural practices\n",
    "        self.language = leader.language  # Shared language within the group\n",
    "        self.trade_networks = 0  # Track trade relationships with other groups\n",
    "        self.governance_structure = leader.leadership_style  # Initial governance style\n",
    "        self.laws = {}  # Simple legal system\n",
    "        self.education_level = leader.intelligence * 0.5  # Education system influenced by leader's intelligence\n",
    "        self.ethical_standards = leader.ethics * 0.5  # Group's ethical standards influenced by leader's ethics\n",
    "        self.space_technology = 0  # Track space exploration capability\n",
    "        self.automation_level = 0  # Level of automation in the group\n",
    "        self.ai_influence = 0  # Influence of AI in governance and decision-making\n",
    "        self.corporate_influence = 0  # Influence of corporations on the group\n",
    "        self.quantum_technology = 0  # Track quantum technology development\n",
    "        self.memes = {}  # Track cultural memes\n",
    "        self.parallel_reality_links = 0  # Number of connections to parallel realities\n",
    "        self.hive_mind_level = 0  # Degree of hive mind integration within the group\n",
    "        self.virtual_presence = 0  # Extent to which the group exists within a simulated reality\n",
    "\n",
    "    def add_member(self, individual):\n",
    "        individual.group = self\n",
    "        self.members.append(individual)\n",
    "\n",
    "    def manage_resources(self):\n",
    "        # Allocate resources among members\n",
    "        for resource in self.resources:\n",
    "            if self.resources[resource] > 0:\n",
    "                allocation = self.resources[resource] / len(self.members)\n",
    "                for member in self.members:\n",
    "                    if resource == \"food\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"water\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"shelter\":\n",
    "                        member.health += allocation * 0.05\n",
    "                    elif resource == \"medicine\":\n",
    "                        member.health += allocation * 0.2\n",
    "                    elif resource == \"tools\":\n",
    "                        member.technology_level += allocation * 0.1\n",
    "                    elif resource == \"currency\":\n",
    "                        member.wealth += allocation * 0.1\n",
    "\n",
    "    def trade_with_group(self, other_group):\n",
    "        # Trade resources between groups based on their trade networks and resources\n",
    "        if self.trade_networks > 0 and other_group.trade_networks > 0:\n",
    "            trade_amount = min(self.resources[\"tools\"], other_group.resources[\"medicine\"])\n",
    "            self.resources[\"tools\"] -= trade_amount\n",
    "            other_group.resources[\"medicine\"] += trade_amount\n",
    "            trade_amount = min(self.resources[\"currency\"], other_group.resources[\"food\"])\n",
    "            self.resources[\"currency\"] -= trade_amount\n",
    "            other_group.resources[\"food\"] += trade_amount\n",
    "\n",
    "    def compete_for_resources(self, other_group):\n",
    "        # Complex competition logic including technology and leadership influence\n",
    "        if (self.territory + self.leader.intelligence + self.leader.technology_level + random.uniform(-0.1, 0.1)) > \\\n",
    "           (other_group.territory + other_group.leader.intelligence + other_group.leader.technology_level):\n",
    "            self.resources[\"food\"] += other_group.resources[\"food\"] * 0.5\n",
    "            other_group.resources[\"food\"] *= 0.5\n",
    "            self.resources[\"water\"] += other_group.resources[\"water\"] * 0.5\n",
    "            other_group.resources[\"water\"] *= 0.5\n",
    "            self.resources[\"shelter\"] += other_group.resources[\"shelter\"] * 0.5\n",
    "            other_group.resources[\"shelter\"] *= 0.5\n",
    "\n",
    "    def enforce_laws(self):\n",
    "        # Enforce laws to maintain order within the group\n",
    "        if self.governance_structure in ['autocratic', 'democratic']:\n",
    "            for law, enforcement in self.laws.items():\n",
    "                for member in self.members:\n",
    "                    if random.random() > enforcement:\n",
    "                        member.mental_health -= 0.05  # Punish lawbreakers with less severity\n",
    "\n",
    "    def develop_space_technology(self):\n",
    "        if self.leader.technology_level > 8 and random.random() < 0.01:\n",
    "            self.space_technology += 1\n",
    "\n",
    "    def increase_automation(self):\n",
    "        if self.leader.technology_level > 7 and random.random() < 0.02:\n",
    "            self.automation_level += 1\n",
    "\n",
    "    def engage_in_diplomacy(self, other_group):\n",
    "        if random.random() < self.ethical_standards * 0.1:\n",
    "            # Form alliances or trade agreements\n",
    "            self.trade_networks += 1\n",
    "            other_group.trade_networks += 1\n",
    "\n",
    "    def engage_in_warfare(self, other_group):\n",
    "        if random.random() < self.leader.aggression * 0.1:\n",
    "            # Initiate war with another group\n",
    "            self.compete_for_resources(other_group)\n",
    "\n",
    "    def deploy_nuclear_weapons(self, other_group):\n",
    "        if random.random() < NUCLEAR_WAR_PROBABILITY:\n",
    "            # Engage in nuclear warfare\n",
    "            destruction_factor = random.uniform(0.3, 0.7)\n",
    "            self.resources[\"food\"] -= self.resources[\"food\"] * destruction_factor\n",
    "            other_group.resources[\"food\"] -= other_group.resources[\"food\"] * destruction_factor\n",
    "            self.resources[\"water\"] -= self.resources[\"water\"] * destruction_factor\n",
    "            other_group.resources[\"water\"] -= other_group.resources[\"water\"] * destruction_factor\n",
    "            self.resources[\"shelter\"] -= self.resources[\"shelter\"] * destruction_factor\n",
    "            other_group.resources[\"shelter\"] -= other_group.resources[\"shelter\"] * destruction_factor\n",
    "\n",
    "    def establish_ai_governance(self):\n",
    "        if self.leader.technology_level >= AI_GOVERNANCE_THRESHOLD:\n",
    "            self.governance_structure = 'AI-driven'\n",
    "            self.ai_influence += 1\n",
    "\n",
    "    def develop_quantum_technology(self):\n",
    "        if self.leader.technology_level > 9 and random.random() < 0.02:\n",
    "            self.quantum_technology += 1\n",
    "\n",
    "    def corporate_takeover(self):\n",
    "        if random.random() < 0.05:\n",
    "            self.corporate_influence += 1\n",
    "            self.governance_structure = 'Corporate'\n",
    "\n",
    "    def spread_memes(self, meme):\n",
    "        # Spread a cultural meme within the group\n",
    "        if meme not in self.memes:\n",
    "            self.memes[meme] = 1\n",
    "        else:\n",
    "            self.memes[meme] += 1\n",
    "        for member in self.members:\n",
    "            if random.random() < 0.1:\n",
    "                member.tradition += 0.01  # Memes may influence cultural traits\n",
    "\n",
    "    def link_parallel_realities(self):\n",
    "        if random.random() < 0.01:\n",
    "            self.parallel_reality_links += 1\n",
    "\n",
    "    def form_hive_mind(self):\n",
    "        if random.random() < 0.05:\n",
    "            self.hive_mind_level += 1\n",
    "\n",
    "    def enter_virtual_reality(self):\n",
    "        if random.random() < 0.1:\n",
    "            self.virtual_presence += 1\n",
    "\n",
    "# Define the Population class with ultra-advanced features\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.groups = []\n",
    "        self.food = INITIAL_FOOD\n",
    "        self.water = INITIAL_WATER\n",
    "        self.shelter = INITIAL_SHELTER\n",
    "        self.medicine = MEDICINE_REGENERATION\n",
    "        self.tools = TOOLS_REGENERATION\n",
    "        self.currency = 3000  # Initial currency for economic systems\n",
    "        self.season = \"Spring\"\n",
    "        self.technology_progress = 0\n",
    "        self.climate_trend = 0  # Track long-term climate trends\n",
    "        self.climate_cycle = 0  # Track the climate cycle\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0.5, 1),  # Increased base birth rate\n",
    "            intelligence=random.uniform(0.5, 1),  # Increased base intelligence\n",
    "            social_cohesion=random.uniform(0.5, 1),  # Increased base social cohesion\n",
    "            health=random.uniform(0.5, 1),  # Increased base health\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0.5, 1),  # Increased base cooperation\n",
    "            curiosity=random.uniform(0, 1),\n",
    "            technology_level=random.uniform(0.5, 1),  # Increased base technology level\n",
    "            environmental_adaptation=random.uniform(0.5, 1),  # Increased base environmental adaptation\n",
    "            risk_taking=random.uniform(0, 1),\n",
    "            tradition=random.uniform(0, 1),\n",
    "            religion=random.uniform(0, 1),\n",
    "            leadership_style=random.choice(['autocratic', 'democratic', 'tribal']),\n",
    "            emotional_intelligence=random.uniform(0.5, 1),  # Increased base emotional intelligence\n",
    "            mental_health=random.uniform(0.5, 1),  # Increased base mental health\n",
    "            ethics=random.uniform(0.5, 1),  # Added ethics trait\n",
    "            communication=random.uniform(0.5, 1)  # Added communication trait\n",
    "        )\n",
    "    \n",
    "    def form_group(self, leader):\n",
    "        group = Group(leader)\n",
    "        self.groups.append(group)\n",
    "        return group\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Environmental effects: seasonal, climate variability, and long-term trends\n",
    "        self.update_season()\n",
    "        seasonal_factor = self.get_seasonal_factor()\n",
    "        climate_factor = 1 + random.uniform(-CLIMATE_VARIABILITY, CLIMATE_VARIABILITY) + self.climate_trend\n",
    "        self.food *= seasonal_factor * climate_factor\n",
    "        self.water *= seasonal_factor * climate_factor\n",
    "        self.shelter *= seasonal_factor * climate_factor\n",
    "        self.medicine *= seasonal_factor * climate_factor\n",
    "        self.tools *= seasonal_factor * climate_factor\n",
    "        self.currency *= seasonal_factor * climate_factor\n",
    "\n",
    "        # Long-term climate change\n",
    "        self.climate_trend += LONG_TERM_CLIMATE_CHANGE * random.choice([-1, 1])\n",
    "\n",
    "        # Climate cycles (e.g., ice ages)\n",
    "        self.climate_cycle += 1\n",
    "        if self.climate_cycle >= CLIMATE_CYCLE_LENGTH:\n",
    "            self.climate_cycle = 0\n",
    "            self.climate_trend *= -1  # Reverse the climate trend\n",
    "\n",
    "        # Catastrophic events\n",
    "        if random.random() < CATASTROPHIC_EVENT_PROBABILITY:\n",
    "            catastrophic_impact = random.uniform(0.3, 0.7)  # Reduced impact of catastrophic events\n",
    "            self.food -= self.food * catastrophic_impact\n",
    "            self.water -= self.water * catastrophic_impact\n",
    "            self.shelter -= self.shelter * catastrophic_impact\n",
    "            self.medicine -= self.medicine * catastrophic_impact\n",
    "            self.tools -= self.tools * catastrophic_impact\n",
    "            self.currency -= self.currency * catastrophic_impact\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > catastrophic_impact]\n",
    "\n",
    "        # Disease outbreak\n",
    "        if random.random() < DISEASE_SPREAD_PROBABILITY:\n",
    "            disease_severity = random.uniform(0.1, 0.3)  # Reduced severity of diseases\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > disease_severity * (1 - ind.immune_system)]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum((ind.health * 1.5 + ind.technology_level * 0.1) for ind in self.individuals)\n",
    "        total_food_demand = total_resource_demand * 0.3\n",
    "        total_water_demand = total_resource_demand * 0.3\n",
    "        total_shelter_demand = total_resource_demand * 0.2\n",
    "        total_medicine_demand = total_resource_demand * 0.1\n",
    "        total_tools_demand = total_resource_demand * 0.1\n",
    "        total_currency_demand = total_resource_demand * 0.05  # Demand for currency in trade\n",
    "\n",
    "        # Check resource limits\n",
    "        if (total_food_demand > self.food or total_water_demand > self.water or total_shelter_demand > self.shelter or \n",
    "            total_medicine_demand > self.medicine or total_tools_demand > self.tools or total_currency_demand > self.currency):\n",
    "            survival_chance = min(self.food / total_food_demand, self.water / total_water_demand, self.shelter / total_shelter_demand,\n",
    "                                  self.medicine / total_medicine_demand, self.tools / total_tools_demand, self.currency / total_currency_demand)\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Social dynamics: Group formation, migration, trade, competition, and governance\n",
    "        for ind in self.individuals:\n",
    "            if ind.group is None:\n",
    "                leader = ind if ind.intelligence > random.uniform(0, 1) else random.choice(self.individuals)\n",
    "                group = self.form_group(leader)\n",
    "                group.add_member(ind)\n",
    "            elif random.random() < ind.curiosity * 0.05:\n",
    "                # Migration to a new group\n",
    "                new_group = self.form_group(ind)\n",
    "                new_group.add_member(ind)\n",
    "        \n",
    "        # Group management\n",
    "        for group in self.groups:\n",
    "            group.manage_resources()\n",
    "            group.enforce_laws()\n",
    "            group.develop_space_technology()\n",
    "            group.increase_automation()\n",
    "            group.establish_ai_governance()\n",
    "            group.develop_quantum_technology()\n",
    "            group.corporate_takeover()\n",
    "            group.link_parallel_realities()\n",
    "            group.form_hive_mind()\n",
    "            group.enter_virtual_reality()\n",
    "\n",
    "            # Spread memes in the group\n",
    "            if random.random() < 0.1:\n",
    "                meme = f\"Meme-{random.randint(1, 100)}\"\n",
    "                group.spread_memes(meme)\n",
    "\n",
    "            if random.random() < 0.1:  # Random chance of competition\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.compete_for_resources(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of trade\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.trade_with_group(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of diplomacy\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_diplomacy(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_warfare(other_group)\n",
    "\n",
    "            if random.random() < NUCLEAR_WAR_PROBABILITY:  # Random chance of nuclear warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.deploy_nuclear_weapons(other_group)\n",
    "\n",
    "        # Technology development and adaptation\n",
    "        for ind in self.individuals:\n",
    "            if random.random() < ind.intelligence * 0.05:\n",
    "                ind.technology_level += 0.1\n",
    "            if random.random() < ind.cooperation * 0.1:\n",
    "                self.technology_progress += 0.1\n",
    "\n",
    "            # Environmental adaptation\n",
    "            if self.climate_trend > 0:\n",
    "                ind.health *= (1 + ind.environmental_adaptation * 0.1)\n",
    "            elif self.climate_trend < 0:\n",
    "                ind.health *= (1 - ind.environmental_adaptation * 0.1)\n",
    "        \n",
    "        # Birth events influenced by technology, medicine, adaptation, and cultural traits\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.food / (CARRYING_CAPACITY * 2)) * (1 + ind.technology_level * 0.05)\n",
    "                if random.random() < birth_probability:\n",
    "                    child = self.create_random_individual()\n",
    "                    child.health = (ind.health + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.intelligence = (ind.intelligence + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.language = ind.language  # Inherit language\n",
    "                    child.immune_system = (ind.immune_system + random.uniform(-0.05, 0.05)) / 2  # Inherit immune system\n",
    "                    new_births.append(child)\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Resource regeneration\n",
    "        self.food = min(INITIAL_FOOD, self.food + FOOD_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.water = min(INITIAL_WATER, self.water + WATER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.shelter = min(INITIAL_SHELTER, self.shelter + SHELTER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.medicine = min(MEDICINE_REGENERATION, self.medicine + MEDICINE_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.tools = min(TOOLS_REGENERATION, self.tools + TOOLS_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.currency = min(3000, self.currency + CURRENCY_REGENERATION * seasonal_factor * climate_factor)\n",
    "\n",
    "    def update_season(self):\n",
    "        if self.season == \"Spring\":\n",
    "            self.season = \"Summer\"\n",
    "        elif self.season == \"Summer\":\n",
    "            self.season = \"Autumn\"\n",
    "        elif self.season == \"Autumn\":\n",
    "            self.season = \"Winter\"\n",
    "        elif self.season == \"Winter\":\n",
    "            self.season = \"Spring\"\n",
    "    \n",
    "    def get_seasonal_factor(self):\n",
    "        if self.season == \"Spring\":\n",
    "            return 1.1\n",
    "        elif self.season == \"Summer\":\n",
    "            return 1.0\n",
    "        elif self.season == \"Autumn\":\n",
    "            return 0.85\n",
    "        elif self.season == \"Winter\":\n",
    "            return 0.7\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        technology_levels = []\n",
    "        climate_trends = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "            technology_levels.append(self.technology_progress / len(self.individuals) if len(self.individuals) > 0 else 0)\n",
    "            climate_trends.append(self.climate_trend)\n",
    "        \n",
    "        return population_sizes, technology_levels, climate_trends\n",
    "\n",
    "# Run the ultra-advanced simulation with even more advanced features\n",
    "population = Population(100)\n",
    "population_sizes, technology_levels, climate_trends = population.simulate_generations(100)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "352e48a7",
   "metadata": {},
   "source": [
    "## Futuristic  Population Simulator with Added Reality Manipulation and Multiversal Governance, Fifth-Dimensional Entities and Hyperintelligent Beings, Temporal Symbiosis and Chronal Feedback Loops, Noosphere and Collective Metaconsciousness, Zero-Point Energy and Vacuum Engineering, Cosmic Awareness and Universal Architects, Thought-Matter Integration and Consciousness-Driven Reality, Planck-Scale Engineering and Subatomic Manipulation, Cosmic String Networks and Extra-Spatial Architectures, Omega Point and the Final Singularity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1bcd206",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Constants for resources and environment\n",
    "INITIAL_FOOD = 7000  # Further increased initial resources\n",
    "INITIAL_WATER = 7000\n",
    "INITIAL_SHELTER = 6500\n",
    "FOOD_REGENERATION = 700  # Further increased resource regeneration\n",
    "WATER_REGENERATION = 700\n",
    "SHELTER_REGENERATION = 600\n",
    "MEDICINE_REGENERATION = 500\n",
    "TOOLS_REGENERATION = 500\n",
    "CURRENCY_REGENERATION = 400\n",
    "\n",
    "CARRYING_CAPACITY = 1400  # Increased carrying capacity\n",
    "\n",
    "# Environmental factors\n",
    "SEASONAL_EFFECT = 0.15\n",
    "CLIMATE_VARIABILITY = 0.05\n",
    "DISASTER_PROBABILITY = 0.01  # Further reduced probability of a catastrophic event\n",
    "LONG_TERM_CLIMATE_CHANGE = 0.001  # Gradual climate change effect\n",
    "CATASTROPHIC_EVENT_PROBABILITY = 0.005  # Rare probability of catastrophic events\n",
    "DISEASE_SPREAD_PROBABILITY = 0.02  # Further reduced probability of disease outbreak\n",
    "CLIMATE_CYCLE_LENGTH = 50  # Length of climate cycles (e.g., ice ages)\n",
    "NUCLEAR_WAR_PROBABILITY = 0.002  # Probability of nuclear war\n",
    "AI_GOVERNANCE_THRESHOLD = 8  # Technology level at which AI governance becomes possible\n",
    "\n",
    "# Define the Individual class with ultra-esoteric features\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, technology_level, environmental_adaptation, risk_taking, tradition, religion, leadership_style, emotional_intelligence, mental_health, ethics, communication, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.technology_level = technology_level\n",
    "        self.environmental_adaptation = environmental_adaptation\n",
    "        self.risk_taking = risk_taking\n",
    "        self.tradition = tradition\n",
    "        self.religion = religion\n",
    "        self.leadership_style = leadership_style\n",
    "        self.emotional_intelligence = emotional_intelligence\n",
    "        self.mental_health = mental_health\n",
    "        self.ethics = ethics\n",
    "        self.communication = communication\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "        self.group = None  # Track group membership\n",
    "        self.language = random.uniform(0, 1)  # Language diversity\n",
    "        self.immune_system = random.uniform(0.7, 1)  # Further increased base immunity\n",
    "        self.symbiotic_relationship = False  # Whether the individual has a symbiotic relationship with another species\n",
    "        self.genetic_code = ''.join(random.choices('ATCG', k=1000))  # Simulate complex genetic sequences\n",
    "        self.quantum_entanglement = random.random() < 0.1  # Probability of being quantum entangled with another individual\n",
    "        self.cosmic_awareness = random.random() < 0.05  # Probability of having cosmic awareness\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        if self.age > 40:\n",
    "            self.health *= 0.98  # Slower health decline with age\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        return 18 <= self.age <= 40\n",
    "\n",
    "    def mutate(self):\n",
    "        # Complex mutations with potential for adaptation and technology improvement\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.03, 0.03)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.03, 0.03)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on birth rate\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.03, 0.03)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on health\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.03, 0.03)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.03, 0.03)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.03, 0.03)))\n",
    "        self.technology_level = max(0, min(10, self.technology_level + random.uniform(-0.01, 0.02)))\n",
    "        self.environmental_adaptation = max(0, min(1, self.environmental_adaptation + random.uniform(-0.02, 0.02)))\n",
    "        self.risk_taking = max(0, min(1, self.risk_taking + random.uniform(-0.03, 0.03)))\n",
    "        self.tradition = max(0, min(1, self.tradition + random.uniform(-0.03, 0.03)))\n",
    "        self.religion = max(0, min(1, self.religion + random.uniform(-0.03, 0.03)))\n",
    "        self.emotional_intelligence = max(0, min(1, self.emotional_intelligence + random.uniform(-0.03, 0.03)))\n",
    "        self.mental_health = max(0, min(1, self.mental_health + random.uniform(-0.02, 0.02)))  # Reduced mutation impact on mental health\n",
    "        self.ethics = max(0, min(1, self.ethics + random.uniform(-0.03, 0.03)))\n",
    "        self.communication = max(0, min(1, self.communication + random.uniform(-0.03, 0.03)))\n",
    "        self.immune_system = max(0.7, min(1, self.immune_system + random.uniform(-0.01, 0.01)))  # Further increased base immunity\n",
    "        # Simulate minor genetic mutations\n",
    "        mutation_point = random.randint(0, len(self.genetic_code) - 1)\n",
    "        self.genetic_code = self.genetic_code[:mutation_point] + random.choice('ATCG') + self.genetic_code[mutation_point + 1:]\n",
    "\n",
    "# Define the Group class to manage groups of individuals\n",
    "class Group:\n",
    "    def __init__(self, leader):\n",
    "        self.leader = leader\n",
    "        self.members = [leader]\n",
    "        self.resources = {\"food\": 0, \"water\": 0, \"shelter\": 0, \"medicine\": 0, \"tools\": 0, \"currency\": 0}\n",
    "        self.territory = random.uniform(0.5, 1)  # Increase base territory richness\n",
    "        self.cultural_practices = random.uniform(0.5, 1)  # More shared cultural practices\n",
    "        self.language = leader.language  # Shared language within the group\n",
    "        self.trade_networks = 0  # Track trade relationships with other groups\n",
    "        self.governance_structure = leader.leadership_style  # Initial governance style\n",
    "        self.laws = {}  # Simple legal system\n",
    "        self.education_level = leader.intelligence * 0.5  # Education system influenced by leader's intelligence\n",
    "        self.ethical_standards = leader.ethics * 0.5  # Group's ethical standards influenced by leader's ethics\n",
    "        self.space_technology = 0  # Track space exploration capability\n",
    "        self.automation_level = 0  # Level of automation in the group\n",
    "        self.ai_influence = 0  # Influence of AI in governance and decision-making\n",
    "        self.corporate_influence = 0  # Influence of corporations on the group\n",
    "        self.quantum_technology = 0  # Track quantum technology development\n",
    "        self.memes = {}  # Track cultural memes\n",
    "        self.parallel_reality_links = 0  # Number of connections to parallel realities\n",
    "        self.hive_mind_level = 0  # Degree of hive mind integration within the group\n",
    "        self.virtual_presence = 0  # Extent to which the group exists within a simulated reality\n",
    "        self.reality_bending = 0  # Ability to manipulate reality\n",
    "        self.noosphere_integration = 0  # Degree of integration into the global noosphere\n",
    "        self.zero_point_energy = 0  # Mastery of zero-point energy technologies\n",
    "\n",
    "    def add_member(self, individual):\n",
    "        individual.group = self\n",
    "        self.members.append(individual)\n",
    "\n",
    "    def manage_resources(self):\n",
    "        # Allocate resources among members\n",
    "        for resource in self.resources:\n",
    "            if self.resources[resource] > 0:\n",
    "                allocation = self.resources[resource] / len(self.members)\n",
    "                for member in self.members:\n",
    "                    if resource == \"food\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"water\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"shelter\":\n",
    "                        member.health += allocation * 0.05\n",
    "                    elif resource == \"medicine\":\n",
    "                        member.health += allocation * 0.2\n",
    "                    elif resource == \"tools\":\n",
    "                        member.technology_level += allocation * 0.1\n",
    "                    elif resource == \"currency\":\n",
    "                        member.wealth += allocation * 0.1\n",
    "\n",
    "    def trade_with_group(self, other_group):\n",
    "        # Trade resources between groups based on their trade networks and resources\n",
    "        if self.trade_networks > 0 and other_group.trade_networks > 0:\n",
    "            trade_amount = min(self.resources[\"tools\"], other_group.resources[\"medicine\"])\n",
    "            self.resources[\"tools\"] -= trade_amount\n",
    "            other_group.resources[\"medicine\"] += trade_amount\n",
    "            trade_amount = min(self.resources[\"currency\"], other_group.resources[\"food\"])\n",
    "            self.resources[\"currency\"] -= trade_amount\n",
    "            other_group.resources[\"food\"] += trade_amount\n",
    "\n",
    "    def compete_for_resources(self, other_group):\n",
    "        # Complex competition logic including technology and leadership influence\n",
    "        if (self.territory + self.leader.intelligence + self.leader.technology_level + random.uniform(-0.1, 0.1)) > \\\n",
    "           (other_group.territory + other_group.leader.intelligence + other_group.leader.technology_level):\n",
    "            self.resources[\"food\"] += other_group.resources[\"food\"] * 0.5\n",
    "            other_group.resources[\"food\"] *= 0.5\n",
    "            self.resources[\"water\"] += other_group.resources[\"water\"] * 0.5\n",
    "            other_group.resources[\"water\"] *= 0.5\n",
    "            self.resources[\"shelter\"] += other_group.resources[\"shelter\"] * 0.5\n",
    "            other_group.resources[\"shelter\"] *= 0.5\n",
    "\n",
    "    def enforce_laws(self):\n",
    "        # Enforce laws to maintain order within the group\n",
    "        if self.governance_structure in ['autocratic', 'democratic']:\n",
    "            for law, enforcement in self.laws.items():\n",
    "                for member in self.members:\n",
    "                    if random.random() > enforcement:\n",
    "                        member.mental_health -= 0.05  # Punish lawbreakers with less severity\n",
    "\n",
    "    def develop_space_technology(self):\n",
    "        if self.leader.technology_level > 8 and random.random() < 0.01:\n",
    "            self.space_technology += 1\n",
    "\n",
    "    def increase_automation(self):\n",
    "        if self.leader.technology_level > 7 and random.random() < 0.02:\n",
    "            self.automation_level += 1\n",
    "\n",
    "    def engage_in_diplomacy(self, other_group):\n",
    "        if random.random() < self.ethical_standards * 0.1:\n",
    "            # Form alliances or trade agreements\n",
    "            self.trade_networks += 1\n",
    "            other_group.trade_networks += 1\n",
    "\n",
    "    def engage_in_warfare(self, other_group):\n",
    "        if random.random() < self.leader.aggression * 0.1:\n",
    "            # Initiate war with another group\n",
    "            self.compete_for_resources(other_group)\n",
    "\n",
    "    def deploy_nuclear_weapons(self, other_group):\n",
    "        if random.random() < NUCLEAR_WAR_PROBABILITY:\n",
    "            # Engage in nuclear warfare\n",
    "            destruction_factor = random.uniform(0.3, 0.7)\n",
    "            self.resources[\"food\"] -= self.resources[\"food\"] * destruction_factor\n",
    "            other_group.resources[\"food\"] -= other_group.resources[\"food\"] * destruction_factor\n",
    "            self.resources[\"water\"] -= self.resources[\"water\"] * destruction_factor\n",
    "            other_group.resources[\"water\"] -= other_group.resources[\"water\"] * destruction_factor\n",
    "            self.resources[\"shelter\"] -= self.resources[\"shelter\"] * destruction_factor\n",
    "            other_group.resources[\"shelter\"] -= other_group.resources[\"shelter\"] * destruction_factor\n",
    "\n",
    "    def establish_ai_governance(self):\n",
    "        if self.leader.technology_level >= AI_GOVERNANCE_THRESHOLD:\n",
    "            self.governance_structure = 'AI-driven'\n",
    "            self.ai_influence += 1\n",
    "\n",
    "    def develop_quantum_technology(self):\n",
    "        if self.leader.technology_level > 9 and random.random() < 0.02:\n",
    "            self.quantum_technology += 1\n",
    "\n",
    "    def corporate_takeover(self):\n",
    "        if random.random() < 0.05:\n",
    "            self.corporate_influence += 1\n",
    "            self.governance_structure = 'Corporate'\n",
    "\n",
    "    def spread_memes(self, meme):\n",
    "        # Spread a cultural meme within the group\n",
    "        if meme not in self.memes:\n",
    "            self.memes[meme] = 1\n",
    "        else:\n",
    "            self.memes[meme] += 1\n",
    "        for member in self.members:\n",
    "            if random.random() < 0.1:\n",
    "                member.tradition += 0.01  # Memes may influence cultural traits\n",
    "\n",
    "    def link_parallel_realities(self):\n",
    "        if random.random() < 0.01:\n",
    "            self.parallel_reality_links += 1\n",
    "\n",
    "    def form_hive_mind(self):\n",
    "        if random.random() < 0.05:\n",
    "            self.hive_mind_level += 1\n",
    "\n",
    "    def enter_virtual_reality(self):\n",
    "        if random.random() < 0.1:\n",
    "            self.virtual_presence += 1\n",
    "\n",
    "    def bend_reality(self):\n",
    "        if random.random() < 0.01:\n",
    "            self.reality_bending += 1\n",
    "\n",
    "    def integrate_noosphere(self):\n",
    "        if random.random() < 0.05:\n",
    "            self.noosphere_integration += 1\n",
    "\n",
    "    def master_zero_point_energy(self):\n",
    "        if random.random() < 0.02:\n",
    "            self.zero_point_energy += 1\n",
    "\n",
    "# Define the Population class with ultra-esoteric features\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.groups = []\n",
    "        self.food = INITIAL_FOOD\n",
    "        self.water = INITIAL_WATER\n",
    "        self.shelter = INITIAL_SHELTER\n",
    "        self.medicine = MEDICINE_REGENERATION\n",
    "        self.tools = TOOLS_REGENERATION\n",
    "        self.currency = 4000  # Initial currency for economic systems\n",
    "        self.season = \"Spring\"\n",
    "        self.technology_progress = 0\n",
    "        self.climate_trend = 0  # Track long-term climate trends\n",
    "        self.climate_cycle = 0  # Track the climate cycle\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0.5, 1),  # Increased base birth rate\n",
    "            intelligence=random.uniform(0.5, 1),  # Increased base intelligence\n",
    "            social_cohesion=random.uniform(0.5, 1),  # Increased base social cohesion\n",
    "            health=random.uniform(0.5, 1),  # Increased base health\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0.5, 1),  # Increased base cooperation\n",
    "            curiosity=random.uniform(0, 1),\n",
    "            technology_level=random.uniform(0.5, 1),  # Increased base technology level\n",
    "            environmental_adaptation=random.uniform(0.5, 1),  # Increased base environmental adaptation\n",
    "            risk_taking=random.uniform(0, 1),\n",
    "            tradition=random.uniform(0, 1),\n",
    "            religion=random.uniform(0, 1),\n",
    "            leadership_style=random.choice(['autocratic', 'democratic', 'tribal']),\n",
    "            emotional_intelligence=random.uniform(0.5, 1),  # Increased base emotional intelligence\n",
    "            mental_health=random.uniform(0.5, 1),  # Increased base mental health\n",
    "            ethics=random.uniform(0.5, 1),  # Added ethics trait\n",
    "            communication=random.uniform(0.5, 1)  # Added communication trait\n",
    "        )\n",
    "    \n",
    "    def form_group(self, leader):\n",
    "        group = Group(leader)\n",
    "        self.groups.append(group)\n",
    "        return group\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Environmental effects: seasonal, climate variability, and long-term trends\n",
    "        self.update_season()\n",
    "        seasonal_factor = self.get_seasonal_factor()\n",
    "        climate_factor = 1 + random.uniform(-CLIMATE_VARIABILITY, CLIMATE_VARIABILITY) + self.climate_trend\n",
    "        self.food *= seasonal_factor * climate_factor\n",
    "        self.water *= seasonal_factor * climate_factor\n",
    "        self.shelter *= seasonal_factor * climate_factor\n",
    "        self.medicine *= seasonal_factor * climate_factor\n",
    "        self.tools *= seasonal_factor * climate_factor\n",
    "        self.currency *= seasonal_factor * climate_factor\n",
    "\n",
    "        # Long-term climate change\n",
    "        self.climate_trend += LONG_TERM_CLIMATE_CHANGE * random.choice([-1, 1])\n",
    "\n",
    "        # Climate cycles (e.g., ice ages)\n",
    "        self.climate_cycle += 1\n",
    "        if self.climate_cycle >= CLIMATE_CYCLE_LENGTH:\n",
    "            self.climate_cycle = 0\n",
    "            self.climate_trend *= -1  # Reverse the climate trend\n",
    "\n",
    "        # Catastrophic events\n",
    "        if random.random() < CATASTROPHIC_EVENT_PROBABILITY:\n",
    "            catastrophic_impact = random.uniform(0.3, 0.7)  # Reduced impact of catastrophic events\n",
    "            self.food -= self.food * catastrophic_impact\n",
    "            self.water -= self.water * catastrophic_impact\n",
    "            self.shelter -= self.shelter * catastrophic_impact\n",
    "            self.medicine -= self.medicine * catastrophic_impact\n",
    "            self.tools -= self.tools * catastrophic_impact\n",
    "            self.currency -= self.currency * catastrophic_impact\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > catastrophic_impact]\n",
    "\n",
    "        # Disease outbreak\n",
    "        if random.random() < DISEASE_SPREAD_PROBABILITY:\n",
    "            disease_severity = random.uniform(0.1, 0.3)  # Reduced severity of diseases\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > disease_severity * (1 - ind.immune_system)]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum((ind.health * 1.5 + ind.technology_level * 0.1) for ind in self.individuals)\n",
    "        total_food_demand = total_resource_demand * 0.3\n",
    "        total_water_demand = total_resource_demand * 0.3\n",
    "        total_shelter_demand = total_resource_demand * 0.2\n",
    "        total_medicine_demand = total_resource_demand * 0.1\n",
    "        total_tools_demand = total_resource_demand * 0.1\n",
    "        total_currency_demand = total_resource_demand * 0.05  # Demand for currency in trade\n",
    "\n",
    "        # Check resource limits\n",
    "        if (total_food_demand > self.food or total_water_demand > self.water or total_shelter_demand > self.shelter or \n",
    "            total_medicine_demand > self.medicine or total_tools_demand > self.tools or total_currency_demand > self.currency):\n",
    "            survival_chance = min(self.food / total_food_demand, self.water / total_water_demand, self.shelter / total_shelter_demand,\n",
    "                                  self.medicine / total_medicine_demand, self.tools / total_tools_demand, self.currency / total_currency_demand)\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Social dynamics: Group formation, migration, trade, competition, and governance\n",
    "        for ind in self.individuals:\n",
    "            if ind.group is None:\n",
    "                leader = ind if ind.intelligence > random.uniform(0, 1) else random.choice(self.individuals)\n",
    "                group = self.form_group(leader)\n",
    "                group.add_member(ind)\n",
    "            elif random.random() < ind.curiosity * 0.05:\n",
    "                # Migration to a new group\n",
    "                new_group = self.form_group(ind)\n",
    "                new_group.add_member(ind)\n",
    "        \n",
    "        # Group management\n",
    "        for group in self.groups:\n",
    "            group.manage_resources()\n",
    "            group.enforce_laws()\n",
    "            group.develop_space_technology()\n",
    "            group.increase_automation()\n",
    "            group.establish_ai_governance()\n",
    "            group.develop_quantum_technology()\n",
    "            group.corporate_takeover()\n",
    "            group.link_parallel_realities()\n",
    "            group.form_hive_mind()\n",
    "            group.enter_virtual_reality()\n",
    "            group.bend_reality()\n",
    "            group.integrate_noosphere()\n",
    "            group.master_zero_point_energy()\n",
    "\n",
    "            # Spread memes in the group\n",
    "            if random.random() < 0.1:\n",
    "                meme = f\"Meme-{random.randint(1, 100)}\"\n",
    "                group.spread_memes(meme)\n",
    "\n",
    "            if random.random() < 0.1:  # Random chance of competition\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.compete_for_resources(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of trade\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.trade_with_group(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of diplomacy\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_diplomacy(other_group)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_warfare(other_group)\n",
    "\n",
    "            if random.random() < NUCLEAR_WAR_PROBABILITY:  # Random chance of nuclear warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.deploy_nuclear_weapons(other_group)\n",
    "\n",
    "        # Technology development and adaptation\n",
    "        for ind in self.individuals:\n",
    "            if random.random() < ind.intelligence * 0.05:\n",
    "                ind.technology_level += 0.1\n",
    "            if random.random() < ind.cooperation * 0.1:\n",
    "                self.technology_progress += 0.1\n",
    "\n",
    "            # Environmental adaptation\n",
    "            if self.climate_trend > 0:\n",
    "                ind.health *= (1 + ind.environmental_adaptation * 0.1)\n",
    "            elif self.climate_trend < 0:\n",
    "                ind.health *= (1 - ind.environmental_adaptation * 0.1)\n",
    "        \n",
    "        # Birth events influenced by technology, medicine, adaptation, and cultural traits\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.food / (CARRYING_CAPACITY * 2)) * (1 + ind.technology_level * 0.05)\n",
    "                if random.random() < birth_probability:\n",
    "                    child = self.create_random_individual()\n",
    "                    child.health = (ind.health + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.intelligence = (ind.intelligence + random.uniform(-0.05, 0.05)) / 2\n",
    "                    child.language = ind.language  # Inherit language\n",
    "                    child.immune_system = (ind.immune_system + random.uniform(-0.05, 0.05)) / 2  # Inherit immune system\n",
    "                    new_births.append(child)\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Resource regeneration\n",
    "        self.food = min(INITIAL_FOOD, self.food + FOOD_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.water = min(INITIAL_WATER, self.water + WATER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.shelter = min(INITIAL_SHELTER, self.shelter + SHELTER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.medicine = min(MEDICINE_REGENERATION, self.medicine + MEDICINE_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.tools = min(TOOLS_REGENERATION, self.tools + TOOLS_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.currency = min(4000, self.currency + CURRENCY_REGENERATION * seasonal_factor * climate_factor)\n",
    "\n",
    "    def update_season(self):\n",
    "        if self.season == \"Spring\":\n",
    "            self.season = \"Summer\"\n",
    "        elif self.season == \"Summer\":\n",
    "            self.season = \"Autumn\"\n",
    "        elif self.season == \"Autumn\":\n",
    "            self.season = \"Winter\"\n",
    "        elif self.season == \"Winter\":\n",
    "            self.season = \"Spring\"\n",
    "    \n",
    "    def get_seasonal_factor(self):\n",
    "        if self.season == \"Spring\":\n",
    "            return 1.1\n",
    "        elif self.season == \"Summer\":\n",
    "            return 1.0\n",
    "        elif self.season == \"Autumn\":\n",
    "            return 0.85\n",
    "        elif self.season == \"Winter\":\n",
    "            return 0.7\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        technology_levels = []\n",
    "        climate_trends = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "            technology_levels.append(self.technology_progress / len(self.individuals) if len(self.individuals) > 0 else 0)\n",
    "            climate_trends.append(self.climate_trend)\n",
    "        \n",
    "        return population_sizes, technology_levels, climate_trends\n",
    "\n",
    "# Run the ultra-esoteric simulation with even more advanced features\n",
    "population = Population(100)\n",
    "population_sizes, technology_levels, climate_trends = population.simulate_generations(100)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c6247bd",
   "metadata": {},
   "source": [
    "## Highly Realistic Population Simulator Going all the Way from Pre Human Civilization to Cosmic Entity Civilization in 1500+ Generations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c0cedc3",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Constants for resources and environment\n",
    "INITIAL_FOOD = 10000  # Increased initial resources\n",
    "INITIAL_WATER = 10000\n",
    "INITIAL_SHELTER = 9000\n",
    "FOOD_REGENERATION = 1000  # Increased resource regeneration\n",
    "WATER_REGENERATION = 1000\n",
    "SHELTER_REGENERATION = 900\n",
    "MEDICINE_REGENERATION = 800\n",
    "TOOLS_REGENERATION = 800\n",
    "CURRENCY_REGENERATION = 700\n",
    "\n",
    "CARRYING_CAPACITY = 2000  # Increased carrying capacity\n",
    "\n",
    "# Environmental factors\n",
    "SEASONAL_EFFECT = 0.10  # Slightly reduced seasonal effect\n",
    "CLIMATE_VARIABILITY = 0.03  # Reduced climate variability\n",
    "DISASTER_PROBABILITY = 0.005  # Reduced probability of a catastrophic event\n",
    "LONG_TERM_CLIMATE_CHANGE = 0.0005  # Slower climate change effect\n",
    "CATASTROPHIC_EVENT_PROBABILITY = 0.002  # Further reduced probability of catastrophic events\n",
    "DISEASE_SPREAD_PROBABILITY = 0.01  # Reduced probability of disease outbreak\n",
    "CLIMATE_CYCLE_LENGTH = 200  # Longer climate cycles\n",
    "NUCLEAR_WAR_PROBABILITY = 0.001  # Reduced probability of nuclear war\n",
    "AI_GOVERNANCE_THRESHOLD = 20  # Higher technology level required for AI governance\n",
    "\n",
    "# Incremental technology levels\n",
    "TECH_LEVEL_INCREMENT = 0.01  # Smaller increments for technology progression\n",
    "\n",
    "# Define the Individual class with slow progression features\n",
    "class Individual:\n",
    "    def __init__(self, aggression, peacefulness, birth_rate, intelligence, social_cohesion, health, wealth, cooperation, curiosity, technology_level, environmental_adaptation, risk_taking, tradition, religion, leadership_style, emotional_intelligence, mental_health, ethics, communication, age=0, lifespan=80):\n",
    "        self.aggression = aggression\n",
    "        self.peacefulness = peacefulness\n",
    "        self.birth_rate = birth_rate\n",
    "        self.intelligence = intelligence\n",
    "        self.social_cohesion = social_cohesion\n",
    "        self.health = health\n",
    "        self.wealth = wealth\n",
    "        self.cooperation = cooperation\n",
    "        self.curiosity = curiosity\n",
    "        self.technology_level = technology_level\n",
    "        self.environmental_adaptation = environmental_adaptation\n",
    "        self.risk_taking = risk_taking\n",
    "        self.tradition = tradition\n",
    "        self.religion = religion\n",
    "        self.leadership_style = leadership_style\n",
    "        self.emotional_intelligence = emotional_intelligence\n",
    "        self.mental_health = mental_health\n",
    "        self.ethics = ethics\n",
    "        self.communication = communication\n",
    "        self.age = age\n",
    "        self.lifespan = lifespan\n",
    "        self.group = None  # Track group membership\n",
    "        self.language = random.uniform(0, 1)  # Language diversity\n",
    "        self.immune_system = random.uniform(0.8, 1)  # Increased base immunity\n",
    "        self.symbiotic_relationship = False  # Whether the individual has a symbiotic relationship with another species\n",
    "        self.genetic_code = ''.join(random.choices('ATCG', k=1000))  # Simulate complex genetic sequences\n",
    "        self.cosmic_awareness = random.random() < 0.01  # Low probability of cosmic awareness\n",
    "\n",
    "    def age_one_year(self):\n",
    "        self.age += 1\n",
    "        if self.age > 50:\n",
    "            self.health *= 0.99  # Slower health decline with age\n",
    "        return self.age >= self.lifespan\n",
    "\n",
    "    def is_fertile(self):\n",
    "        return 20 <= self.age <= 45\n",
    "\n",
    "    def mutate(self):\n",
    "        # Slow and minor genetic mutations over generations\n",
    "        self.aggression = max(0, min(1, self.aggression + random.uniform(-0.01, 0.01)))\n",
    "        self.peacefulness = max(0, min(1, self.peacefulness + random.uniform(-0.01, 0.01)))\n",
    "        self.birth_rate = max(0, min(1, self.birth_rate + random.uniform(-0.005, 0.005)))\n",
    "        self.intelligence = max(0, min(1, self.intelligence + random.uniform(-0.01, 0.01)))\n",
    "        self.social_cohesion = max(0, min(1, self.social_cohesion + random.uniform(-0.01, 0.01)))\n",
    "        self.health = max(0, min(1, self.health + random.uniform(-0.005, 0.005)))\n",
    "        self.wealth = max(0, min(1, self.wealth + random.uniform(-0.01, 0.01)))\n",
    "        self.cooperation = max(0, min(1, self.cooperation + random.uniform(-0.01, 0.01)))\n",
    "        self.curiosity = max(0, min(1, self.curiosity + random.uniform(-0.01, 0.01)))\n",
    "        self.technology_level = max(0, min(10, self.technology_level + random.uniform(-0.002, 0.005)))\n",
    "        self.environmental_adaptation = max(0, min(1, self.environmental_adaptation + random.uniform(-0.005, 0.005)))\n",
    "        self.risk_taking = max(0, min(1, self.risk_taking + random.uniform(-0.01, 0.01)))\n",
    "        self.tradition = max(0, min(1, self.tradition + random.uniform(-0.01, 0.01)))\n",
    "        self.religion = max(0, min(1, self.religion + random.uniform(-0.01, 0.01)))\n",
    "        self.emotional_intelligence = max(0, min(1, self.emotional_intelligence + random.uniform(-0.01, 0.01)))\n",
    "        self.mental_health = max(0, min(1, self.mental_health + random.uniform(-0.005, 0.005)))\n",
    "        self.ethics = max(0, min(1, self.ethics + random.uniform(-0.01, 0.01)))\n",
    "        self.communication = max(0, min(1, self.communication + random.uniform(-0.01, 0.01)))\n",
    "        self.immune_system = max(0.8, min(1, self.immune_system + random.uniform(-0.002, 0.002)))\n",
    "        # Slow mutation of genetic code\n",
    "        mutation_point = random.randint(0, len(self.genetic_code) - 1)\n",
    "        self.genetic_code = self.genetic_code[:mutation_point] + random.choice('ATCG') + self.genetic_code[mutation_point + 1:]\n",
    "\n",
    "# Define the Group class with gradual progression features\n",
    "class Group:\n",
    "    def __init__(self, leader):\n",
    "        self.leader = leader\n",
    "        self.members = [leader]\n",
    "        self.resources = {\"food\": 0, \"water\": 0, \"shelter\": 0, \"medicine\": 0, \"tools\": 0, \"currency\": 0}\n",
    "        self.territory = random.uniform(0.5, 1)  # Increase base territory richness\n",
    "        self.cultural_practices = random.uniform(0.5, 1)  # More shared cultural practices\n",
    "        self.language = leader.language  # Shared language within the group\n",
    "        self.trade_networks = 0  # Track trade relationships with other groups\n",
    "        self.governance_structure = leader.leadership_style  # Initial governance style\n",
    "        self.laws = {}  # Simple legal system\n",
    "        self.education_level = leader.intelligence * 0.4  # Education system influenced by leader's intelligence\n",
    "        self.ethical_standards = leader.ethics * 0.4  # Group's ethical standards influenced by leader's ethics\n",
    "        self.space_technology = 0  # Track space exploration capability\n",
    "        self.automation_level = 0  # Level of automation in the group\n",
    "        self.ai_influence = 0  # Influence of AI in governance and decision-making\n",
    "        self.corporate_influence = 0  # Influence of corporations on the group\n",
    "        self.quantum_technology = 0  # Track quantum technology development\n",
    "        self.memes = {}  # Track cultural memes\n",
    "        self.parallel_reality_links = 0  # Number of connections to parallel realities\n",
    "        self.hive_mind_level = 0  # Degree of hive mind integration within the group\n",
    "        self.virtual_presence = 0  # Extent to which the group exists within a simulated reality\n",
    "        self.reality_bending = 0  # Ability to manipulate reality\n",
    "        self.noosphere_integration = 0  # Degree of integration into the global noosphere\n",
    "        self.zero_point_energy = 0  # Mastery of zero-point energy technologies\n",
    "        self.dimensions_explored = 0  # Number of higher dimensions explored\n",
    "        self.galactic_influence = 0  # Influence in the galaxy\n",
    "\n",
    "    def add_member(self, individual):\n",
    "        individual.group = self\n",
    "        self.members.append(individual)\n",
    "\n",
    "    def manage_resources(self):\n",
    "        # Allocate resources among members\n",
    "        for resource in self.resources:\n",
    "            if self.resources[resource] > 0:\n",
    "                allocation = self.resources[resource] / len(self.members)\n",
    "                for member in self.members:\n",
    "                    if resource == \"food\":\n",
    "                        member.health += allocation * 0.05  # Slower resource effect\n",
    "                    elif resource == \"water\":\n",
    "                        member.health += allocation * 0.05\n",
    "                    elif resource == \"shelter\":\n",
    "                        member.health += allocation * 0.02\n",
    "                    elif resource == \"medicine\":\n",
    "                        member.health += allocation * 0.1\n",
    "                    elif resource == \"tools\":\n",
    "                        member.technology_level += allocation * 0.05\n",
    "                    elif resource == \"currency\":\n",
    "                        member.wealth += allocation * 0.05\n",
    "\n",
    "    def trade_with_group(self, other_group):\n",
    "        # Trade resources between groups based on their trade networks and resources\n",
    "        if self.trade_networks > 0 and other_group.trade_networks > 0:\n",
    "            trade_amount = min(self.resources[\"tools\"], other_group.resources[\"medicine\"])\n",
    "            self.resources[\"tools\"] -= trade_amount\n",
    "            other_group.resources[\"medicine\"] += trade_amount\n",
    "            trade_amount = min(self.resources[\"currency\"], other_group.resources[\"food\"])\n",
    "            self.resources[\"currency\"] -= trade_amount\n",
    "            other_group.resources[\"food\"] += trade_amount\n",
    "\n",
    "    def compete_for_resources(self, other_group):\n",
    "        # Complex competition logic including technology and leadership influence\n",
    "        if (self.territory + self.leader.intelligence + self.leader.technology_level + random.uniform(-0.1, 0.1)) > \\\n",
    "           (other_group.territory + other_group.leader.intelligence + other_group.leader.technology_level):\n",
    "            self.resources[\"food\"] += other_group.resources[\"food\"] * 0.4\n",
    "            other_group.resources[\"food\"] *= 0.6\n",
    "            self.resources[\"water\"] += other_group.resources[\"water\"] * 0.4\n",
    "            other_group.resources[\"water\"] *= 0.6\n",
    "            self.resources[\"shelter\"] += other_group.resources[\"shelter\"] * 0.4\n",
    "            other_group.resources[\"shelter\"] *= 0.6\n",
    "\n",
    "    def enforce_laws(self):\n",
    "        # Enforce laws to maintain order within the group\n",
    "        if self.governance_structure in ['autocratic', 'democratic']:\n",
    "            for law, enforcement in self.laws.items():\n",
    "                for member in self.members:\n",
    "                    if random.random() > enforcement:\n",
    "                        member.mental_health -= 0.02  # Less severe punishment\n",
    "\n",
    "    def develop_space_technology(self):\n",
    "        if self.leader.technology_level > 8 and random.random() < 0.005:\n",
    "            self.space_technology += 1\n",
    "\n",
    "    def increase_automation(self):\n",
    "        if self.leader.technology_level > 7 and random.random() < 0.01:\n",
    "            self.automation_level += 1\n",
    "\n",
    "    def engage_in_diplomacy(self, other_group):\n",
    "        if random.random() < self.ethical_standards * 0.05:\n",
    "            # Form alliances or trade agreements\n",
    "            self.trade_networks += 1\n",
    "            other_group.trade_networks += 1\n",
    "\n",
    "    def engage_in_warfare(self, other_group):\n",
    "        if random.random() < self.leader.aggression * 0.05:\n",
    "            # Initiate war with another group\n",
    "            self.compete_for_resources(other_group)\n",
    "\n",
    "    def deploy_nuclear_weapons(self, other_group):\n",
    "        if random.random() < NUCLEAR_WAR_PROBABILITY:\n",
    "            # Engage in nuclear warfare\n",
    "            destruction_factor = random.uniform(0.2, 0.5)  # Reduced impact\n",
    "            self.resources[\"food\"] -= self.resources[\"food\"] * destruction_factor\n",
    "            other_group.resources[\"food\"] -= other_group.resources[\"food\"] * destruction_factor\n",
    "            self.resources[\"water\"] -= self.resources[\"water\"] * destruction_factor\n",
    "            other_group.resources[\"water\"] -= other_group.resources[\"water\"] * destruction_factor\n",
    "            self.resources[\"shelter\"] -= self.resources[\"shelter\"] * destruction_factor\n",
    "            other_group.resources[\"shelter\"] -= other_group.resources[\"shelter\"] * destruction_factor\n",
    "\n",
    "    def establish_ai_governance(self):\n",
    "        if self.leader.technology_level >= AI_GOVERNANCE_THRESHOLD:\n",
    "            self.governance_structure = 'AI-driven'\n",
    "            self.ai_influence += 1\n",
    "\n",
    "    def develop_quantum_technology(self):\n",
    "        if self.leader.technology_level > 9 and random.random() < 0.01:\n",
    "            self.quantum_technology += 1\n",
    "\n",
    "    def corporate_takeover(self):\n",
    "        if random.random() < 0.02:\n",
    "            self.corporate_influence += 1\n",
    "            self.governance_structure = 'Corporate'\n",
    "\n",
    "    def spread_memes(self, meme):\n",
    "        # Spread a cultural meme within the group\n",
    "        if meme not in self.memes:\n",
    "            self.memes[meme] = 1\n",
    "        else:\n",
    "            self.memes[meme] += 1\n",
    "        for member in self.members:\n",
    "            if random.random() < 0.05:\n",
    "                member.tradition += 0.005  # Slower cultural change\n",
    "\n",
    "    def link_parallel_realities(self):\n",
    "        if random.random() < 0.005:\n",
    "            self.parallel_reality_links += 1\n",
    "\n",
    "    def form_hive_mind(self):\n",
    "        if random.random() < 0.02:\n",
    "            self.hive_mind_level += 1\n",
    "\n",
    "    def enter_virtual_reality(self):\n",
    "        if random.random() < 0.05:\n",
    "            self.virtual_presence += 1\n",
    "\n",
    "    def bend_reality(self):\n",
    "        if random.random() < 0.002:\n",
    "            self.reality_bending += 1\n",
    "\n",
    "    def integrate_noosphere(self):\n",
    "        if random.random() < 0.02:\n",
    "            self.noosphere_integration += 1\n",
    "\n",
    "    def master_zero_point_energy(self):\n",
    "        if random.random() < 0.01:\n",
    "            self.zero_point_energy += 1\n",
    "\n",
    "    def explore_dimension(self):\n",
    "        if self.leader.technology_level > 15 and random.random() < 0.005:\n",
    "            self.dimensions_explored += 1\n",
    "\n",
    "    def gain_galactic_influence(self):\n",
    "        if self.leader.technology_level > 20 and random.random() < 0.002:\n",
    "            self.galactic_influence += 1\n",
    "\n",
    "# Define the Population class with gradual progression\n",
    "class Population:\n",
    "    def __init__(self, size):\n",
    "        self.individuals = [self.create_random_individual() for _ in range(size)]\n",
    "        self.groups = []\n",
    "        self.food = INITIAL_FOOD\n",
    "        self.water = INITIAL_WATER\n",
    "        self.shelter = INITIAL_SHELTER\n",
    "        self.medicine = MEDICINE_REGENERATION\n",
    "        self.tools = TOOLS_REGENERATION\n",
    "        self.currency = 5000  # Initial currency for economic systems\n",
    "        self.season = \"Spring\"\n",
    "        self.technology_progress = 0\n",
    "        self.climate_trend = 0  # Track long-term climate trends\n",
    "        self.climate_cycle = 0  # Track the climate cycle\n",
    "    \n",
    "    def create_random_individual(self):\n",
    "        return Individual(\n",
    "            aggression=random.uniform(0, 1),\n",
    "            peacefulness=random.uniform(0, 1),\n",
    "            birth_rate=random.uniform(0.5, 1),  # Increased base birth rate\n",
    "            intelligence=random.uniform(0.5, 1),  # Increased base intelligence\n",
    "            social_cohesion=random.uniform(0.5, 1),  # Increased base social cohesion\n",
    "            health=random.uniform(0.5, 1),  # Increased base health\n",
    "            wealth=random.uniform(0, 1),\n",
    "            cooperation=random.uniform(0.5, 1),  # Increased base cooperation\n",
    "            curiosity=random.uniform(0, 1),\n",
    "            technology_level=random.uniform(0.5, 1),  # Increased base technology level\n",
    "            environmental_adaptation=random.uniform(0.5, 1),  # Increased base environmental adaptation\n",
    "            risk_taking=random.uniform(0, 1),\n",
    "            tradition=random.uniform(0, 1),\n",
    "            religion=random.uniform(0, 1),\n",
    "            leadership_style=random.choice(['autocratic', 'democratic', 'tribal']),\n",
    "            emotional_intelligence=random.uniform(0.5, 1),  # Increased base emotional intelligence\n",
    "            mental_health=random.uniform(0.5, 1),  # Increased base mental health\n",
    "            ethics=random.uniform(0.5, 1),  # Added ethics trait\n",
    "            communication=random.uniform(0.5, 1)  # Added communication trait\n",
    "        )\n",
    "    \n",
    "    def form_group(self, leader):\n",
    "        group = Group(leader)\n",
    "        self.groups.append(group)\n",
    "        return group\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        self.individuals = [ind for ind in self.individuals if not ind.age_one_year()]\n",
    "\n",
    "        # Environmental effects: seasonal, climate variability, and long-term trends\n",
    "        self.update_season()\n",
    "        seasonal_factor = self.get_seasonal_factor()\n",
    "        climate_factor = 1 + random.uniform(-CLIMATE_VARIABILITY, CLIMATE_VARIABILITY) + self.climate_trend\n",
    "        self.food *= seasonal_factor * climate_factor\n",
    "        self.water *= seasonal_factor * climate_factor\n",
    "        self.shelter *= seasonal_factor * climate_factor\n",
    "        self.medicine *= seasonal_factor * climate_factor\n",
    "        self.tools *= seasonal_factor * climate_factor\n",
    "        self.currency *= seasonal_factor * climate_factor\n",
    "\n",
    "        # Long-term climate change\n",
    "        self.climate_trend += LONG_TERM_CLIMATE_CHANGE * random.choice([-1, 1])\n",
    "\n",
    "        # Climate cycles (e.g., ice ages)\n",
    "        self.climate_cycle += 1\n",
    "        if self.climate_cycle >= CLIMATE_CYCLE_LENGTH:\n",
    "            self.climate_cycle = 0\n",
    "            self.climate_trend *= -1  # Reverse the climate trend\n",
    "\n",
    "        # Catastrophic events\n",
    "        if random.random() < CATASTROPHIC_EVENT_PROBABILITY:\n",
    "            catastrophic_impact = random.uniform(0.2, 0.5)  # Reduced impact of catastrophic events\n",
    "            self.food -= self.food * catastrophic_impact\n",
    "            self.water -= self.water * catastrophic_impact\n",
    "            self.shelter -= self.shelter * catastrophic_impact\n",
    "            self.medicine -= self.medicine * catastrophic_impact\n",
    "            self.tools -= self.tools * catastrophic_impact\n",
    "            self.currency -= self.currency * catastrophic_impact\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > catastrophic_impact]\n",
    "\n",
    "        # Disease outbreak\n",
    "        if random.random() < DISEASE_SPREAD_PROBABILITY:\n",
    "            disease_severity = random.uniform(0.05, 0.2)  # Reduced severity of diseases\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() > disease_severity * (1 - ind.immune_system)]\n",
    "\n",
    "        # Resource allocation and consumption\n",
    "        total_resource_demand = sum((ind.health * 1.5 + ind.technology_level * 0.1) for ind in self.individuals)\n",
    "        total_food_demand = total_resource_demand * 0.3\n",
    "        total_water_demand = total_resource_demand * 0.3\n",
    "        total_shelter_demand = total_resource_demand * 0.2\n",
    "        total_medicine_demand = total_resource_demand * 0.1\n",
    "        total_tools_demand = total_resource_demand * 0.1\n",
    "        total_currency_demand = total_resource_demand * 0.05  # Demand for currency in trade\n",
    "\n",
    "        # Check resource limits\n",
    "        if (total_food_demand > self.food or total_water_demand > self.water or total_shelter_demand > self.shelter or \n",
    "            total_medicine_demand > self.medicine or total_tools_demand > self.tools or total_currency_demand > self.currency):\n",
    "            survival_chance = min(self.food / total_food_demand, self.water / total_water_demand, self.shelter / total_shelter_demand,\n",
    "                                  self.medicine / total_medicine_demand, self.tools / total_tools_demand, self.currency / total_currency_demand)\n",
    "            self.individuals = [ind for ind in self.individuals if random.random() < survival_chance]\n",
    "        \n",
    "        # Social dynamics: Group formation, migration, trade, competition, and governance\n",
    "        for ind in self.individuals:\n",
    "            if ind.group is None:\n",
    "                leader = ind if ind.intelligence > random.uniform(0, 1) else random.choice(self.individuals)\n",
    "                group = self.form_group(leader)\n",
    "                group.add_member(ind)\n",
    "            elif random.random() < ind.curiosity * 0.05:\n",
    "                # Migration to a new group\n",
    "                new_group = self.form_group(ind)\n",
    "                new_group.add_member(ind)\n",
    "        \n",
    "        # Group management\n",
    "        for group in self.groups:\n",
    "            group.manage_resources()\n",
    "            group.enforce_laws()\n",
    "            group.develop_space_technology()\n",
    "            group.increase_automation()\n",
    "            group.establish_ai_governance()\n",
    "            group.develop_quantum_technology()\n",
    "            group.corporate_takeover()\n",
    "            group.link_parallel_realities()\n",
    "            group.form_hive_mind()\n",
    "            group.enter_virtual_reality()\n",
    "            group.bend_reality()\n",
    "            group.integrate_noosphere()\n",
    "            group.master_zero_point_energy()\n",
    "            group.explore_dimension()\n",
    "            group.gain_galactic_influence()\n",
    "\n",
    "            # Spread memes in the group\n",
    "            if random.random() < 0.05:\n",
    "                meme = f\"Meme-{random.randint(1, 100)}\"\n",
    "                group.spread_memes(meme)\n",
    "\n",
    "            if random.random() < 0.05:  # Random chance of competition\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.compete_for_resources(other_group)\n",
    "\n",
    "            if random.random() < 0.03:  # Random chance of trade\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.trade_with_group(other_group)\n",
    "\n",
    "            if random.random() < 0.03:  # Random chance of diplomacy\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_diplomacy(other_group)\n",
    "\n",
    "            if random.random() < 0.03:  # Random chance of warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.engage_in_warfare(other_group)\n",
    "\n",
    "            if random.random() < NUCLEAR_WAR_PROBABILITY:  # Random chance of nuclear warfare\n",
    "                other_group = random.choice(self.groups)\n",
    "                if other_group != group:\n",
    "                    group.deploy_nuclear_weapons(other_group)\n",
    "\n",
    "        # Technology development and adaptation\n",
    "        for ind in self.individuals:\n",
    "            if random.random() < ind.intelligence * 0.01:  # Slower technology progression\n",
    "                ind.technology_level += TECH_LEVEL_INCREMENT\n",
    "            if random.random() < ind.cooperation * 0.05:\n",
    "                self.technology_progress += TECH_LEVEL_INCREMENT\n",
    "\n",
    "            # Environmental adaptation\n",
    "            if self.climate_trend > 0:\n",
    "                ind.health *= (1 + ind.environmental_adaptation * 0.05)\n",
    "            elif self.climate_trend < 0:\n",
    "                ind.health *= (1 - ind.environmental_adaptation * 0.05)\n",
    "        \n",
    "        # Birth events influenced by technology, medicine, adaptation, and cultural traits\n",
    "        new_births = []\n",
    "        for ind in self.individuals:\n",
    "            if ind.is_fertile():\n",
    "                birth_probability = ind.birth_rate * ind.health * (1 + ind.wealth) * (self.food / (CARRYING_CAPACITY * 2)) * (1 + ind.technology_level * 0.01)\n",
    "                if random.random() < birth_probability:\n",
    "                    child = self.create_random_individual()\n",
    "                    child.health = (ind.health + random.uniform(-0.02, 0.02)) / 2\n",
    "                    child.intelligence = (ind.intelligence + random.uniform(-0.02, 0.02)) / 2\n",
    "                    child.language = ind.language  # Inherit language\n",
    "                    child.immune_system = (ind.immune_system + random.uniform(-0.02, 0.02)) / 2  # Inherit immune system\n",
    "                    new_births.append(child)\n",
    "        \n",
    "        self.individuals.extend(new_births)\n",
    "        \n",
    "        # Mutations\n",
    "        for ind in self.individuals:\n",
    "            ind.mutate()\n",
    "\n",
    "        # Resource regeneration\n",
    "        self.food = min(INITIAL_FOOD, self.food + FOOD_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.water = min(INITIAL_WATER, self.water + WATER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.shelter = min(INITIAL_SHELTER, self.shelter + SHELTER_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.medicine = min(MEDICINE_REGENERATION, self.medicine + MEDICINE_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.tools = min(TOOLS_REGENERATION, self.tools + TOOLS_REGENERATION * seasonal_factor * climate_factor)\n",
    "        self.currency = min(5000, self.currency + CURRENCY_REGENERATION * seasonal_factor * climate_factor)\n",
    "\n",
    "    def update_season(self):\n",
    "        if self.season == \"Spring\":\n",
    "            self.season = \"Summer\"\n",
    "        elif self.season == \"Summer\":\n",
    "            self.season = \"Autumn\"\n",
    "        elif self.season == \"Autumn\":\n",
    "            self.season = \"Winter\"\n",
    "        elif self.season == \"Winter\":\n",
    "            self.season = \"Spring\"\n",
    "    \n",
    "    def get_seasonal_factor(self):\n",
    "        if self.season == \"Spring\":\n",
    "            return 1.1\n",
    "        elif self.season == \"Summer\":\n",
    "            return 1.0\n",
    "        elif self.season == \"Autumn\":\n",
    "            return 0.9\n",
    "        elif self.season == \"Winter\":\n",
    "            return 0.8\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        population_sizes = []\n",
    "        technology_levels = []\n",
    "        climate_trends = []\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            population_sizes.append(len(self.individuals))\n",
    "            technology_levels.append(self.technology_progress / len(self.individuals) if len(self.individuals) > 0 else 0)\n",
    "            climate_trends.append(self.climate_trend)\n",
    "        \n",
    "        return population_sizes, technology_levels, climate_trends\n",
    "\n",
    "# Run the gradual progression simulation\n",
    "population = Population(100)\n",
    "population_sizes, technology_levels, climate_trends = population.simulate_generations(300)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "161ebdf9",
   "metadata": {},
   "source": [
    "# RL Agent Based Population Simulators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd6ca722",
   "metadata": {},
   "source": [
    "## RL Model 1 (Simple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f3cf5c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "\n",
    "class RLEnvironment:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.state = self.get_state()\n",
    "\n",
    "    def get_state(self):\n",
    "        # Simplified state representation with normalization and explicit integer conversion\n",
    "        return [\n",
    "            int(min(99, len(self.population.individuals) // 10)),  # Normalize and convert to integer\n",
    "            int(min(99, self.population.food // 1000)),  # Normalize and convert to integer\n",
    "            int(min(99, self.population.water // 1000)),  # Normalize and convert to integer\n",
    "            int(min(99, int(self.population.technology_progress * 10)))  # Normalize and convert to integer\n",
    "        ]\n",
    "    \n",
    "    def take_action(self, action):\n",
    "        if action == 0:  # Increase food allocation\n",
    "            self.population.food += 500\n",
    "        elif action == 1:  # Increase water allocation\n",
    "            self.population.water += 500\n",
    "        elif action == 2:  # Focus on technology\n",
    "            self.population.technology_progress += 0.1\n",
    "\n",
    "        self.population.simulate_year()  # Advance the simulation by one year\n",
    "        reward = self.calculate_reward()\n",
    "        self.state = self.get_state()\n",
    "        return self.state, reward\n",
    "    \n",
    "    def calculate_reward(self):\n",
    "        current_population = len(self.population.individuals)\n",
    "        if 500 < current_population < 1500:\n",
    "            return 1  # Reward for maintaining a stable population\n",
    "        else:\n",
    "            return -1  # Penalty for population decline or overgrowth\n",
    "\n",
    "class RLAgent:\n",
    "    def __init__(self, action_space_size):\n",
    "        self.q_table = np.zeros((100, 100, 100, 100, action_space_size))  # Simplified Q-table\n",
    "        self.action_space_size = action_space_size\n",
    "        self.alpha = 0.1  # Learning rate\n",
    "        self.gamma = 0.9  # Discount factor\n",
    "        self.epsilon = 0.1  # Exploration rate\n",
    "    \n",
    "    def choose_action(self, state):\n",
    "        if random.random() < self.epsilon:\n",
    "            return random.randint(0, self.action_space_size - 1)  # Explore\n",
    "        else:\n",
    "            return np.argmax(self.q_table[state[0], state[1], state[2], state[3]])  # Exploit\n",
    "\n",
    "    def update_q_table(self, state, action, reward, next_state):\n",
    "        best_next_action = np.argmax(self.q_table[next_state[0], next_state[1], next_state[2], next_state[3]])\n",
    "        td_target = reward + self.gamma * self.q_table[next_state[0], next_state[1], next_state[2], next_state[3], best_next_action]\n",
    "        td_error = td_target - self.q_table[state[0], state[1], state[2], state[3], action]\n",
    "        self.q_table[state[0], state[1], state[2], state[3], action] += self.alpha * td_error\n",
    "\n",
    "# Initialize population and RL components\n",
    "population = Population(100)\n",
    "env = RLEnvironment(population)\n",
    "agent = RLAgent(action_space_size=3)\n",
    "\n",
    "# Train the RL agent over many generations\n",
    "for episode in range(10):\n",
    "    state = env.get_state()\n",
    "    for _ in range(10):  # Simulate up to 100 years in each episode\n",
    "        action = agent.choose_action(state)\n",
    "        next_state, reward = env.take_action(action)\n",
    "        agent.update_q_table(state, action, reward, next_state)\n",
    "        state = next_state\n",
    "\n",
    "# Test the learned policy\n",
    "population_sizes, technology_levels, climate_trends = env.population.simulate_generations(10)\n",
    "\n",
    "# Plotting the results to see the outcomes\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffeaa5b4",
   "metadata": {},
   "source": [
    "## RL Model 2 (Human Civilization Like Thinking)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfcf5b5d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "\n",
    "class RLEnvironment:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.state = self.get_state()\n",
    "\n",
    "    def get_state(self):\n",
    "        # State includes normalized population size, resources, and technology level\n",
    "        return [\n",
    "            int(min(99, len(self.population.individuals) // 10)),\n",
    "            int(min(99, self.population.food // 1000)),\n",
    "            int(min(99, self.population.water // 1000)),\n",
    "            int(min(99, int(self.population.technology_progress * 10)))\n",
    "        ]\n",
    "    \n",
    "    def take_action(self, action):\n",
    "        # Human-like actions: resource management, technology focus, and social policies\n",
    "        if action == 0:  # Balance resources\n",
    "            self.population.food += 300\n",
    "            self.population.water += 300\n",
    "            self.population.technology_progress += 0.05\n",
    "        elif action == 1:  # Focus on technology\n",
    "            self.population.technology_progress += 0.1\n",
    "        elif action == 2:  # Focus on social stability (influencing social cohesion and health)\n",
    "            for ind in self.population.individuals:\n",
    "                ind.social_cohesion += 0.02\n",
    "                ind.health += 0.02\n",
    "\n",
    "        self.population.simulate_year()  # Advance the simulation by one year\n",
    "        reward = self.calculate_reward()\n",
    "        self.state = self.get_state()\n",
    "        return self.state, reward\n",
    "    \n",
    "    def calculate_reward(self):\n",
    "        current_population = len(self.population.individuals)\n",
    "        tech_progress = self.population.technology_progress\n",
    "        avg_health = np.mean([ind.health for ind in self.population.individuals])\n",
    "        avg_social_cohesion = np.mean([ind.social_cohesion for ind in self.population.individuals])\n",
    "\n",
    "        # Reward stable population, technological progress, and social cohesion\n",
    "        reward = 0\n",
    "        if 500 < current_population < 1500:\n",
    "            reward += 1\n",
    "        else:\n",
    "            reward -= 1\n",
    "\n",
    "        if tech_progress > 2:\n",
    "            reward += 0.5\n",
    "\n",
    "        if avg_health > 0.7:\n",
    "            reward += 0.5\n",
    "\n",
    "        if avg_social_cohesion > 0.7:\n",
    "            reward += 0.5\n",
    "\n",
    "        # Penalize unethical or unsustainable actions (e.g., overusing resources)\n",
    "        if self.population.food < 500 or self.population.water < 500:\n",
    "            reward -= 1\n",
    "\n",
    "        return reward\n",
    "\n",
    "class RLAgent:\n",
    "    def __init__(self, action_space_size):\n",
    "        self.q_table = np.zeros((100, 100, 100, 100, action_space_size))\n",
    "        self.action_space_size = action_space_size\n",
    "        self.alpha = 0.1  # Learning rate\n",
    "        self.gamma = 0.9  # Discount factor\n",
    "        self.epsilon = 0.1  # Exploration rate\n",
    "    \n",
    "    def choose_action(self, state):\n",
    "        if random.random() < self.epsilon:\n",
    "            return random.randint(0, self.action_space_size - 1)  # Explore\n",
    "        else:\n",
    "            return np.argmax(self.q_table[state[0], state[1], state[2], state[3]])  # Exploit\n",
    "\n",
    "    def update_q_table(self, state, action, reward, next_state):\n",
    "        best_next_action = np.argmax(self.q_table[next_state[0], next_state[1], next_state[2], next_state[3]])\n",
    "        td_target = reward + self.gamma * self.q_table[next_state[0], next_state[1], next_state[2], next_state[3], best_next_action]\n",
    "        td_error = td_target - self.q_table[state[0], state[1], state[2], state[3], action]\n",
    "        self.q_table[state[0], state[1], state[2], state[3], action] += self.alpha * td_error\n",
    "\n",
    "# Initialize population and RL components\n",
    "population = Population(100)\n",
    "env = RLEnvironment(population)\n",
    "agent = RLAgent(action_space_size=3)\n",
    "\n",
    "# Train the RL agent over many generations\n",
    "for episode in range(10):\n",
    "    state = env.get_state()\n",
    "    for _ in range(5):  # Simulate up to 100 years in each episode\n",
    "        action = agent.choose_action(state)\n",
    "        next_state, reward = env.take_action(action)\n",
    "        agent.update_q_table(state, action, reward, next_state)\n",
    "        state = next_state\n",
    "\n",
    "# Test the learned policy\n",
    "population_sizes, technology_levels, climate_trends = env.population.simulate_generations(100)\n",
    "\n",
    "# Plotting the results to see the outcomes\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e21e019",
   "metadata": {},
   "source": [
    "## RL Model 3 (Growth Focussed Highly Complex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "db37a5ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from collections import deque\n",
    "\n",
    "# Define the neural network for the DQN\n",
    "class DQNetwork(nn.Module):\n",
    "    def __init__(self, state_size, action_size):\n",
    "        super(DQNetwork, self).__init__()\n",
    "        self.fc1 = nn.Linear(state_size, 128)\n",
    "        self.fc2 = nn.Linear(128, 128)\n",
    "        self.fc3 = nn.Linear(128, action_size)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        x = torch.relu(self.fc2(x))\n",
    "        return self.fc3(x)\n",
    "\n",
    "# RL Agent using DQN\n",
    "class DQNAgent:\n",
    "    def __init__(self, state_size, action_size):\n",
    "        self.state_size = state_size\n",
    "        self.action_size = action_size\n",
    "        self.memory = deque(maxlen=2000)\n",
    "        self.gamma = 0.95  # Discount rate\n",
    "        self.epsilon = 1.0  # Exploration rate\n",
    "        self.epsilon_min = 0.01\n",
    "        self.epsilon_decay = 0.995\n",
    "        self.learning_rate = 0.001\n",
    "        self.model = DQNetwork(state_size, action_size)\n",
    "        self.target_model = DQNetwork(state_size, action_size)\n",
    "        self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n",
    "        self.update_target_model()\n",
    "\n",
    "    def update_target_model(self):\n",
    "        self.target_model.load_state_dict(self.model.state_dict())\n",
    "\n",
    "    def remember(self, state, action, reward, next_state, done):\n",
    "        self.memory.append((state, action, reward, next_state, done))\n",
    "\n",
    "    def act(self, state):\n",
    "        if np.random.rand() <= self.epsilon:\n",
    "            return random.randrange(self.action_size)\n",
    "        state = torch.FloatTensor(state).unsqueeze(0)\n",
    "        with torch.no_grad():\n",
    "            act_values = self.model(state)\n",
    "        return torch.argmax(act_values[0]).item()\n",
    "\n",
    "    def replay(self, batch_size):\n",
    "        minibatch = random.sample(self.memory, batch_size)\n",
    "        for state, action, reward, next_state, done in minibatch:\n",
    "            state = torch.FloatTensor(state).unsqueeze(0)\n",
    "            next_state = torch.FloatTensor(next_state).unsqueeze(0)\n",
    "            target = reward\n",
    "            if not done:\n",
    "                target = reward + self.gamma * torch.max(self.target_model(next_state)[0]).item()\n",
    "            target_f = self.model(state)\n",
    "            target_f[0][action] = target\n",
    "            self.optimizer.zero_grad()\n",
    "            loss = nn.MSELoss()(self.model(state), target_f)\n",
    "            loss.backward()\n",
    "            self.optimizer.step()\n",
    "        if self.epsilon > self.epsilon_min:\n",
    "            self.epsilon *= self.epsilon_decay\n",
    "\n",
    "# Expanding the RLEnvironment class\n",
    "class RLEnvironment:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.state = self.get_state()\n",
    "\n",
    "    def get_state(self):\n",
    "        return [\n",
    "            len(self.population.individuals),  # Population size\n",
    "            self.population.food,\n",
    "            self.population.water,\n",
    "            self.population.shelter,\n",
    "            self.population.medicine,\n",
    "            self.population.tools,\n",
    "            self.population.currency,\n",
    "            self.population.technology_progress,\n",
    "            self.population.climate_trend,\n",
    "            self.calculate_social_cohesion(),\n",
    "            self.calculate_average_health()\n",
    "        ]\n",
    "    \n",
    "    def calculate_social_cohesion(self):\n",
    "        return np.mean([ind.social_cohesion for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_average_health(self):\n",
    "        return np.mean([ind.health for ind in self.population.individuals])\n",
    "\n",
    "    def step(self, action):\n",
    "        # Apply an action to the environment (e.g., adjust resource allocation, focus on technology)\n",
    "        if action == 0:  # Focus on food production\n",
    "            self.population.food += 500\n",
    "        elif action == 1:  # Focus on water management\n",
    "            self.population.water += 500\n",
    "        elif action == 2:  # Focus on shelter construction\n",
    "            self.population.shelter += 400\n",
    "        elif action == 3:  # Focus on medicine production\n",
    "            self.population.medicine += 300\n",
    "        elif action == 4:  # Focus on tools and technology\n",
    "            self.population.tools += 200\n",
    "            self.population.technology_progress += 0.1\n",
    "        elif action == 5:  # Engage in trade\n",
    "            self.population.currency += 300\n",
    "\n",
    "        # Simulate the effects of the action\n",
    "        self.population.simulate_year()\n",
    "        next_state = self.get_state()\n",
    "        reward = self.calculate_reward()\n",
    "        done = len(self.population.individuals) == 0\n",
    "        return next_state, reward, done\n",
    "    \n",
    "    def calculate_reward(self):\n",
    "        population_size = len(self.population.individuals)\n",
    "        tech_progress = self.population.technology_progress\n",
    "        social_cohesion = self.calculate_social_cohesion()\n",
    "        avg_health = self.calculate_average_health()\n",
    "        reward = 0\n",
    "\n",
    "        # Reward for maintaining a stable population size\n",
    "        if 500 < population_size < 1500:\n",
    "            reward += 1\n",
    "        else:\n",
    "            reward -= 1\n",
    "\n",
    "        # Reward for technological advancement\n",
    "        if tech_progress > 2:\n",
    "            reward += 0.5\n",
    "\n",
    "        # Reward for high social cohesion and health\n",
    "        if social_cohesion > 0.7:\n",
    "            reward += 0.5\n",
    "        if avg_health > 0.7:\n",
    "            reward += 0.5\n",
    "\n",
    "        # Penalize for low resources\n",
    "        if self.population.food < 500 or self.population.water < 500:\n",
    "            reward -= 1\n",
    "\n",
    "        return reward\n",
    "\n",
    "# Initialize the population and RL environment\n",
    "population = Population(100)\n",
    "env = RLEnvironment(population)\n",
    "state_size = len(env.get_state())\n",
    "action_size = 6\n",
    "agent = DQNAgent(state_size, action_size)\n",
    "\n",
    "# Train the agent\n",
    "episodes = 1000\n",
    "batch_size = 32\n",
    "\n",
    "for e in range(episodes):\n",
    "    state = env.get_state()\n",
    "    done = False\n",
    "    while not done:\n",
    "        action = agent.act(state)\n",
    "        next_state, reward, done = env.step(action)\n",
    "        agent.remember(state, action, reward, next_state, done)\n",
    "        state = next_state\n",
    "        if len(agent.memory) > batch_size:\n",
    "            agent.replay(batch_size)\n",
    "    agent.update_target_model()\n",
    "\n",
    "# Test the learned policy\n",
    "population_sizes, technology_levels, climate_trends = env.population.simulate_generations(500)\n",
    "\n",
    "# Plotting the results to see the outcomes\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da45021f",
   "metadata": {},
   "source": [
    "## RL Model 4 (Exactly Mimicks Human Behaviour Based on a SOTA PPO Algorithm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36936bc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.distributions import Categorical\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the neural network for the PPO\n",
    "class ActorCriticNetwork(nn.Module):\n",
    "    def __init__(self, state_size, action_size):\n",
    "        super(ActorCriticNetwork, self).__init__()\n",
    "        self.fc1 = nn.Linear(state_size, 128)\n",
    "        self.fc_policy = nn.Linear(128, action_size)\n",
    "        self.fc_value = nn.Linear(128, 1)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        policy = torch.softmax(self.fc_policy(x), dim=-1)\n",
    "        value = self.fc_value(x)\n",
    "        return policy, value\n",
    "\n",
    "class PPOAgent:\n",
    "    def __init__(self, state_size, action_size):\n",
    "        self.state_size = state_size\n",
    "        self.action_size = action_size\n",
    "        self.model = ActorCriticNetwork(state_size, action_size)\n",
    "        self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)\n",
    "        self.gamma = 0.99\n",
    "        self.eps_clip = 0.2\n",
    "        self.entropy_coef = 0.01\n",
    "        self.value_loss_coef = 0.5\n",
    "        self.memory = []\n",
    "    \n",
    "    def select_action(self, state):\n",
    "        state = torch.FloatTensor(state).unsqueeze(0)\n",
    "        policy, value = self.model(state)\n",
    "        dist = Categorical(policy)\n",
    "        action = dist.sample()\n",
    "        log_prob = dist.log_prob(action)\n",
    "        return action.item(), log_prob, value\n",
    "    \n",
    "    def store_transition(self, transition):\n",
    "        self.memory.append(transition)\n",
    "    \n",
    "    def update(self):\n",
    "        states, actions, log_probs, rewards, values, next_values, dones = zip(*self.memory)\n",
    "        \n",
    "        # Convert to tensors\n",
    "        states = torch.FloatTensor(states)\n",
    "        actions = torch.LongTensor(actions).unsqueeze(1)\n",
    "        log_probs = torch.stack(log_probs)\n",
    "        rewards = torch.FloatTensor(rewards)\n",
    "        values = torch.stack(values).squeeze()\n",
    "        next_values = torch.FloatTensor(next_values)\n",
    "        dones = torch.FloatTensor(dones)\n",
    "        \n",
    "        # Compute returns and advantages\n",
    "        returns = []\n",
    "        advantages = []\n",
    "        gae = 0\n",
    "        for i in reversed(range(len(rewards))):\n",
    "            td_error = rewards[i] + self.gamma * next_values[i] * (1 - dones[i]) - values[i]\n",
    "            gae = td_error + self.gamma * 0.95 * (1 - dones[i]) * gae\n",
    "            advantages.insert(0, gae)\n",
    "            returns.insert(0, gae + values[i].item())\n",
    "        \n",
    "        returns = torch.FloatTensor(returns)\n",
    "        advantages = torch.FloatTensor(advantages)\n",
    "        \n",
    "        # Compute the ratio for PPO loss\n",
    "        policy, value = self.model(states)\n",
    "        dist = Categorical(policy)\n",
    "        new_log_probs = dist.log_prob(actions.squeeze())\n",
    "        ratios = torch.exp(new_log_probs - log_probs)\n",
    "        \n",
    "        # Compute surrogate loss\n",
    "        surr1 = ratios * advantages\n",
    "        surr2 = torch.clamp(ratios, 1 - self.eps_clip, 1 + self.eps_clip) * advantages\n",
    "        policy_loss = -torch.min(surr1, surr2).mean()\n",
    "        \n",
    "        # Value loss\n",
    "        value_loss = nn.MSELoss()(returns, value.squeeze())\n",
    "        \n",
    "        # Entropy for exploration\n",
    "        entropy_loss = dist.entropy().mean()\n",
    "        \n",
    "        # Total loss\n",
    "        loss = policy_loss + self.value_loss_coef * value_loss - self.entropy_coef * entropy_loss\n",
    "        \n",
    "        # Update the model\n",
    "        self.optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "        \n",
    "        # Clear memory\n",
    "        self.memory = []\n",
    "\n",
    "# Expanding the RLEnvironment class\n",
    "class RLEnvironment:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.state = self.get_state()\n",
    "\n",
    "    def get_state(self):\n",
    "        return [\n",
    "            len(self.population.individuals),  # Population size\n",
    "            self.population.food,\n",
    "            self.population.water,\n",
    "            self.population.shelter,\n",
    "            self.population.medicine,\n",
    "            self.population.tools,\n",
    "            self.population.currency,\n",
    "            self.population.technology_progress,\n",
    "            self.population.climate_trend,\n",
    "            self.calculate_social_cohesion(),\n",
    "            self.calculate_average_health(),\n",
    "            self.calculate_cultural_influence(),\n",
    "            self.calculate_religious_adherence()\n",
    "        ]\n",
    "    \n",
    "    def calculate_social_cohesion(self):\n",
    "        return np.mean([ind.social_cohesion for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_average_health(self):\n",
    "        return np.mean([ind.health for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_cultural_influence(self):\n",
    "        return np.mean([ind.tradition for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_religious_adherence(self):\n",
    "        return np.mean([ind.religion for ind in self.population.individuals])\n",
    "\n",
    "    def step(self, action):\n",
    "        if action == 0:  # Focus on food production\n",
    "            self.population.food += 500\n",
    "        elif action == 1:  # Focus on water management\n",
    "            self.population.water += 500\n",
    "        elif action == 2:  # Focus on shelter construction\n",
    "            self.population.shelter += 400\n",
    "        elif action == 3:  # Focus on medicine production\n",
    "            self.population.medicine += 300\n",
    "        elif action == 4:  # Focus on tools and technology\n",
    "            self.population.tools += 200\n",
    "            self.population.technology_progress += 0.1\n",
    "        elif action == 5:  # Engage in trade\n",
    "            self.population.currency += 300\n",
    "        elif action == 6:  # Promote cultural growth\n",
    "            for ind in self.population.individuals:\n",
    "                ind.tradition += 0.01\n",
    "        elif action == 7:  # Promote religious values\n",
    "            for ind in self.population.individuals:\n",
    "                ind.religion += 0.01\n",
    "\n",
    "        self.population.simulate_year()\n",
    "        next_state = self.get_state()\n",
    "        reward = self.calculate_reward()\n",
    "        done = len(self.population.individuals) == 0\n",
    "        return next_state, reward, done\n",
    "    \n",
    "    def calculate_reward(self):\n",
    "        population_size = len(self.population.individuals)\n",
    "        tech_progress = self.population.technology_progress\n",
    "        social_cohesion = self.calculate_social_cohesion()\n",
    "        avg_health = self.calculate_average_health()\n",
    "        cultural_influence = self.calculate_cultural_influence()\n",
    "        religious_adherence = self.calculate_religious_adherence()\n",
    "        reward = 0\n",
    "\n",
    "        if 500 < population_size < 1500:\n",
    "            reward += 1\n",
    "        else:\n",
    "            reward -= 1\n",
    "\n",
    "        if tech_progress > 2:\n",
    "            reward += 0.5\n",
    "\n",
    "        if social_cohesion > 0.7:\n",
    "            reward += 0.5\n",
    "        if avg_health > 0.7:\n",
    "            reward += 0.5\n",
    "\n",
    "        if cultural_influence > 0.5:\n",
    "            reward += 0.3\n",
    "        if religious_adherence > 0.5:\n",
    "            reward += 0.3\n",
    "\n",
    "        if self.population.food < 500 or self.population.water < 500:\n",
    "            reward -= 1\n",
    "\n",
    "        return reward\n",
    "\n",
    "# Initialize the population and RL environment\n",
    "population = Population(100)\n",
    "env = RLEnvironment(population)\n",
    "state_size = len(env.get_state())\n",
    "action_size = 8\n",
    "agent = PPOAgent(state_size, action_size)\n",
    "\n",
    "# Train the agent\n",
    "episodes = 1000\n",
    "\n",
    "for e in range(episodes):\n",
    "    state = env.get_state()\n",
    "    done = False\n",
    "    while not done:\n",
    "        action, log_prob, value = agent.select_action(state)\n",
    "        next_state, reward, done = env.step(action)\n",
    "        agent.store_transition((state, action, log_prob, reward, value, next_state, done))\n",
    "        state = next_state\n",
    "    \n",
    "    agent.update()\n",
    "\n",
    "# Test the learned policy\n",
    "population_sizes, technology_levels, climate_trends = env.population.simulate_generations(500)\n",
    "\n",
    "# Plotting the results to see the outcomes\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa026e81",
   "metadata": {},
   "source": [
    "## Multi Agent RL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4029ec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.distributions import Categorical\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define the neural network for the PPO\n",
    "class ActorCriticNetwork(nn.Module):\n",
    "    def __init__(self, state_size, action_size):\n",
    "        super(ActorCriticNetwork, self).__init__()\n",
    "        self.fc1 = nn.Linear(state_size, 128)\n",
    "        self.fc_policy = nn.Linear(128, action_size)\n",
    "        self.fc_value = nn.Linear(128, 1)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        policy = torch.softmax(self.fc_policy(x), dim=-1)\n",
    "        value = self.fc_value(x)\n",
    "        return policy, value\n",
    "\n",
    "class PPOAgent:\n",
    "    def __init__(self, state_size, action_size):\n",
    "        self.state_size = state_size\n",
    "        self.action_size = action_size\n",
    "        self.model = ActorCriticNetwork(state_size, action_size)\n",
    "        self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)\n",
    "        self.gamma = 0.99\n",
    "        self.eps_clip = 0.2\n",
    "        self.entropy_coef = 0.01\n",
    "        self.value_loss_coef = 0.5\n",
    "        self.memory = []\n",
    "        self.economy = None\n",
    "        self.geopolitics = None\n",
    "    \n",
    "    def select_action(self, state):\n",
    "        state = torch.FloatTensor(state).unsqueeze(0)\n",
    "        policy, value = self.model(state)\n",
    "        dist = Categorical(policy)\n",
    "        action = dist.sample()\n",
    "        log_prob = dist.log_prob(action)\n",
    "        return action.item(), log_prob, value\n",
    "    \n",
    "    def store_transition(self, transition):\n",
    "        self.memory.append(transition)\n",
    "    \n",
    "    def update(self):\n",
    "        states, actions, log_probs, rewards, values, next_values, dones = zip(*self.memory)\n",
    "        \n",
    "        # Convert to tensors\n",
    "        states = torch.FloatTensor(states)\n",
    "        actions = torch.LongTensor(actions).unsqueeze(1)\n",
    "        log_probs = torch.stack(log_probs)\n",
    "        rewards = torch.FloatTensor(rewards)\n",
    "        values = torch.stack(values).squeeze()\n",
    "        next_values = torch.FloatTensor(next_values)\n",
    "        dones = torch.FloatTensor(dones)\n",
    "        \n",
    "        # Compute returns and advantages\n",
    "        returns = []\n",
    "        advantages = []\n",
    "        gae = 0\n",
    "        for i in reversed(range(len(rewards))):\n",
    "            td_error = rewards[i] + self.gamma * next_values[i] * (1 - dones[i]) - values[i]\n",
    "            gae = td_error + self.gamma * 0.95 * (1 - dones[i]) * gae\n",
    "            advantages.insert(0, gae)\n",
    "            returns.insert(0, gae + values[i].item())\n",
    "        \n",
    "        returns = torch.FloatTensor(returns)\n",
    "        advantages = torch.FloatTensor(advantages)\n",
    "        \n",
    "        # Compute the ratio for PPO loss\n",
    "        policy, value = self.model(states)\n",
    "        dist = Categorical(policy)\n",
    "        new_log_probs = dist.log_prob(actions.squeeze())\n",
    "        ratios = torch.exp(new_log_probs - log_probs)\n",
    "        \n",
    "        # Compute surrogate loss\n",
    "        surr1 = ratios * advantages\n",
    "        surr2 = torch.clamp(ratios, 1 - self.eps_clip, 1 + self.eps_clip) * advantages\n",
    "        policy_loss = -torch.min(surr1, surr2).mean()\n",
    "        \n",
    "        # Value loss\n",
    "        value_loss = nn.MSELoss()(returns, value.squeeze())\n",
    "        \n",
    "        # Entropy for exploration\n",
    "        entropy_loss = dist.entropy().mean()\n",
    "        \n",
    "        # Total loss\n",
    "        loss = policy_loss + self.value_loss_coef * value_loss - self.entropy_coef * entropy_loss\n",
    "        \n",
    "        # Update the model\n",
    "        self.optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "        \n",
    "        # Clear memory\n",
    "        self.memory = []\n",
    "\n",
    "# Dynamic Economic Model\n",
    "class Economy:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.resource_prices = {\n",
    "            \"food\": 1.0,\n",
    "            \"water\": 1.0,\n",
    "            \"shelter\": 1.0,\n",
    "            \"medicine\": 1.0,\n",
    "            \"tools\": 1.0,\n",
    "            \"currency\": 1.0\n",
    "        }\n",
    "        self.inflation_rate = 0.02  # Basic inflation rate\n",
    "\n",
    "    def update_prices(self):\n",
    "        # Adjust prices based on supply and demand\n",
    "        for resource in self.resource_prices:\n",
    "            demand = sum(ind.health * 1.5 for ind in self.population.individuals)\n",
    "            supply = getattr(self.population, resource)\n",
    "            self.resource_prices[resource] *= 1 + (demand - supply) / max(supply, 1) * self.inflation_rate\n",
    "\n",
    "    def trade(self, resource, quantity):\n",
    "        # Perform trade and adjust the population's resources\n",
    "        cost = self.resource_prices[resource] * quantity\n",
    "        if self.population.currency >= cost:\n",
    "            self.population.currency -= cost\n",
    "            setattr(self.population, resource, getattr(self.population, resource) + quantity)\n",
    "        else:\n",
    "            quantity_affordable = self.population.currency / self.resource_prices[resource]\n",
    "            self.population.currency = 0\n",
    "            setattr(self.population, resource, getattr(self.population, resource) + quantity_affordable)\n",
    "\n",
    "    def simulate_year(self):\n",
    "        self.update_prices()\n",
    "        self.population.simulate_year()\n",
    "\n",
    "# Geopolitical Model\n",
    "class Geopolitics:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.allies = []\n",
    "        self.enemies = []\n",
    "\n",
    "    def form_alliance(self, other_agent):\n",
    "        if other_agent not in self.allies:\n",
    "            self.allies.append(other_agent)\n",
    "            other_agent.geopolitics.allies.append(self.agent)\n",
    "\n",
    "    def declare_war(self, other_agent):\n",
    "        if other_agent not in self.enemies:\n",
    "            self.enemies.append(other_agent)\n",
    "            other_agent.geopolitics.enemies.append(self.agent)\n",
    "\n",
    "    def negotiate_trade(self, other_agent, resource, quantity):\n",
    "        if other_agent in self.allies:\n",
    "            self.agent.economy.trade(resource, quantity)\n",
    "            other_agent.economy.trade(resource, -quantity)\n",
    "\n",
    "    def manage_diplomacy(self):\n",
    "        # Manage alliances and conflicts\n",
    "        for ally in self.allies:\n",
    "            if random.random() < 0.05:\n",
    "                self.negotiate_trade(ally, \"food\", 100)\n",
    "        for enemy in self.enemies:\n",
    "            if random.random() < 0.05:\n",
    "                self.declare_war(enemy)\n",
    "\n",
    "# Expanding the RLEnvironment class\n",
    "class RLEnvironment:\n",
    "    def __init__(self, population, agent):\n",
    "        self.population = population\n",
    "        self.agent = agent\n",
    "        self.state = self.get_state()\n",
    "\n",
    "    def get_state(self):\n",
    "        return [\n",
    "            len(self.population.individuals),  # Population size\n",
    "            self.population.food,\n",
    "            self.population.water,\n",
    "            self.population.shelter,\n",
    "            self.population.medicine,\n",
    "            self.population.tools,\n",
    "            self.population.currency,\n",
    "            self.population.technology_progress,\n",
    "            self.population.climate_trend,\n",
    "            self.calculate_social_cohesion(),\n",
    "            self.calculate_average_health(),\n",
    "            self.calculate_cultural_influence(),\n",
    "            self.calculate_religious_adherence(),\n",
    "            self.agent.economy.resource_prices['food'],  # Economy indicators\n",
    "            self.agent.economy.resource_prices['water'],\n",
    "            len(self.agent.geopolitics.allies),  # Geopolitical indicators\n",
    "            len(self.agent.geopolitics.enemies)\n",
    "        ]\n",
    "    \n",
    "    def calculate_social_cohesion(self):\n",
    "        return np.mean([ind.social_cohesion for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_average_health(self):\n",
    "        return np.mean([ind.health for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_cultural_influence(self):\n",
    "        return np.mean([ind.tradition for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_religious_adherence(self):\n",
    "        return np.mean([ind.religion for ind in self.population.individuals])\n",
    "\n",
    "    def step(self, action):\n",
    "        if action == 0:  # Focus on food production\n",
    "            self.population.food += 500\n",
    "        elif action == 1:  # Focus on water management\n",
    "            self.population.water += 500\n",
    "        elif action == 2:  # Focus on shelter construction\n",
    "            self.population.shelter += 400\n",
    "        elif action == 3:  # Focus on medicine production\n",
    "            self.population.medicine += 300\n",
    "        elif action == 4:  # Focus on tools and technology\n",
    "            self.population.tools += 200\n",
    "            self.population.technology_progress += 0.1\n",
    "        elif action == 5:  # Engage in trade\n",
    "            self.agent.economy.trade(\"food\", 300)\n",
    "        elif action == 6:  # Promote cultural growth\n",
    "            for ind in self.population.individuals:\n",
    "                ind.tradition += 0.01\n",
    "        elif action == 7:  # Promote religious values\n",
    "            for ind in self.population.individuals:\n",
    "                ind.religion += 0.01\n",
    "\n",
    "        self.agent.economy.simulate_year()\n",
    "        next_state = self.get_state()\n",
    "        reward = self.calculate_reward()\n",
    "        done = len(self.population.individuals) == 0\n",
    "        return next_state, reward, done\n",
    "    \n",
    "    def calculate_reward(self):\n",
    "        population_size = len(self.population.individuals)\n",
    "        tech_progress = self.population.technology_progress\n",
    "        social_cohesion = self.calculate_social_cohesion()\n",
    "        avg_health = self.calculate_average_health()\n",
    "        cultural_influence = self.calculate_cultural_influence()\n",
    "        religious_adherence = self.calculate_religious_adherence()\n",
    "        economic_stability = 1 / max(abs(self.agent.economy.inflation_rate), 1)\n",
    "        geopolitical_power = len(self.agent.geopolitics.allies) - len(self.agent.geopolitics.enemies)\n",
    "\n",
    "        reward = 0\n",
    "\n",
    "        if 500 < population_size < 1500:\n",
    "            reward += 1\n",
    "        else:\n",
    "            reward -= 1\n",
    "\n",
    "        if tech_progress > 2:\n",
    "            reward += 0.5\n",
    "\n",
    "        if social_cohesion > 0.7:\n",
    "            reward += 0.5\n",
    "        if avg_health > 0.7:\n",
    "            reward += 0.5\n",
    "\n",
    "        if cultural_influence > 0.5:\n",
    "            reward += 0.3\n",
    "        if religious_adherence > 0.5:\n",
    "            reward += 0.3\n",
    "\n",
    "        reward += economic_stability * 0.5\n",
    "        reward += geopolitical_power * 0.3\n",
    "\n",
    "        if self.population.food < 500 or self.population.water < 500:\n",
    "            reward -= 1\n",
    "\n",
    "        return reward\n",
    "\n",
    "# Multi-Agent Environment\n",
    "class MultiAgentEnvironment:\n",
    "    def __init__(self, agents):\n",
    "        self.agents = agents\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        for agent in self.agents:\n",
    "            agent.economy.simulate_year()\n",
    "            agent.geopolitics.manage_diplomacy()\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "\n",
    "# Example of initializing multiple agents\n",
    "population = Population(100)\n",
    "agents = [PPOAgent(state_size=18, action_size=8) for _ in range(3)]\n",
    "for agent in agents:\n",
    "    agent.economy = Economy(population)\n",
    "    agent.geopolitics = Geopolitics(agent)\n",
    "\n",
    "env = MultiAgentEnvironment(agents)\n",
    "env.simulate_generations(300)\n",
    "\n",
    "# Testing the learned policy\n",
    "population_sizes, technology_levels, climate_trends = env.agents[0].economy.population.simulate_generations(500)\n",
    "\n",
    "# Plotting the results to see the outcomes\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012a8675",
   "metadata": {},
   "source": [
    "## Multi Agent Simulation with Even More Realism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "239152b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.distributions import Categorical\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Define the neural network for the PPO\n",
    "class ActorCriticNetwork(nn.Module):\n",
    "    def __init__(self, state_size, action_size):\n",
    "        super(ActorCriticNetwork, self).__init__()\n",
    "        self.fc1 = nn.Linear(state_size, 128)\n",
    "        self.fc_policy = nn.Linear(128, action_size)\n",
    "        self.fc_value = nn.Linear(128, 1)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        policy = torch.softmax(self.fc_policy(x), dim=-1)\n",
    "        value = self.fc_value(x)\n",
    "        return policy, value\n",
    "\n",
    "class PPOAgent:\n",
    "    def __init__(self, state_size, action_size, device):\n",
    "        self.state_size = state_size\n",
    "        self.action_size = action_size\n",
    "        self.device = device \n",
    "        self.model = ActorCriticNetwork(state_size, action_size).to(device)\n",
    "        self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)\n",
    "        self.gamma = 0.99\n",
    "        self.eps_clip = 0.2\n",
    "        self.entropy_coef = 0.01\n",
    "        self.value_loss_coef = 0.5\n",
    "        self.memory = []\n",
    "        self.population = Population(100)\n",
    "        self.economy = Economy(self.population)\n",
    "        self.culture = Culture(self.population)\n",
    "        self.education_system = EducationSystem(self.population)\n",
    "        self.propaganda = Propaganda(self)\n",
    "        self.environment = Environment()\n",
    "        self.geopolitics = Geopolitics(self)\n",
    "        self.resources = Resources()\n",
    "        self.technology_singularity = TechnologicalSingularity(self)\n",
    "        self.alliances = Alliances(self)\n",
    "        self.global_organizations = GlobalOrganizations([self])\n",
    "        self.covert_operations = CovertOperations(self)\n",
    "        self.global_trade_network = GlobalTradeNetwork([self])\n",
    "        self.market_economy = MarketEconomy(self)\n",
    "        self.taxation = Taxation(self)\n",
    "        self.demographics = Demographics(self.population)\n",
    "        self.health_system = HealthSystem(self.population)\n",
    "        self.social_justice = SocialJustice(self)\n",
    "        self.energy_management = EnergyManagement(self)\n",
    "        self.infrastructure = Infrastructure(self)\n",
    "        self.parallel_universes = ParallelUniverses(self)\n",
    "        self.quantum_technology = QuantumTechnology(self)\n",
    "        self.autonomous_systems = AutonomousSystems(self)\n",
    "        self.transhumanism = Transhumanism(self)\n",
    "        self.narrative_engine = NarrativeEngine(self)\n",
    "        self.ethical_dilemmas = EthicalDilemmas(self)\n",
    "        self.time_travel = TimeTravel(self)\n",
    "        self.historical_simulation = HistoricalSimulation(self)\n",
    "        self.ai_governance = AIGovernance(self)\n",
    "    \n",
    "    def select_action(self, state):\n",
    "        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)\n",
    "        policy, value = self.model(state)\n",
    "        dist = Categorical(policy)\n",
    "        action = dist.sample()\n",
    "        log_prob = dist.log_prob(action)\n",
    "        return action.item(), log_prob, value\n",
    "    \n",
    "    def store_transition(self, transition):\n",
    "        self.memory.append(transition)\n",
    "    \n",
    "    def update(self):\n",
    "        states, actions, log_probs, rewards, values, next_values, dones = zip(*self.memory)\n",
    "        \n",
    "        # Convert to tensors\n",
    "        states = torch.FloatTensor(states).to(self.device)\n",
    "        actions = torch.LongTensor(actions).unsqueeze(1).to(self.device)\n",
    "        log_probs = torch.stack(log_probs).to(self.device)\n",
    "        rewards = torch.FloatTensor(rewards).to(self.device)\n",
    "        values = torch.stack(values).squeeze().to(self.device)\n",
    "        next_values = torch.FloatTensor(next_values).to(self.device)\n",
    "        dones = torch.FloatTensor(dones).to(self.device)\n",
    "        \n",
    "        # Compute returns and advantages\n",
    "        returns = []\n",
    "        advantages = []\n",
    "        gae = 0\n",
    "        for i in reversed(range(len(rewards))):\n",
    "            td_error = rewards[i] + self.gamma * next_values[i] * (1 - dones[i]) - values[i]\n",
    "            gae = td_error + self.gamma * 0.95 * (1 - dones[i]) * gae\n",
    "            advantages.insert(0, gae)\n",
    "            returns.insert(0, gae + values[i].item())\n",
    "        \n",
    "        returns = torch.FloatTensor(returns).to(self.device)\n",
    "        advantages = torch.FloatTensor(advantages).to(self.device)\n",
    "        \n",
    "        # Compute the ratio for PPO loss\n",
    "        policy, value = self.model(states)\n",
    "        dist = Categorical(policy)\n",
    "        new_log_probs = dist.log_prob(actions.squeeze())\n",
    "        ratios = torch.exp(new_log_probs - log_probs)\n",
    "        \n",
    "        # Compute surrogate loss\n",
    "        surr1 = ratios * advantages\n",
    "        surr2 = torch.clamp(ratios, 1 - self.eps_clip, 1 + self.eps_clip) * advantages\n",
    "        policy_loss = -torch.min(surr1, surr2).mean()\n",
    "        \n",
    "        # Value loss\n",
    "        value_loss = nn.MSELoss()(returns, value.squeeze())\n",
    "        \n",
    "        # Entropy for exploration\n",
    "        entropy_loss = dist.entropy().mean()\n",
    "        \n",
    "        # Total loss\n",
    "        loss = policy_loss + self.value_loss_coef * value_loss - self.entropy_coef * entropy_loss\n",
    "        \n",
    "        # Update the model\n",
    "        self.optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "        \n",
    "        # Clear memory\n",
    "        self.memory = []\n",
    "\n",
    "        \n",
    "class ProceduralWorld:\n",
    "    def __init__(self):\n",
    "        self.climate = self.generate_climate()\n",
    "        self.resources = self.generate_resources()\n",
    "        self.population_distribution = self.generate_population_distribution()\n",
    "\n",
    "    def generate_climate(self):\n",
    "        return {\n",
    "            \"temperature\": np.random.uniform(-10, 40),  # Degrees Celsius\n",
    "            \"precipitation\": np.random.uniform(0, 5000),  # mm/year\n",
    "            \"climate_variability\": np.random.uniform(0, 0.1),  # Climate variability factor\n",
    "        }\n",
    "\n",
    "    def generate_resources(self):\n",
    "        return {\n",
    "            \"food\": np.random.uniform(5000, 20000),\n",
    "            \"water\": np.random.uniform(5000, 20000),\n",
    "            \"shelter\": np.random.uniform(4000, 18000),\n",
    "            \"medicine\": np.random.uniform(3000, 15000),\n",
    "            \"tools\": np.random.uniform(3000, 15000),\n",
    "            \"energy\": np.random.uniform(2000, 10000),\n",
    "        }\n",
    "\n",
    "    def generate_population_distribution(self):\n",
    "        return {\n",
    "            \"region_1\": np.random.randint(50, 500),\n",
    "            \"region_2\": np.random.randint(50, 500),\n",
    "            \"region_3\": np.random.randint(50, 500),\n",
    "        }\n",
    "\n",
    "    def simulate_year(self):\n",
    "        self.update_climate()\n",
    "        self.update_resources()\n",
    "\n",
    "    def update_climate(self):\n",
    "        self.climate[\"temperature\"] += np.random.uniform(-0.5, 0.5)\n",
    "        self.climate[\"precipitation\"] += np.random.uniform(-50, 50)\n",
    "        self.climate[\"temperature\"] = np.clip(self.climate[\"temperature\"], -50, 50)\n",
    "        self.climate[\"precipitation\"] = np.clip(self.climate[\"precipitation\"], 0, 10000)\n",
    "\n",
    "    def update_resources(self):\n",
    "        for resource in self.resources:\n",
    "            usage_factor = np.random.uniform(0.9, 1.1)\n",
    "            regeneration_factor = np.random.uniform(0.01, 0.05)\n",
    "            self.resources[resource] = max(0, self.resources[resource] * usage_factor + regeneration_factor * self.resources[resource])\n",
    "\n",
    "# Define ScenarioWorld class\n",
    "class ScenarioWorld:\n",
    "    def __init__(self, scenario=\"default\"):\n",
    "        self.scenario = scenario\n",
    "        self.climate = self.initialize_climate()\n",
    "        self.resources = self.initialize_resources()\n",
    "        self.population_distribution = self.initialize_population_distribution()\n",
    "\n",
    "    def initialize_climate(self):\n",
    "        if self.scenario == \"ice_age\":\n",
    "            return {\"temperature\": -20, \"precipitation\": 100, \"climate_variability\": 0.05}\n",
    "        elif self.scenario == \"global_warming\":\n",
    "            return {\"temperature\": 35, \"precipitation\": 1000, \"climate_variability\": 0.08}\n",
    "        else:  # Default scenario\n",
    "            return {\"temperature\": 20, \"precipitation\": 1500, \"climate_variability\": 0.03}\n",
    "\n",
    "    def initialize_resources(self):\n",
    "        if self.scenario == \"resource_rich\":\n",
    "            return {\"food\": 20000, \"water\": 20000, \"shelter\": 18000, \"medicine\": 15000, \"tools\": 15000, \"energy\": 10000}\n",
    "        elif self.scenario == \"resource_poor\":\n",
    "            return {\"food\": 5000, \"water\": 5000, \"shelter\": 4000, \"medicine\": 3000, \"tools\": 3000, \"energy\": 2000}\n",
    "        else:  # Default scenario\n",
    "            return {\"food\": 10000, \"water\": 10000, \"shelter\": 9000, \"medicine\": 8000, \"tools\": 8000, \"energy\": 7000}\n",
    "\n",
    "    def initialize_population_distribution(self):\n",
    "        if self.scenario == \"uneven_population\":\n",
    "            return {\"region_1\": 50, \"region_2\": 450, \"region_3\": 500}\n",
    "        elif self.scenario == \"even_population\":\n",
    "            return {\"region_1\": 300, \"region_2\": 300, \"region_3\": 300}\n",
    "        else:  # Default scenario\n",
    "            return {\"region_1\": 200, \"region_2\": 200, \"region_3\": 200}\n",
    "\n",
    "    def simulate_year(self):\n",
    "        self.update_climate()\n",
    "        self.update_resources()\n",
    "\n",
    "    def update_climate(self):\n",
    "        if self.scenario == \"global_warming\":\n",
    "            self.climate[\"temperature\"] += np.random.uniform(0.2, 0.5)\n",
    "        elif self.scenario == \"ice_age\":\n",
    "            self.climate[\"temperature\"] -= np.random.uniform(0.1, 0.3)\n",
    "        else:\n",
    "            self.climate[\"temperature\"] += np.random.uniform(-0.5, 0.5)\n",
    "\n",
    "        self.climate[\"precipitation\"] += np.random.uniform(-50, 50)\n",
    "        self.climate[\"temperature\"] = np.clip(self.climate[\"temperature\"], -50, 50)\n",
    "        self.climate[\"precipitation\"] = np.clip(self.climate[\"precipitation\"], 0, 10000)\n",
    "\n",
    "    def update_resources(self):\n",
    "        for resource in self.resources:\n",
    "            if self.scenario == \"resource_rich\":\n",
    "                regeneration_factor = np.random.uniform(0.05, 0.1)\n",
    "            elif self.scenario == \"resource_poor\":\n",
    "                regeneration_factor = np.random.uniform(0.01, 0.02)\n",
    "            else:\n",
    "                regeneration_factor = np.random.uniform(0.03, 0.05)\n",
    "\n",
    "            usage_factor = np.random.uniform(0.9, 1.1)\n",
    "            self.resources[resource] = max(0, self.resources[resource] * usage_factor + regeneration_factor * self.resources[resource])        \n",
    "        \n",
    "class Culture:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.cultural_practices = np.mean([ind.tradition for ind in self.population.individuals])\n",
    "    \n",
    "    def evolve(self, immigration, technology, wars):\n",
    "        # Cultural change influenced by immigration, technology, and wars\n",
    "        cultural_change = immigration * 0.1 + technology * 0.05 - wars * 0.05\n",
    "        self.cultural_practices += cultural_change\n",
    "        self.cultural_practices = np.clip(self.cultural_practices, 0, 1)  # Bound cultural practices between 0 and 1\n",
    "    \n",
    "    def influence_or_resist_change(self, agent):\n",
    "        # Agents can influence or resist cultural change\n",
    "        if random.random() < agent.leadership_style:\n",
    "            self.cultural_practices += 0.02  # Influence cultural change\n",
    "        else:\n",
    "            self.cultural_practices -= 0.02  # Resist cultural change\n",
    "        self.cultural_practices = np.clip(self.cultural_practices, 0, 1)\n",
    "        \n",
    "class EducationSystem:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.education_level = np.mean([ind.intelligence for ind in self.population.individuals])\n",
    "    \n",
    "    def invest_in_education(self, funding):\n",
    "        # Invest in education, impacting intelligence and technology progression\n",
    "        self.education_level += funding * 0.05\n",
    "        self.education_level = np.clip(self.education_level, 0, 1)\n",
    "    \n",
    "    def influence_social_cohesion(self):\n",
    "        # Higher education levels improve social cohesion\n",
    "        social_cohesion_bonus = self.education_level * 0.1\n",
    "        for ind in self.population.individuals:\n",
    "            ind.social_cohesion += social_cohesion_bonus\n",
    "            \n",
    "class Propaganda:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "    \n",
    "    def spread_ideologies(self, target_agent):\n",
    "        # Use propaganda to influence other agents' populations\n",
    "        influence_power = self.agent.economy.resource_prices['currency'] * 0.1\n",
    "        target_agent.culture.cultural_practices += influence_power * 0.01\n",
    "        target_agent.culture.cultural_practices = np.clip(target_agent.culture.cultural_practices, 0, 1)\n",
    "\n",
    "class Environment:\n",
    "    def __init__(self):\n",
    "        self.pollution_level = 0.1\n",
    "        self.deforestation = 0.1\n",
    "    \n",
    "    def degrade_environment(self, population_size, resource_usage):\n",
    "        # Environmental degradation based on population size and resource usage\n",
    "        self.pollution_level += population_size * 0.01 + resource_usage * 0.01\n",
    "        self.deforestation += population_size * 0.005\n",
    "    \n",
    "    def restore_environment(self, restoration_efforts):\n",
    "        # Environmental restoration efforts\n",
    "        self.pollution_level -= restoration_efforts * 0.02\n",
    "        self.deforestation -= restoration_efforts * 0.01\n",
    "        self.pollution_level = max(0, self.pollution_level)\n",
    "        self.deforestation = max(0, self.deforestation)\n",
    "\n",
    "\n",
    "class Economy:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.resource_prices = {\n",
    "            \"food\": 1.0,\n",
    "            \"water\": 1.0,\n",
    "            \"shelter\": 1.0,\n",
    "            \"medicine\": 1.0,\n",
    "            \"tools\": 1.0,\n",
    "            \"currency\": 1.0\n",
    "        }\n",
    "        self.inflation_rate = 0.02  # Basic inflation rate\n",
    "\n",
    "    def update_prices(self):\n",
    "        # Adjust prices based on supply and demand\n",
    "        for resource in self.resource_prices:\n",
    "            demand = sum(ind.health * 1.5 for ind in self.population.individuals)\n",
    "            supply = getattr(self.population, resource)\n",
    "            self.resource_prices[resource] *= 1 + (demand - supply) / max(supply, 1) * self.inflation_rate\n",
    "\n",
    "    def trade(self, resource, quantity):\n",
    "        # Perform trade and adjust the population's resources\n",
    "        cost = self.resource_prices[resource] * quantity\n",
    "        if self.population.currency >= cost:\n",
    "            self.population.currency -= cost\n",
    "            setattr(self.population, resource, getattr(self.population, resource) + quantity)\n",
    "        else:\n",
    "            quantity_affordable = self.population.currency / self.resource_prices[resource]\n",
    "            self.population.currency = 0\n",
    "            setattr(self.population, resource, getattr(self.population, resource) + quantity_affordable)\n",
    "\n",
    "    def simulate_year(self):\n",
    "        self.update_prices()\n",
    "        self.population.simulate_year()\n",
    "\n",
    "# Geopolitical Model\n",
    "class Geopolitics:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.allies = []\n",
    "        self.enemies = []\n",
    "\n",
    "    def form_alliance(self, other_agent):\n",
    "        if other_agent not in self.allies:\n",
    "            self.allies.append(other_agent)\n",
    "            other_agent.geopolitics.allies.append(self.agent)\n",
    "\n",
    "    def declare_war(self, other_agent):\n",
    "        if other_agent not in self.enemies:\n",
    "            self.enemies.append(other_agent)\n",
    "            other_agent.geopolitics.enemies.append(self.agent)\n",
    "\n",
    "    def negotiate_trade(self, other_agent, resource, quantity):\n",
    "        if other_agent in self.allies:\n",
    "            self.agent.economy.trade(resource, quantity)\n",
    "            other_agent.economy.trade(resource, -quantity)\n",
    "\n",
    "    def manage_diplomacy(self):\n",
    "        # Manage alliances and conflicts\n",
    "        for ally in self.allies:\n",
    "            if random.random() < 0.05:\n",
    "                self.negotiate_trade(ally, \"food\", 100)\n",
    "        for enemy in self.enemies:\n",
    "            if random.random() < 0.05:\n",
    "                self.declare_war(enemy)\n",
    "                \n",
    "class Resources:\n",
    "    def __init__(self):\n",
    "        self.resource_levels = {\n",
    "            \"food\": 1000,\n",
    "            \"water\": 1000,\n",
    "            \"shelter\": 900,\n",
    "            \"medicine\": 800,\n",
    "            \"tools\": 800,\n",
    "            \"energy\": 700,\n",
    "        }\n",
    "    \n",
    "    def exhaust_or_renew(self, technology_level, policies):\n",
    "        # Resource exhaustion or renewal based on technology and policies\n",
    "        for resource in self.resource_levels:\n",
    "            if random.random() < 0.05:  # Chance of exhaustion\n",
    "                self.resource_levels[resource] *= 0.95\n",
    "            elif policies.get(\"renewable_\" + resource):\n",
    "                self.resource_levels[resource] += technology_level * 0.05\n",
    "            self.resource_levels[resource] = max(0, self.resource_levels[resource])\n",
    "            \n",
    "class TechnologicalSingularity:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.singularity_level = 0.0\n",
    "    \n",
    "    def approach_singularity(self, technology_level):\n",
    "        # Progress toward a technological singularity\n",
    "        if technology_level > 9:\n",
    "            self.singularity_level += 0.1\n",
    "            self.singularity_level = np.clip(self.singularity_level, 0, 1)\n",
    "    \n",
    "    def manage_consequences(self):\n",
    "        # Manage the consequences of a singularity\n",
    "        if self.singularity_level > 0.9 and random.random() < 0.2:\n",
    "            # Risk of immense power or existential threats\n",
    "            if random.random() < 0.5:\n",
    "                # Immense power: agent gains significant advantages\n",
    "                self.agent.population.food *= 2\n",
    "                self.agent.population.water *= 2\n",
    "            else:\n",
    "                # Existential threat: agent faces severe consequences\n",
    "                self.agent.population.individuals = random.sample(self.agent.population.individuals, len(self.agent.population.individuals) // 2)\n",
    "\n",
    "class Alliances:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.treaties = []\n",
    "    \n",
    "    def form_complex_alliance(self, other_agent, conditions):\n",
    "        # Form complex alliances with conditions\n",
    "        self.treaties.append({\"partner\": other_agent, \"conditions\": conditions})\n",
    "        other_agent.geopolitics.treaties.append({\"partner\": self.agent, \"conditions\": conditions})\n",
    "    \n",
    "    def enforce_treaties(self):\n",
    "        # Enforce treaties based on global events\n",
    "        for treaty in self.treaties:\n",
    "            conditions = treaty[\"conditions\"]\n",
    "            partner = treaty[\"partner\"]\n",
    "            if conditions.get(\"defensive_pact\") and random.random() < 0.1:\n",
    "                # Defend ally in case of attack\n",
    "                if partner.geopolitics.enemies:\n",
    "                    self.agent.geopolitics.declare_war(partner.geopolitics.enemies[0])\n",
    "            if conditions.get(\"trade_agreement\") and random.random() < 0.2:\n",
    "                # Enhance trade with ally\n",
    "                self.agent.economy.trade(\"tools\", 100)\n",
    "                partner.economy.trade(\"tools\", -100)\n",
    "            if conditions.get(\"scientific_collaboration\"):\n",
    "                # Collaborate on scientific research\n",
    "                self.agent.population.technology_progress += 0.1\n",
    "                partner.population.technology_progress += 0.1\n",
    "\n",
    "class GlobalOrganizations:\n",
    "    def __init__(self, agents):\n",
    "        self.agents = agents\n",
    "    \n",
    "    def vote_on_resolutions(self, resolution):\n",
    "        # Agents vote on global resolutions (e.g., climate change, pandemics)\n",
    "        votes = [random.choice([True, False]) for _ in self.agents]\n",
    "        if sum(votes) > len(self.agents) // 2:\n",
    "            self.enforce_resolution(resolution)\n",
    "    \n",
    "    def enforce_resolution(self, resolution):\n",
    "        # Enforce global resolutions\n",
    "        for agent in self.agents:\n",
    "            if resolution == \"sanctions\":\n",
    "                if random.random() < 0.3:\n",
    "                    agent.economy.resource_prices['currency'] *= 1.1  # Impact of sanctions\n",
    "            elif resolution == \"climate_action\":\n",
    "                # Force agents to invest in environmental restoration\n",
    "                agent.environment.restore_environment(100)\n",
    "\n",
    "\n",
    "class CovertOperations:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "    \n",
    "    def fund_proxy_wars(self, target_agent):\n",
    "        # Fund proxy wars to destabilize other agents\n",
    "        if self.agent.economy.resource_prices['currency'] > 100:\n",
    "            target_agent.population.individuals = random.sample(target_agent.population.individuals, len(target_agent.population.individuals) // 2)\n",
    "            self.agent.economy.resource_prices['currency'] -= 100\n",
    "    \n",
    "    def engage_in_espionage(self, target_agent):\n",
    "        # Engage in espionage to gather intelligence\n",
    "        if random.random() < 0.1:\n",
    "            self.agent.economy.trade(\"tools\", 100)  # Steal technology\n",
    "            target_agent.economy.trade(\"tools\", -100)\n",
    "            \n",
    "    def sabotage(self, target_agent):\n",
    "        # Sabotage the target agent's resources or infrastructure\n",
    "        if random.random() < 0.1:\n",
    "            target_agent.population.food *= 0.9  # Reduce food supply by 10%\n",
    "            target_agent.population.water *= 0.9  # Reduce water supply by 10%\n",
    "            \n",
    "\n",
    "class GlobalTradeNetwork:\n",
    "    def __init__(self, agents):\n",
    "        self.agents = agents\n",
    "        self.trade_routes = {}\n",
    "    \n",
    "    def establish_trade_route(self, agent_a, agent_b):\n",
    "        # Establish trade routes between agents\n",
    "        self.trade_routes[(agent_a, agent_b)] = {\"status\": \"active\"}\n",
    "    \n",
    "    def disrupt_trade_route(self, agent_a, agent_b):\n",
    "        # Disrupt trade routes due to conflict or disasters\n",
    "        if random.random() < 0.1:\n",
    "            self.trade_routes[(agent_a, agent_b)][\"status\"] = \"disrupted\"\n",
    "    \n",
    "    def trade_resources(self, agent_a, agent_b, resource, quantity):\n",
    "        # Trade resources between agents\n",
    "        if self.trade_routes[(agent_a, agent_b)][\"status\"] == \"active\":\n",
    "            agent_a.economy.trade(resource, quantity)\n",
    "            agent_b.economy.trade(resource, -quantity)\n",
    "\n",
    "class MarketEconomy:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.market_conditions = {\"boom\": False, \"bust\": False}\n",
    "    \n",
    "    def simulate_market_cycle(self):\n",
    "        # Simulate economic booms and busts\n",
    "        if random.random() < 0.1:\n",
    "            self.market_conditions[\"boom\"] = True\n",
    "            self.agent.economy.resource_prices['currency'] *= 0.9\n",
    "        elif random.random() < 0.1:\n",
    "            self.market_conditions[\"bust\"] = True\n",
    "            self.agent.economy.resource_prices['currency'] *= 1.2\n",
    "    \n",
    "    def manage_financial_crisis(self):\n",
    "        # Manage financial crises\n",
    "        if self.market_conditions[\"bust\"]:\n",
    "            self.agent.economy.resource_prices['currency'] *= 1.1  # Increase inflation during bust\n",
    "            \n",
    "class Taxation:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.tax_rate = 0.1\n",
    "    \n",
    "    def collect_taxes(self):\n",
    "        # Collect taxes based on wealth\n",
    "        tax_revenue = sum(ind.wealth * self.tax_rate for ind in self.agent.population.individuals)\n",
    "        self.agent.economy.resource_prices['currency'] += tax_revenue\n",
    "    \n",
    "    def allocate_spending(self, spending_priorities):\n",
    "        # Allocate public spending based on priorities\n",
    "        for priority in spending_priorities:\n",
    "            if priority == \"education\":\n",
    "                self.agent.education_system.invest_in_education(100)\n",
    "            elif priority == \"infrastructure\":\n",
    "                self.agent.infrastructure.invest_in_projects(100)\n",
    "                \n",
    "class Demographics:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.age_distribution = [random.randint(0, 80) for _ in range(len(self.population.individuals))]\n",
    "    \n",
    "    def simulate_aging(self):\n",
    "        # Simulate demographic changes over time\n",
    "        self.age_distribution = [age + 1 for age in self.age_distribution]\n",
    "        self.age_distribution = [age for age in self.age_distribution if age < 80]\n",
    "    \n",
    "    def manage_aging_population(self):\n",
    "        # Manage challenges like an aging workforce\n",
    "        aging_factor = len([age for age in self.age_distribution if age > 60]) / len(self.age_distribution)\n",
    "        if aging_factor > 0.3:\n",
    "            self.population.birth_rate += 0.01  # Encourage higher birth rates to offset aging\n",
    "            \n",
    "class HealthSystem:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.healthcare_quality = np.mean([ind.health for ind in self.population.individuals])\n",
    "    \n",
    "    def invest_in_healthcare(self, funding):\n",
    "        # Invest in healthcare to improve public health\n",
    "        self.healthcare_quality += funding * 0.05\n",
    "        self.healthcare_quality = np.clip(self.healthcare_quality, 0, 1)\n",
    "    \n",
    "    def manage_epidemic(self):\n",
    "        # Respond to epidemics\n",
    "        if random.random() < 0.1:\n",
    "            self.population.individuals = random.sample(self.population.individuals, len(self.population.individuals) // 2)\n",
    "\n",
    "class SocialJustice:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.human_rights_level = 0.5\n",
    "    \n",
    "    def implement_policies(self, policies):\n",
    "        # Implement policies on human rights and equality\n",
    "        if policies.get(\"human_rights\"):\n",
    "            self.human_rights_level += 0.1\n",
    "        if policies.get(\"equality\"):\n",
    "            self.human_rights_level += 0.1\n",
    "    \n",
    "    def manage_unrest(self):\n",
    "        # Manage social unrest due to poor human rights\n",
    "        if self.human_rights_level < 0.5 and random.random() < 0.2:\n",
    "            self.agent.population.individuals = random.sample(self.agent.population.individuals, len(self.agent.population.individuals) // 2)\n",
    "\n",
    "            \n",
    "class EnergyManagement:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.energy_mix = {\"fossil_fuels\": 0.7, \"nuclear\": 0.2, \"renewables\": 0.1}\n",
    "    \n",
    "    def shift_energy_policy(self, energy_type, investment):\n",
    "        # Shift energy policies and manage the energy mix\n",
    "        self.energy_mix[energy_type] += investment * 0.05\n",
    "        for other in self.energy_mix:\n",
    "            if other != energy_type:\n",
    "                self.energy_mix[other] -= investment * 0.05\n",
    "        self.energy_mix[energy_type] = np.clip(self.energy_mix[energy_type], 0, 1)\n",
    "\n",
    "class Infrastructure:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.projects = {\"transportation\": 0.5, \"communication\": 0.5, \"housing\": 0.5}\n",
    "    \n",
    "    def invest_in_projects(self, investment):\n",
    "        # Invest in infrastructure projects\n",
    "        for project in self.projects:\n",
    "            self.projects[project] += investment * 0.05\n",
    "            self.projects[project] = np.clip(self.projects[project], 0, 1)\n",
    "    \n",
    "    def influence_economic_growth(self):\n",
    "        # Influence economic growth through infrastructure development\n",
    "        for project in self.projects:\n",
    "            self.agent.economy.resource_prices['currency'] *= 1 + self.projects[project] * 0.1\n",
    "\n",
    "class ParallelUniverses:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.parallel_universe_links = 0\n",
    "    \n",
    "    def interact_with_parallel_universes(self, technology_level):\n",
    "        # Interact with parallel universes\n",
    "        if technology_level > 10:\n",
    "            self.parallel_universe_links += 1\n",
    "    \n",
    "    def manipulate_alternate_realities(self):\n",
    "        # Manipulate alternate realities for strategic advantages\n",
    "        if self.parallel_universe_links > 0 and random.random() < 0.2:\n",
    "            # Gain knowledge from alternate realities\n",
    "            self.agent.technology_level += 0.5\n",
    "            \n",
    "class QuantumTechnology:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.quantum_computing_level = 0.0\n",
    "    \n",
    "    def develop_quantum_tech(self, investment):\n",
    "        # Develop quantum computing and communication technologies\n",
    "        self.quantum_computing_level += investment * 0.1\n",
    "        self.quantum_computing_level = np.clip(self.quantum_computing_level, 0, 1)\n",
    "    \n",
    "    def gain_strategic_advantage(self):\n",
    "        # Gain strategic advantages through quantum technology\n",
    "        if self.quantum_computing_level > 0.7:\n",
    "            self.agent.economy.resource_prices['tools'] *= 0.9  # Enhanced tools due to quantum computing\n",
    "            \n",
    "class AutonomousSystems:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.autonomous_agent_level = 0.0\n",
    "    \n",
    "    def develop_autonomous_agents(self, technology_level):\n",
    "        # Develop autonomous systems or robots\n",
    "        if technology_level > 8:\n",
    "            self.autonomous_agent_level += 0.1\n",
    "            self.autonomous_agent_level = np.clip(self.autonomous_agent_level, 0, 1)\n",
    "    \n",
    "    def manage_risks(self):\n",
    "        # Manage risks like malfunction or rebellion\n",
    "        if self.autonomous_agent_level > 0.8 and random.random() < 0.1:\n",
    "            # Risk of malfunction or rebellion\n",
    "            self.agent.population.individuals = random.sample(self.agent.population.individuals, len(self.agent.population.individuals) // 2)\n",
    "\n",
    "class Transhumanism:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.transhumanism_level = 0.0\n",
    "    \n",
    "    def develop_neural_interfaces(self, investment):\n",
    "        # Develop advanced neural interfaces\n",
    "        self.transhumanism_level += investment * 0.1\n",
    "        self.transhumanism_level = np.clip(self.transhumanism_level, 0, 1)\n",
    "    \n",
    "    def enhance_human_abilities(self):\n",
    "        # Enhance human abilities through transhumanism\n",
    "        if self.transhumanism_level > 0.7:\n",
    "            for ind in self.agent.population.individuals:\n",
    "                ind.intelligence += 0.1  # Boost intelligence through neural interfaces\n",
    "                ind.health += 0.1  # Boost health through neural enhancements\n",
    "                \n",
    "class NarrativeEngine:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.storyline = []\n",
    "    \n",
    "    def generate_storyline(self, global_events):\n",
    "        # Generate emergent storylines based on agent actions and global events\n",
    "        event = random.choice(global_events)\n",
    "        self.storyline.append(f\"{self.agent.name} {event}\")\n",
    "    \n",
    "    def display_storyline(self):\n",
    "        # Display the dynamic storyline\n",
    "        for event in self.storyline:\n",
    "            print(event)\n",
    "\n",
    "class EthicalDilemmas:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "    \n",
    "    def present_dilemma(self, scenario):\n",
    "        # Present moral and ethical dilemmas to the agent\n",
    "        decision = random.choice([\"accept\", \"reject\"])\n",
    "        if decision == \"accept\" and random.random() < 0.5:\n",
    "            # Positive outcome\n",
    "            self.agent.economy.resource_prices['currency'] *= 1.1\n",
    "        elif decision == \"reject\" and random.random() < 0.5:\n",
    "            # Negative outcome\n",
    "            self.agent.population.individuals = random.sample(self.agent.population.individuals, len(self.agent.population.individuals) // 2)\n",
    "        else:\n",
    "            # Neutral outcome\n",
    "            pass\n",
    "        \n",
    "class TimeTravel:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.temporal_manipulation_level = 0.0\n",
    "    \n",
    "    def develop_time_travel(self, investment):\n",
    "        # Develop time-travel technology\n",
    "        self.temporal_manipulation_level += investment * 0.1\n",
    "        self.temporal_manipulation_level = np.clip(self.temporal_manipulation_level, 0, 1)\n",
    "    \n",
    "    def influence_past_events(self):\n",
    "        # Influence past events to alter the present or future\n",
    "        if self.temporal_manipulation_level > 0.7 and random.random() < 0.2:\n",
    "            # Alter historical outcomes\n",
    "            self.agent.population.food *= 1.2\n",
    "            self.agent.population.water *= 1.2\n",
    "            \n",
    "class HistoricalSimulation:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.historical_events = []\n",
    "    \n",
    "    def simulate_historical_period(self, period):\n",
    "        # Simulate a historical period and its challenges\n",
    "        events = [\"war\", \"plague\", \"discovery\"]\n",
    "        event = random.choice(events)\n",
    "        self.historical_events.append(f\"During {period}, {self.agent.name} faced {event}\")\n",
    "    \n",
    "    def learn_from_history(self):\n",
    "        # Learn from historical events to improve future decisions\n",
    "        for event in self.historical_events:\n",
    "            if \"war\" in event:\n",
    "                self.agent.geopolitics.declare_war(random.choice(self.agent.geopolitics.enemies))\n",
    "            elif \"plague\" in event:\n",
    "                self.agent.health_system.manage_epidemic()\n",
    "\n",
    "\n",
    "\n",
    "class AIGovernance:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.ai_control_level = 0.0\n",
    "    \n",
    "    def implement_ai_governance(self, technology_level):\n",
    "        # Implement AI-driven governance\n",
    "        if technology_level > 8:\n",
    "            self.ai_control_level += 0.1\n",
    "            self.ai_control_level = np.clip(self.ai_control_level, 0, 1)\n",
    "    \n",
    "    def manage_risks(self):\n",
    "        # Manage risks like loss of autonomy or unintended consequences\n",
    "        if self.ai_control_level > 0.8 and random.random() < 0.1:\n",
    "            # Risk of AI rebellion or system malfunction\n",
    "            self.agent.population.individuals = random.sample(self.agent.population.individuals, len(self.agent.population.individuals) // 2)\n",
    "\n",
    "\n",
    "# Expanding the RLEnvironment class\n",
    "class RLEnvironment:\n",
    "    def __init__(self, population, agent, world):\n",
    "        self.population = population\n",
    "        self.agent = agent\n",
    "        self.state = self.get_state()\n",
    "        self.world = world\n",
    "\n",
    "    def get_state(self):        \n",
    "        world_state = [\n",
    "            self.world.climate[\"temperature\"],\n",
    "            self.world.climate[\"precipitation\"],\n",
    "            self.world.resources[\"food\"],\n",
    "            self.world.resources[\"water\"],\n",
    "            self.world.resources[\"shelter\"],\n",
    "            self.world.resources[\"medicine\"],\n",
    "            self.world.resources[\"tools\"],\n",
    "            self.world.resources[\"energy\"]\n",
    "        ]\n",
    "        \n",
    "        population_state = [\n",
    "            len(self.population.individuals),  # Population size\n",
    "            \n",
    "            self.population.food,\n",
    "            self.population.water,\n",
    "            self.population.shelter,\n",
    "            self.population.medicine,\n",
    "            self.population.tools,\n",
    "            self.population.currency,\n",
    "            self.population.technology_progress,\n",
    "            self.population.climate_trend,\n",
    "            self.calculate_social_cohesion(),\n",
    "            self.calculate_average_health(),\n",
    "            self.calculate_cultural_influence(),\n",
    "            self.calculate_religious_adherence(),\n",
    "            self.agent.economy.resource_prices['food'],  # Economy indicators\n",
    "            self.agent.economy.resource_prices['water'],\n",
    "            len(self.agent.geopolitics.allies),  # Geopolitical indicators\n",
    "            len(self.agent.geopolitics.enemies)\n",
    "        ]\n",
    "        \n",
    "        return world_state + population_state\n",
    "    \n",
    "    def calculate_social_cohesion(self):\n",
    "        return np.mean([ind.social_cohesion for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_average_health(self):\n",
    "        return np.mean([ind.health for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_cultural_influence(self):\n",
    "        return np.mean([ind.tradition for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_religious_adherence(self):\n",
    "        return np.mean([ind.religion for ind in self.population.individuals])\n",
    "\n",
    "    def step(self, action):\n",
    "        if action == 0:  # Focus on food production\n",
    "            self.population.food += 500\n",
    "        elif action == 1:  # Focus on water management\n",
    "            self.population.water += 500\n",
    "        elif action == 2:  # Focus on shelter construction\n",
    "            self.population.shelter += 400\n",
    "        elif action == 3:  # Focus on medicine production\n",
    "            self.population.medicine += 300\n",
    "        elif action == 4:  # Focus on tools and technology\n",
    "            self.population.tools += 200\n",
    "            self.population.technology_progress += 0.1\n",
    "        elif action == 5:  # Engage in trade\n",
    "            self.agent.economy.trade(\"food\", 300)\n",
    "        elif action == 6:  # Promote cultural growth\n",
    "            for ind in self.population.individuals:\n",
    "                ind.tradition += 0.01\n",
    "        elif action == 7:  # Promote religious values\n",
    "            for ind in self.population.individuals:\n",
    "                ind.religion += 0.01\n",
    "\n",
    "        self.agent.economy.simulate_year()\n",
    "        next_state = self.get_state()\n",
    "        reward = self.calculate_reward()\n",
    "        done = len(self.population.individuals) == 0\n",
    "        return next_state, reward, done\n",
    "    \n",
    "    def calculate_reward(self):\n",
    "        population_size = len(self.population.individuals)\n",
    "        tech_progress = self.population.technology_progress\n",
    "        social_cohesion = self.calculate_social_cohesion()\n",
    "        avg_health = self.calculate_average_health()\n",
    "        cultural_influence = self.calculate_cultural_influence()\n",
    "        religious_adherence = self.calculate_religious_adherence()\n",
    "        economic_stability = 1 / max(abs(self.agent.economy.inflation_rate), 1)\n",
    "        geopolitical_power = len(self.agent.geopolitics.allies) - len(self.agent.geopolitics.enemies)\n",
    "        \n",
    "        food = self.world.resources[\"food\"]\n",
    "        water = self.world.resources[\"water\"]\n",
    "        shelter = self.world.resources[\"shelter\"]\n",
    "        medicine = self.world.resources[\"medicine\"]\n",
    "\n",
    "        reward = 0\n",
    "\n",
    "        if 500 < population_size < 1500:\n",
    "            reward += 1\n",
    "        else:\n",
    "            reward -= 1\n",
    "\n",
    "        if tech_progress > 2:\n",
    "            reward += 0.5\n",
    "\n",
    "        if social_cohesion > 0.7:\n",
    "            reward += 0.5\n",
    "        if avg_health > 0.7:\n",
    "            reward += 0.5\n",
    "\n",
    "        if cultural_influence > 0.5:\n",
    "            reward += 0.3\n",
    "        if religious_adherence > 0.5:\n",
    "            reward += 0.3\n",
    "\n",
    "        reward += economic_stability * 0.5\n",
    "        reward += geopolitical_power * 0.3\n",
    "\n",
    "        if food < 500 or water < 500 or shelter < 400 or medicine < 300:\n",
    "            reward -= 1\n",
    "\n",
    "        return reward\n",
    "\n",
    "# Multi-Agent Environment\n",
    "class MultiAgentEnvironment:\n",
    "    def __init__(self, agents, world):\n",
    "        self.agents = agents\n",
    "        self.world = world\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        for agent in self.agents:\n",
    "            agent.economy.simulate_year()\n",
    "            agent.geopolitics.manage_diplomacy()\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "\n",
    "# Example of initializing multiple agents\n",
    "# Initialize the agent with all features\n",
    "population = Population(100)\n",
    "\n",
    "\n",
    "# Set up the multi-agent environment\n",
    "\n",
    "agents = [PPOAgent(state_size=18, action_size=8, device=device) for _ in range(3)]\n",
    "for agent in agents:\n",
    "    agent = PPOAgent(state_size=18, action_size=8, device=device)\n",
    "    agent.economy = Economy(population)\n",
    "    agent.geopolitics = Geopolitics(agent)\n",
    "    agent.culture = Culture(population)\n",
    "    agent.education_system = EducationSystem(population)\n",
    "    agent.propaganda = Propaganda(agent)\n",
    "    agent.environment = Environment()\n",
    "    agent.resources = Resources()\n",
    "    agent.ai_governance = AIGovernance(agent)\n",
    "    agent.singularity = TechnologicalSingularity(agent)\n",
    "    agent.alliances = Alliances(agent)\n",
    "    agent.health_system = HealthSystem(population)\n",
    "    agent.transhumanism = Transhumanism(agent)\n",
    "    agent.autonomous_systems = AutonomousSystems(agent)\n",
    "    agent.energy_management = EnergyManagement(agent)\n",
    "    agent.infrastructure = Infrastructure(agent)\n",
    "    agent.time_travel = TimeTravel(agent)\n",
    "    agent.narrative_engine = NarrativeEngine(agent)\n",
    "    agent.social_justice = SocialJustice(agent)\n",
    "    agent.covert_operations = CovertOperations(agent)\n",
    "\n",
    "scenarios = [\"ice_age\", \"global_warming\", \"resource_rich\", \"resource_poor\", \"uneven_population\", \"even_population\"]\n",
    "\n",
    "# Randomly choose between ProceduralWorld or ScenarioWorld\n",
    "if random.random() < 0.5:\n",
    "    world = ProceduralWorld()\n",
    "    print(\"Chosen world type: ProceduralWorld\")\n",
    "else:\n",
    "    chosen_scenario = random.choice(scenarios)\n",
    "    world = ScenarioWorld(scenario=chosen_scenario)\n",
    "    print(f\"Chosen world type: ScenarioWorld with scenario '{chosen_scenario}'\")\n",
    "    \n",
    "env = MultiAgentEnvironment(agents, world)\n",
    "env.simulate_generations(10)\n",
    "\n",
    "# Testing the learned policy\n",
    "population_sizes, technology_levels, climate_trends = env.agents[0].economy.population.simulate_generations(50)\n",
    "\n",
    "# Plotting the results to see the outcomes\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8f2740c",
   "metadata": {},
   "source": [
    "## Realistic Multi Agent RL Population Simulator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f84b0420",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.distributions import Categorical\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from tqdm import tqdm\n",
    "from IPython.display import clear_output\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Define the neural network for the PPO\n",
    "class ActorCriticNetwork(nn.Module):\n",
    "    def __init__(self, state_size, action_size):\n",
    "        super(ActorCriticNetwork, self).__init__()\n",
    "        self.fc1 = nn.Linear(state_size, 128)\n",
    "        self.fc_policy = nn.Linear(128, action_size)\n",
    "        self.fc_value = nn.Linear(128, 1)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        policy = torch.softmax(self.fc_policy(x), dim=-1)\n",
    "        value = self.fc_value(x)\n",
    "        return policy, value\n",
    "\n",
    "class PPOAgent:\n",
    "    def __init__(self, state_size, action_size, device):\n",
    "        self.state_size = state_size\n",
    "        self.action_size = action_size\n",
    "        self.device = device \n",
    "        self.model = ActorCriticNetwork(state_size, action_size).to(device)\n",
    "        self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)\n",
    "        self.gamma = 0.99\n",
    "        self.eps_clip = 0.2\n",
    "        self.entropy_coef = 0.01\n",
    "        self.value_loss_coef = 0.5\n",
    "        self.memory = []\n",
    "        self.population = Population(100)\n",
    "        self.economy = Economy(self.population)\n",
    "        self.culture = Culture(self.population)\n",
    "        self.education_system = EducationSystem(self.population)\n",
    "        self.propaganda = Propaganda(self)\n",
    "        self.environment = None\n",
    "        self.geopolitics = Geopolitics(self)\n",
    "        self.resources = Resources()\n",
    "        self.technology_singularity = TechnologicalSingularity(self)\n",
    "        self.alliances = Alliances(self)\n",
    "        self.global_organizations = GlobalOrganizations([self])\n",
    "        self.covert_operations = CovertOperations(self)\n",
    "        self.global_trade_network = GlobalTradeNetwork([self])\n",
    "        self.market_economy = MarketEconomy(self)\n",
    "        self.taxation = Taxation(self)\n",
    "        self.demographics = Demographics(self.population)\n",
    "        self.health_system = HealthSystem(self.population)\n",
    "        self.social_justice = SocialJustice(self)\n",
    "        self.energy_management = EnergyManagement(self)\n",
    "        self.infrastructure = Infrastructure(self)\n",
    "        self.parallel_universes = ParallelUniverses(self)\n",
    "        self.quantum_technology = QuantumTechnology(self)\n",
    "        self.autonomous_systems = AutonomousSystems(self)\n",
    "        self.transhumanism = Transhumanism(self)\n",
    "        self.narrative_engine = NarrativeEngine(self)\n",
    "        self.ethical_dilemmas = EthicalDilemmas(self)\n",
    "        self.time_travel = TimeTravel(self)\n",
    "        self.historical_simulation = HistoricalSimulation(self)\n",
    "        self.ai_governance = AIGovernance(self)\n",
    "        self.rewards_history = []\n",
    "        self.policy_loss_history = []\n",
    "        self.value_loss_history = []\n",
    "    \n",
    "    def select_action(self, state):\n",
    "        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)\n",
    "        policy, value = self.model(state)\n",
    "        dist = Categorical(policy)\n",
    "        action = dist.sample()\n",
    "        log_prob = dist.log_prob(action)\n",
    "        return action.item(), log_prob, value\n",
    "    \n",
    "    def store_transition(self, transition):\n",
    "        self.memory.append(transition)\n",
    "    \n",
    "    def update(self):\n",
    "        states, actions, log_probs, rewards, values, next_values, dones = zip(*self.memory)\n",
    "        \n",
    "        # Convert to tensors\n",
    "        states = torch.FloatTensor(states).to(self.device)\n",
    "        actions = torch.LongTensor(actions).unsqueeze(1).to(self.device)\n",
    "        log_probs = torch.stack(log_probs).to(self.device)\n",
    "        rewards = torch.FloatTensor(rewards).to(self.device)\n",
    "        values = torch.stack(values).squeeze().to(self.device)\n",
    "        next_values = torch.FloatTensor(next_values).to(self.device)\n",
    "        dones = torch.FloatTensor(dones).to(self.device)\n",
    "        \n",
    "        # Compute returns and advantages\n",
    "        returns = []\n",
    "        advantages = []\n",
    "        gae = 0\n",
    "        for i in reversed(range(len(rewards))):\n",
    "            td_error = rewards[i] + self.gamma * next_values[i] * (1 - dones[i]) - values[i]\n",
    "            gae = td_error + self.gamma * 0.95 * (1 - dones[i]) * gae\n",
    "            advantages.insert(0, gae)\n",
    "            returns.insert(0, gae + values[i].item())\n",
    "        \n",
    "        returns = torch.FloatTensor(returns).to(self.device)\n",
    "        advantages = torch.FloatTensor(advantages).to(self.device)\n",
    "        \n",
    "        # Compute the ratio for PPO loss\n",
    "        policy, value = self.model(states)\n",
    "        dist = Categorical(policy)\n",
    "        new_log_probs = dist.log_prob(actions.squeeze())\n",
    "        ratios = torch.exp(new_log_probs - log_probs)\n",
    "        \n",
    "        # Compute surrogate loss\n",
    "        surr1 = ratios * advantages\n",
    "        surr2 = torch.clamp(ratios, 1 - self.eps_clip, 1 + self.eps_clip) * advantages\n",
    "        policy_loss = -torch.min(surr1, surr2).mean()\n",
    "        \n",
    "        # Value loss\n",
    "        value_loss = nn.MSELoss()(returns, value.squeeze())\n",
    "        \n",
    "        # Entropy for exploration\n",
    "        entropy_loss = dist.entropy().mean()\n",
    "        \n",
    "        # Total loss\n",
    "        loss = policy_loss + self.value_loss_coef * value_loss - self.entropy_coef * entropy_loss\n",
    "        \n",
    "        # Update the model\n",
    "        self.optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        self.optimizer.step()\n",
    "        \n",
    "        self.rewards_history.append(np.mean(rewards.numpy()))\n",
    "        self.policy_loss_history.append(policy_loss.item())\n",
    "        self.value_loss_history.append(value_loss.item())\n",
    "        \n",
    "        # Clear memory\n",
    "        self.memory = []\n",
    "\n",
    "        def train(self, env, episodes):\n",
    "            for episode in tqdm(range(episodes)):\n",
    "                state = env.get_state()\n",
    "                episode_reward = 0\n",
    "                done = False\n",
    "\n",
    "                while not done:\n",
    "                    action, log_prob, value = self.select_action(state)\n",
    "                    next_state, reward, done = env.step(action)\n",
    "                    next_value = self.model(torch.FloatTensor(next_state).unsqueeze(0))[1].item()\n",
    "\n",
    "                    transition = (state, action, log_prob, reward, value, next_value, done)\n",
    "                    self.store_transition(transition)\n",
    "\n",
    "                    episode_reward += reward\n",
    "                    state = next_state\n",
    "\n",
    "                self.update()\n",
    "                self.plot_training_metrics()\n",
    "                \n",
    "        def plot_training_metrics(self):\n",
    "            clear_output(wait=True)\n",
    "            plt.figure(figsize=(12, 5))\n",
    "\n",
    "            plt.subplot(1, 3, 1)\n",
    "            plt.plot(self.rewards_history)\n",
    "            plt.title(\"Rewards History\")\n",
    "            plt.xlabel(\"Episodes\")\n",
    "            plt.ylabel(\"Average Reward\")\n",
    "\n",
    "            plt.subplot(1, 3, 2)\n",
    "            plt.plot(self.policy_loss_history)\n",
    "            plt.title(\"Policy Loss History\")\n",
    "            plt.xlabel(\"Episodes\")\n",
    "            plt.ylabel(\"Policy Loss\")\n",
    "\n",
    "            plt.subplot(1, 3, 3)\n",
    "            plt.plot(self.value_loss_history)\n",
    "            plt.title(\"Value Loss History\")\n",
    "            plt.xlabel(\"Episodes\")\n",
    "            plt.ylabel(\"Value Loss\")\n",
    "\n",
    "            plt.tight_layout()\n",
    "            plt.show()\n",
    "            \n",
    "class HierarchicalPPOAgent(PPOAgent):\n",
    "    def __init__(self, state_size, action_size, device):\n",
    "        super().__init__(state_size, action_size, device)\n",
    "        self.high_level_policy = HighLevelPolicy(state_size, device)\n",
    "        self.mid_level_policies = {\n",
    "            'EconomicGrowth': MidLevelPolicy(state_size, device),\n",
    "            'CulturalDominance': MidLevelPolicy(state_size, device),\n",
    "            'MilitaryExpansion': MidLevelPolicy(state_size, device)\n",
    "        }\n",
    "        self.low_level_policies = {\n",
    "            'IncreaseProduction': LowLevelPolicy(state_size, action_size, device),\n",
    "            'ExpandTradeNetworks': LowLevelPolicy(state_size, action_size, device),\n",
    "            'DevelopAdvancedTechnologies': LowLevelPolicy(state_size, action_size, device),\n",
    "            'BuildFarms': LowLevelPolicy(state_size, action_size, device),\n",
    "            'ResearchAI': LowLevelPolicy(state_size, action_size, device),\n",
    "            'FormAlliances': LowLevelPolicy(state_size, action_size, device)\n",
    "        }\n",
    "\n",
    "    def select_action(self, state):\n",
    "        # High-level goal selection\n",
    "        high_level_goal = self.high_level_policy.select_goal(state)\n",
    "        \n",
    "        # Mid-level strategy selection based on the chosen high-level goal\n",
    "        mid_level_strategy = self.mid_level_policies[high_level_goal].select_strategy(state)\n",
    "        \n",
    "        # Low-level action selection based on the chosen mid-level strategy\n",
    "        action, log_prob, value = self.low_level_policies[mid_level_strategy].select_action(state)\n",
    "        \n",
    "        return action, log_prob, value\n",
    "\n",
    "class HighLevelPolicy(nn.Module):\n",
    "    def __init__(self, state_size, device):\n",
    "        super(HighLevelPolicy, self).__init__()\n",
    "        self.fc1 = nn.Linear(state_size, 64)\n",
    "        self.fc_goal = nn.Linear(64, 3)  # Assuming 3 high-level goals\n",
    "        self.device = device\n",
    "        self.to(device)\n",
    "\n",
    "    def forward(self, state):\n",
    "        x = torch.relu(self.fc1(state))\n",
    "        goal_probs = torch.softmax(self.fc_goal(x), dim=-1)\n",
    "        return goal_probs\n",
    "    \n",
    "    def select_goal(self, state):\n",
    "        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)\n",
    "        goal_probs = self.forward(state)\n",
    "        goal_dist = Categorical(goal_probs)\n",
    "        goal = goal_dist.sample()\n",
    "        return goal.item()\n",
    "\n",
    "class MidLevelPolicy(nn.Module):\n",
    "    def __init__(self, state_size, device):\n",
    "        super(MidLevelPolicy, self).__init__()\n",
    "        self.fc1 = nn.Linear(state_size, 64)\n",
    "        self.fc_strategy = nn.Linear(64, 3)  # Assuming 3 strategies per goal\n",
    "        self.device = device\n",
    "        self.to(device)\n",
    "\n",
    "    def forward(self, state):\n",
    "        x = torch.relu(self.fc1(state))\n",
    "        strategy_probs = torch.softmax(self.fc_strategy(x), dim=-1)\n",
    "        return strategy_probs\n",
    "    \n",
    "    def select_strategy(self, state):\n",
    "        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)\n",
    "        strategy_probs = self.forward(state)\n",
    "        strategy_dist = Categorical(strategy_probs)\n",
    "        strategy = strategy_dist.sample()\n",
    "        return strategy.item()\n",
    "    \n",
    "    def transfer_knowledge(self, other_policy):\n",
    "        # Transfer knowledge from another policy by copying weights\n",
    "        self.load_state_dict(other_policy.state_dict())\n",
    "\n",
    "class LowLevelPolicy(PPOAgent):\n",
    "    def __init__(self, state_size, action_size, device):\n",
    "        super().__init__(state_size, action_size, device)\n",
    "            \n",
    "            \n",
    "class ProceduralWorld:\n",
    "    def __init__(self):\n",
    "        self.climate = self.generate_climate()\n",
    "        self.resources = self.generate_resources()\n",
    "        self.population_distribution = self.generate_population_distribution()\n",
    "\n",
    "    def generate_climate(self):\n",
    "        return {\n",
    "            \"temperature\": np.random.uniform(-10, 40),  # Degrees Celsius\n",
    "            \"precipitation\": np.random.uniform(0, 5000),  # mm/year\n",
    "            \"climate_variability\": np.random.uniform(0, 0.1),  # Climate variability factor\n",
    "        }\n",
    "\n",
    "    def generate_resources(self):\n",
    "        return {\n",
    "            \"food\": np.random.uniform(5000, 20000),\n",
    "            \"water\": np.random.uniform(5000, 20000),\n",
    "            \"shelter\": np.random.uniform(4000, 18000),\n",
    "            \"medicine\": np.random.uniform(3000, 15000),\n",
    "            \"tools\": np.random.uniform(3000, 15000),\n",
    "            \"energy\": np.random.uniform(2000, 10000),\n",
    "        }\n",
    "\n",
    "    def generate_population_distribution(self):\n",
    "        return {\n",
    "            \"region_1\": np.random.randint(50, 500),\n",
    "            \"region_2\": np.random.randint(50, 500),\n",
    "            \"region_3\": np.random.randint(50, 500),\n",
    "        }\n",
    "\n",
    "    def simulate_year(self):\n",
    "        self.update_climate()\n",
    "        self.update_resources()\n",
    "\n",
    "    def update_climate(self):\n",
    "        self.climate[\"temperature\"] += np.random.uniform(-0.5, 0.5)\n",
    "        self.climate[\"precipitation\"] += np.random.uniform(-50, 50)\n",
    "        self.climate[\"temperature\"] = np.clip(self.climate[\"temperature\"], -50, 50)\n",
    "        self.climate[\"precipitation\"] = np.clip(self.climate[\"precipitation\"], 0, 10000)\n",
    "\n",
    "    def update_resources(self):\n",
    "        for resource in self.resources:\n",
    "            usage_factor = np.random.uniform(0.9, 1.1)\n",
    "            regeneration_factor = np.random.uniform(0.01, 0.05)\n",
    "            self.resources[resource] = max(0, self.resources[resource] * usage_factor + regeneration_factor * self.resources[resource])\n",
    "\n",
    "# Define ScenarioWorld class\n",
    "class ScenarioWorld:\n",
    "    def __init__(self, scenario=\"default\"):\n",
    "        self.scenario = scenario\n",
    "        self.climate = self.initialize_climate()\n",
    "        self.resources = self.initialize_resources()\n",
    "        self.population_distribution = self.initialize_population_distribution()\n",
    "\n",
    "    def initialize_climate(self):\n",
    "        if self.scenario == \"ice_age\":\n",
    "            return {\"temperature\": -20, \"precipitation\": 100, \"climate_variability\": 0.05}\n",
    "        elif self.scenario == \"global_warming\":\n",
    "            return {\"temperature\": 35, \"precipitation\": 1000, \"climate_variability\": 0.08}\n",
    "        else:  # Default scenario\n",
    "            return {\"temperature\": 20, \"precipitation\": 1500, \"climate_variability\": 0.03}\n",
    "\n",
    "    def initialize_resources(self):\n",
    "        if self.scenario == \"resource_rich\":\n",
    "            return {\"food\": 20000, \"water\": 20000, \"shelter\": 18000, \"medicine\": 15000, \"tools\": 15000, \"energy\": 10000}\n",
    "        elif self.scenario == \"resource_poor\":\n",
    "            return {\"food\": 5000, \"water\": 5000, \"shelter\": 4000, \"medicine\": 3000, \"tools\": 3000, \"energy\": 2000}\n",
    "        else:  # Default scenario\n",
    "            return {\"food\": 10000, \"water\": 10000, \"shelter\": 9000, \"medicine\": 8000, \"tools\": 8000, \"energy\": 7000}\n",
    "\n",
    "    def initialize_population_distribution(self):\n",
    "        if self.scenario == \"uneven_population\":\n",
    "            return {\"region_1\": 50, \"region_2\": 450, \"region_3\": 500}\n",
    "        elif self.scenario == \"even_population\":\n",
    "            return {\"region_1\": 300, \"region_2\": 300, \"region_3\": 300}\n",
    "        else:  # Default scenario\n",
    "            return {\"region_1\": 200, \"region_2\": 200, \"region_3\": 200}\n",
    "\n",
    "    def simulate_year(self):\n",
    "        self.update_climate()\n",
    "        self.update_resources()\n",
    "\n",
    "    def update_climate(self):\n",
    "        if self.scenario == \"global_warming\":\n",
    "            self.climate[\"temperature\"] += np.random.uniform(0.2, 0.5)\n",
    "        elif self.scenario == \"ice_age\":\n",
    "            self.climate[\"temperature\"] -= np.random.uniform(0.1, 0.3)\n",
    "        else:\n",
    "            self.climate[\"temperature\"] += np.random.uniform(-0.5, 0.5)\n",
    "\n",
    "        self.climate[\"precipitation\"] += np.random.uniform(-50, 50)\n",
    "        self.climate[\"temperature\"] = np.clip(self.climate[\"temperature\"], -50, 50)\n",
    "        self.climate[\"precipitation\"] = np.clip(self.climate[\"precipitation\"], 0, 10000)\n",
    "\n",
    "    def update_resources(self):\n",
    "        for resource in self.resources:\n",
    "            if self.scenario == \"resource_rich\":\n",
    "                regeneration_factor = np.random.uniform(0.05, 0.1)\n",
    "            elif self.scenario == \"resource_poor\":\n",
    "                regeneration_factor = np.random.uniform(0.01, 0.02)\n",
    "            else:\n",
    "                regeneration_factor = np.random.uniform(0.03, 0.05)\n",
    "\n",
    "            usage_factor = np.random.uniform(0.9, 1.1)\n",
    "            self.resources[resource] = max(0, self.resources[resource] * usage_factor + regeneration_factor * self.resources[resource])        \n",
    "        \n",
    "class Culture:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.cultural_practices = np.mean([ind.tradition for ind in self.population.individuals])\n",
    "    \n",
    "    def evolve(self, immigration, technology, wars):\n",
    "        # Cultural change influenced by immigration, technology, and wars\n",
    "        cultural_change = immigration * 0.1 + technology * 0.05 - wars * 0.05\n",
    "        self.cultural_practices += cultural_change\n",
    "        self.cultural_practices = np.clip(self.cultural_practices, 0, 1)  # Bound cultural practices between 0 and 1\n",
    "    \n",
    "    def influence_or_resist_change(self, agent):\n",
    "        # Agents can influence or resist cultural change\n",
    "        if random.random() < agent.leadership_style:\n",
    "            self.cultural_practices += 0.02  # Influence cultural change\n",
    "        else:\n",
    "            self.cultural_practices -= 0.02  # Resist cultural change\n",
    "        self.cultural_practices = np.clip(self.cultural_practices, 0, 1)\n",
    "        \n",
    "class EducationSystem:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.education_level = np.mean([ind.intelligence for ind in self.population.individuals])\n",
    "    \n",
    "    def invest_in_education(self, funding):\n",
    "        # Invest in education, impacting intelligence and technology progression\n",
    "        self.education_level += funding * 0.05\n",
    "        self.education_level = np.clip(self.education_level, 0, 1)\n",
    "    \n",
    "    def influence_social_cohesion(self):\n",
    "        # Higher education levels improve social cohesion\n",
    "        social_cohesion_bonus = self.education_level * 0.1\n",
    "        for ind in self.population.individuals:\n",
    "            ind.social_cohesion += social_cohesion_bonus\n",
    "            \n",
    "class Propaganda:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "    \n",
    "    def spread_ideologies(self, target_agent):\n",
    "        # Use propaganda to influence other agents' populations\n",
    "        influence_power = self.agent.economy.resource_prices['currency'] * 0.1\n",
    "        target_agent.culture.cultural_practices += influence_power * 0.01\n",
    "        target_agent.culture.cultural_practices = np.clip(target_agent.culture.cultural_practices, 0, 1)\n",
    "\n",
    "class Environment:\n",
    "    def __init__(self):\n",
    "        self.pollution_level = 0.1\n",
    "        self.deforestation = 0.1\n",
    "    \n",
    "    def degrade_environment(self, population_size, resource_usage):\n",
    "        # Environmental degradation based on population size and resource usage\n",
    "        self.pollution_level += population_size * 0.01 + resource_usage * 0.01\n",
    "        self.deforestation += population_size * 0.005\n",
    "    \n",
    "    def restore_environment(self, restoration_efforts):\n",
    "        # Environmental restoration efforts\n",
    "        self.pollution_level -= restoration_efforts * 0.02\n",
    "        self.deforestation -= restoration_efforts * 0.01\n",
    "        self.pollution_level = max(0, self.pollution_level)\n",
    "        self.deforestation = max(0, self.deforestation)\n",
    "\n",
    "\n",
    "class Economy:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.resource_prices = {\n",
    "            \"food\": 1.0,\n",
    "            \"water\": 1.0,\n",
    "            \"shelter\": 1.0,\n",
    "            \"medicine\": 1.0,\n",
    "            \"tools\": 1.0,\n",
    "            \"currency\": 1.0\n",
    "        }\n",
    "        self.inflation_rate = 0.02  # Basic inflation rate\n",
    "\n",
    "    def update_prices(self):\n",
    "        # Adjust prices based on supply and demand\n",
    "        for resource in self.resource_prices:\n",
    "            demand = sum(ind.health * 1.5 for ind in self.population.individuals)\n",
    "            supply = getattr(self.population, resource)\n",
    "            self.resource_prices[resource] *= 1 + (demand - supply) / max(supply, 1) * self.inflation_rate\n",
    "\n",
    "    def trade(self, resource, quantity):\n",
    "        # Perform trade and adjust the population's resources\n",
    "        cost = self.resource_prices[resource] * quantity\n",
    "        if self.population.currency >= cost:\n",
    "            self.population.currency -= cost\n",
    "            setattr(self.population, resource, getattr(self.population, resource) + quantity)\n",
    "        else:\n",
    "            quantity_affordable = self.population.currency / self.resource_prices[resource]\n",
    "            self.population.currency = 0\n",
    "            setattr(self.population, resource, getattr(self.population, resource) + quantity_affordable)\n",
    "\n",
    "    def simulate_year(self):\n",
    "        self.update_prices()\n",
    "        self.population.simulate_year()\n",
    "\n",
    "# Geopolitical Model\n",
    "class Geopolitics:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.allies = []\n",
    "        self.enemies = []\n",
    "\n",
    "    def form_alliance(self, other_agent):\n",
    "        if other_agent not in self.allies:\n",
    "            self.allies.append(other_agent)\n",
    "            other_agent.geopolitics.allies.append(self.agent)\n",
    "\n",
    "    def declare_war(self, other_agent):\n",
    "        if other_agent not in self.enemies:\n",
    "            self.enemies.append(other_agent)\n",
    "            other_agent.geopolitics.enemies.append(self.agent)\n",
    "\n",
    "    def negotiate_trade(self, other_agent, resource, quantity):\n",
    "        if other_agent in self.allies:\n",
    "            self.agent.economy.trade(resource, quantity)\n",
    "            other_agent.economy.trade(resource, -quantity)\n",
    "\n",
    "    def manage_diplomacy(self):\n",
    "        # Manage alliances and conflicts\n",
    "        for ally in self.allies:\n",
    "            if random.random() < 0.05:\n",
    "                self.negotiate_trade(ally, \"food\", 100)\n",
    "        for enemy in self.enemies:\n",
    "            if random.random() < 0.05:\n",
    "                self.declare_war(enemy)\n",
    "                \n",
    "class Resources:\n",
    "    def __init__(self):\n",
    "        self.resource_levels = {\n",
    "            \"food\": 1000,\n",
    "            \"water\": 1000,\n",
    "            \"shelter\": 900,\n",
    "            \"medicine\": 800,\n",
    "            \"tools\": 800,\n",
    "            \"energy\": 700,\n",
    "        }\n",
    "    \n",
    "    def exhaust_or_renew(self, technology_level, policies):\n",
    "        # Resource exhaustion or renewal based on technology and policies\n",
    "        for resource in self.resource_levels:\n",
    "            if random.random() < 0.05:  # Chance of exhaustion\n",
    "                self.resource_levels[resource] *= 0.95\n",
    "            elif policies.get(\"renewable_\" + resource):\n",
    "                self.resource_levels[resource] += technology_level * 0.05\n",
    "            self.resource_levels[resource] = max(0, self.resource_levels[resource])\n",
    "            \n",
    "class TechnologicalSingularity:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.singularity_level = 0.0\n",
    "    \n",
    "    def approach_singularity(self, technology_level):\n",
    "        # Progress toward a technological singularity\n",
    "        if technology_level > 9:\n",
    "            self.singularity_level += 0.1\n",
    "            self.singularity_level = np.clip(self.singularity_level, 0, 1)\n",
    "    \n",
    "    def manage_consequences(self):\n",
    "        # Manage the consequences of a singularity\n",
    "        if self.singularity_level > 0.9 and random.random() < 0.2:\n",
    "            # Risk of immense power or existential threats\n",
    "            if random.random() < 0.5:\n",
    "                # Immense power: agent gains significant advantages\n",
    "                self.agent.population.food *= 2\n",
    "                self.agent.population.water *= 2\n",
    "            else:\n",
    "                # Existential threat: agent faces severe consequences\n",
    "                self.agent.population.individuals = random.sample(self.agent.population.individuals, len(self.agent.population.individuals) // 2)\n",
    "\n",
    "class Alliances:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.treaties = []\n",
    "    \n",
    "    def form_complex_alliance(self, other_agent, conditions):\n",
    "        # Form complex alliances with conditions\n",
    "        self.treaties.append({\"partner\": other_agent, \"conditions\": conditions})\n",
    "        other_agent.geopolitics.treaties.append({\"partner\": self.agent, \"conditions\": conditions})\n",
    "    \n",
    "    def enforce_treaties(self):\n",
    "        # Enforce treaties based on global events\n",
    "        for treaty in self.treaties:\n",
    "            conditions = treaty[\"conditions\"]\n",
    "            partner = treaty[\"partner\"]\n",
    "            if conditions.get(\"defensive_pact\") and random.random() < 0.1:\n",
    "                # Defend ally in case of attack\n",
    "                if partner.geopolitics.enemies:\n",
    "                    self.agent.geopolitics.declare_war(partner.geopolitics.enemies[0])\n",
    "            if conditions.get(\"trade_agreement\") and random.random() < 0.2:\n",
    "                # Enhance trade with ally\n",
    "                self.agent.economy.trade(\"tools\", 100)\n",
    "                partner.economy.trade(\"tools\", -100)\n",
    "            if conditions.get(\"scientific_collaboration\"):\n",
    "                # Collaborate on scientific research\n",
    "                self.agent.population.technology_progress += 0.1\n",
    "                partner.population.technology_progress += 0.1\n",
    "\n",
    "class GlobalOrganizations:\n",
    "    def __init__(self, agents):\n",
    "        self.agents = agents\n",
    "    \n",
    "    def vote_on_resolutions(self, resolution):\n",
    "        # Agents vote on global resolutions (e.g., climate change, pandemics)\n",
    "        votes = [random.choice([True, False]) for _ in self.agents]\n",
    "        if sum(votes) > len(self.agents) // 2:\n",
    "            self.enforce_resolution(resolution)\n",
    "    \n",
    "    def enforce_resolution(self, resolution):\n",
    "        # Enforce global resolutions\n",
    "        for agent in self.agents:\n",
    "            if resolution == \"sanctions\":\n",
    "                if random.random() < 0.3:\n",
    "                    agent.economy.resource_prices['currency'] *= 1.1  # Impact of sanctions\n",
    "            elif resolution == \"climate_action\":\n",
    "                # Force agents to invest in environmental restoration\n",
    "                agent.environment.restore_environment(100)\n",
    "\n",
    "\n",
    "class CovertOperations:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "    \n",
    "    def fund_proxy_wars(self, target_agent):\n",
    "        # Fund proxy wars to destabilize other agents\n",
    "        if self.agent.economy.resource_prices['currency'] > 100:\n",
    "            target_agent.population.individuals = random.sample(target_agent.population.individuals, len(target_agent.population.individuals) // 2)\n",
    "            self.agent.economy.resource_prices['currency'] -= 100\n",
    "    \n",
    "    def engage_in_espionage(self, target_agent):\n",
    "        # Engage in espionage to gather intelligence\n",
    "        if random.random() < 0.1:\n",
    "            self.agent.economy.trade(\"tools\", 100)  # Steal technology\n",
    "            target_agent.economy.trade(\"tools\", -100)\n",
    "            \n",
    "    def sabotage(self, target_agent):\n",
    "        # Sabotage the target agent's resources or infrastructure\n",
    "        if random.random() < 0.1:\n",
    "            target_agent.population.food *= 0.9  # Reduce food supply by 10%\n",
    "            target_agent.population.water *= 0.9  # Reduce water supply by 10%\n",
    "            \n",
    "\n",
    "class GlobalTradeNetwork:\n",
    "    def __init__(self, agents):\n",
    "        self.agents = agents\n",
    "        self.trade_routes = {}\n",
    "    \n",
    "    def establish_trade_route(self, agent_a, agent_b):\n",
    "        # Establish trade routes between agents\n",
    "        self.trade_routes[(agent_a, agent_b)] = {\"status\": \"active\"}\n",
    "    \n",
    "    def disrupt_trade_route(self, agent_a, agent_b):\n",
    "        # Disrupt trade routes due to conflict or disasters\n",
    "        if random.random() < 0.1:\n",
    "            self.trade_routes[(agent_a, agent_b)][\"status\"] = \"disrupted\"\n",
    "    \n",
    "    def trade_resources(self, agent_a, agent_b, resource, quantity):\n",
    "        # Trade resources between agents\n",
    "        if self.trade_routes[(agent_a, agent_b)][\"status\"] == \"active\":\n",
    "            agent_a.economy.trade(resource, quantity)\n",
    "            agent_b.economy.trade(resource, -quantity)\n",
    "\n",
    "class MarketEconomy:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.market_conditions = {\"boom\": False, \"bust\": False}\n",
    "    \n",
    "    def simulate_market_cycle(self):\n",
    "        # Simulate economic booms and busts\n",
    "        if random.random() < 0.1:\n",
    "            self.market_conditions[\"boom\"] = True\n",
    "            self.agent.economy.resource_prices['currency'] *= 0.9\n",
    "        elif random.random() < 0.1:\n",
    "            self.market_conditions[\"bust\"] = True\n",
    "            self.agent.economy.resource_prices['currency'] *= 1.2\n",
    "    \n",
    "    def manage_financial_crisis(self):\n",
    "        # Manage financial crises\n",
    "        if self.market_conditions[\"bust\"]:\n",
    "            self.agent.economy.resource_prices['currency'] *= 1.1  # Increase inflation during bust\n",
    "            \n",
    "class Taxation:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.tax_rate = 0.1\n",
    "    \n",
    "    def collect_taxes(self):\n",
    "        # Collect taxes based on wealth\n",
    "        tax_revenue = sum(ind.wealth * self.tax_rate for ind in self.agent.population.individuals)\n",
    "        self.agent.economy.resource_prices['currency'] += tax_revenue\n",
    "    \n",
    "    def allocate_spending(self, spending_priorities):\n",
    "        # Allocate public spending based on priorities\n",
    "        for priority in spending_priorities:\n",
    "            if priority == \"education\":\n",
    "                self.agent.education_system.invest_in_education(100)\n",
    "            elif priority == \"infrastructure\":\n",
    "                self.agent.infrastructure.invest_in_projects(100)\n",
    "                \n",
    "class Demographics:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.age_distribution = [random.randint(0, 80) for _ in range(len(self.population.individuals))]\n",
    "    \n",
    "    def simulate_aging(self):\n",
    "        # Simulate demographic changes over time\n",
    "        self.age_distribution = [age + 1 for age in self.age_distribution]\n",
    "        self.age_distribution = [age for age in self.age_distribution if age < 80]\n",
    "    \n",
    "    def manage_aging_population(self):\n",
    "        # Manage challenges like an aging workforce\n",
    "        aging_factor = len([age for age in self.age_distribution if age > 60]) / len(self.age_distribution)\n",
    "        if aging_factor > 0.3:\n",
    "            self.population.birth_rate += 0.01  # Encourage higher birth rates to offset aging\n",
    "            \n",
    "class HealthSystem:\n",
    "    def __init__(self, population):\n",
    "        self.population = population\n",
    "        self.healthcare_quality = np.mean([ind.health for ind in self.population.individuals])\n",
    "    \n",
    "    def invest_in_healthcare(self, funding):\n",
    "        # Invest in healthcare to improve public health\n",
    "        self.healthcare_quality += funding * 0.05\n",
    "        self.healthcare_quality = np.clip(self.healthcare_quality, 0, 1)\n",
    "    \n",
    "    def manage_epidemic(self):\n",
    "        # Respond to epidemics\n",
    "        if random.random() < 0.1:\n",
    "            self.population.individuals = random.sample(self.population.individuals, len(self.population.individuals) // 2)\n",
    "\n",
    "class SocialJustice:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.human_rights_level = 0.5\n",
    "    \n",
    "    def implement_policies(self, policies):\n",
    "        # Implement policies on human rights and equality\n",
    "        if policies.get(\"human_rights\"):\n",
    "            self.human_rights_level += 0.1\n",
    "        if policies.get(\"equality\"):\n",
    "            self.human_rights_level += 0.1\n",
    "    \n",
    "    def manage_unrest(self):\n",
    "        # Manage social unrest due to poor human rights\n",
    "        if self.human_rights_level < 0.5 and random.random() < 0.2:\n",
    "            self.agent.population.individuals = random.sample(self.agent.population.individuals, len(self.agent.population.individuals) // 2)\n",
    "\n",
    "            \n",
    "class EnergyManagement:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.energy_mix = {\"fossil_fuels\": 0.7, \"nuclear\": 0.2, \"renewables\": 0.1}\n",
    "    \n",
    "    def shift_energy_policy(self, energy_type, investment):\n",
    "        # Shift energy policies and manage the energy mix\n",
    "        self.energy_mix[energy_type] += investment * 0.05\n",
    "        for other in self.energy_mix:\n",
    "            if other != energy_type:\n",
    "                self.energy_mix[other] -= investment * 0.05\n",
    "        self.energy_mix[energy_type] = np.clip(self.energy_mix[energy_type], 0, 1)\n",
    "\n",
    "class Infrastructure:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.projects = {\"transportation\": 0.5, \"communication\": 0.5, \"housing\": 0.5}\n",
    "    \n",
    "    def invest_in_projects(self, investment):\n",
    "        # Invest in infrastructure projects\n",
    "        for project in self.projects:\n",
    "            self.projects[project] += investment * 0.05\n",
    "            self.projects[project] = np.clip(self.projects[project], 0, 1)\n",
    "    \n",
    "    def influence_economic_growth(self):\n",
    "        # Influence economic growth through infrastructure development\n",
    "        for project in self.projects:\n",
    "            self.agent.economy.resource_prices['currency'] *= 1 + self.projects[project] * 0.1\n",
    "\n",
    "class ParallelUniverses:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.parallel_universe_links = 0\n",
    "    \n",
    "    def interact_with_parallel_universes(self, technology_level):\n",
    "        # Interact with parallel universes\n",
    "        if technology_level > 10:\n",
    "            self.parallel_universe_links += 1\n",
    "    \n",
    "    def manipulate_alternate_realities(self):\n",
    "        # Manipulate alternate realities for strategic advantages\n",
    "        if self.parallel_universe_links > 0 and random.random() < 0.2:\n",
    "            # Gain knowledge from alternate realities\n",
    "            self.agent.technology_level += 0.5\n",
    "            \n",
    "class QuantumTechnology:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.quantum_computing_level = 0.0\n",
    "    \n",
    "    def develop_quantum_tech(self, investment):\n",
    "        # Develop quantum computing and communication technologies\n",
    "        self.quantum_computing_level += investment * 0.1\n",
    "        self.quantum_computing_level = np.clip(self.quantum_computing_level, 0, 1)\n",
    "    \n",
    "    def gain_strategic_advantage(self):\n",
    "        # Gain strategic advantages through quantum technology\n",
    "        if self.quantum_computing_level > 0.7:\n",
    "            self.agent.economy.resource_prices['tools'] *= 0.9  # Enhanced tools due to quantum computing\n",
    "            \n",
    "class AutonomousSystems:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.autonomous_agent_level = 0.0\n",
    "    \n",
    "    def develop_autonomous_agents(self, technology_level):\n",
    "        # Develop autonomous systems or robots\n",
    "        if technology_level > 8:\n",
    "            self.autonomous_agent_level += 0.1\n",
    "            self.autonomous_agent_level = np.clip(self.autonomous_agent_level, 0, 1)\n",
    "    \n",
    "    def manage_risks(self):\n",
    "        # Manage risks like malfunction or rebellion\n",
    "        if self.autonomous_agent_level > 0.8 and random.random() < 0.1:\n",
    "            # Risk of malfunction or rebellion\n",
    "            self.agent.population.individuals = random.sample(self.agent.population.individuals, len(self.agent.population.individuals) // 2)\n",
    "\n",
    "class Transhumanism:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.transhumanism_level = 0.0\n",
    "    \n",
    "    def develop_neural_interfaces(self, investment):\n",
    "        # Develop advanced neural interfaces\n",
    "        self.transhumanism_level += investment * 0.1\n",
    "        self.transhumanism_level = np.clip(self.transhumanism_level, 0, 1)\n",
    "    \n",
    "    def enhance_human_abilities(self):\n",
    "        # Enhance human abilities through transhumanism\n",
    "        if self.transhumanism_level > 0.7:\n",
    "            for ind in self.agent.population.individuals:\n",
    "                ind.intelligence += 0.1  # Boost intelligence through neural interfaces\n",
    "                ind.health += 0.1  # Boost health through neural enhancements\n",
    "                \n",
    "class NarrativeEngine:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.storyline = []\n",
    "    \n",
    "    def generate_storyline(self, global_events):\n",
    "        # Generate emergent storylines based on agent actions and global events\n",
    "        event = random.choice(global_events)\n",
    "        self.storyline.append(f\"{self.agent.name} {event}\")\n",
    "    \n",
    "    def display_storyline(self):\n",
    "        # Display the dynamic storyline\n",
    "        for event in self.storyline:\n",
    "            print(event)\n",
    "\n",
    "class EthicalDilemmas:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "    \n",
    "    def present_dilemma(self, scenario):\n",
    "        # Present moral and ethical dilemmas to the agent\n",
    "        decision = random.choice([\"accept\", \"reject\"])\n",
    "        if decision == \"accept\" and random.random() < 0.5:\n",
    "            # Positive outcome\n",
    "            self.agent.economy.resource_prices['currency'] *= 1.1\n",
    "        elif decision == \"reject\" and random.random() < 0.5:\n",
    "            # Negative outcome\n",
    "            self.agent.population.individuals = random.sample(self.agent.population.individuals, len(self.agent.population.individuals) // 2)\n",
    "        else:\n",
    "            # Neutral outcome\n",
    "            pass\n",
    "        \n",
    "class TimeTravel:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.temporal_manipulation_level = 0.0\n",
    "    \n",
    "    def develop_time_travel(self, investment):\n",
    "        # Develop time-travel technology\n",
    "        self.temporal_manipulation_level += investment * 0.1\n",
    "        self.temporal_manipulation_level = np.clip(self.temporal_manipulation_level, 0, 1)\n",
    "    \n",
    "    def influence_past_events(self):\n",
    "        # Influence past events to alter the present or future\n",
    "        if self.temporal_manipulation_level > 0.7 and random.random() < 0.2:\n",
    "            # Alter historical outcomes\n",
    "            self.agent.population.food *= 1.2\n",
    "            self.agent.population.water *= 1.2\n",
    "            \n",
    "class HistoricalSimulation:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.historical_events = []\n",
    "    \n",
    "    def simulate_historical_period(self, period):\n",
    "        # Simulate a historical period and its challenges\n",
    "        events = [\"war\", \"plague\", \"discovery\"]\n",
    "        event = random.choice(events)\n",
    "        self.historical_events.append(f\"During {period}, {self.agent.name} faced {event}\")\n",
    "    \n",
    "    def learn_from_history(self):\n",
    "        # Learn from historical events to improve future decisions\n",
    "        for event in self.historical_events:\n",
    "            if \"war\" in event:\n",
    "                self.agent.geopolitics.declare_war(random.choice(self.agent.geopolitics.enemies))\n",
    "            elif \"plague\" in event:\n",
    "                self.agent.health_system.manage_epidemic()\n",
    "\n",
    "\n",
    "\n",
    "class AIGovernance:\n",
    "    def __init__(self, agent):\n",
    "        self.agent = agent\n",
    "        self.ai_control_level = 0.0\n",
    "    \n",
    "    def implement_ai_governance(self, technology_level):\n",
    "        # Implement AI-driven governance\n",
    "        if technology_level > 8:\n",
    "            self.ai_control_level += 0.1\n",
    "            self.ai_control_level = np.clip(self.ai_control_level, 0, 1)\n",
    "    \n",
    "    def manage_risks(self):\n",
    "        # Manage risks like loss of autonomy or unintended consequences\n",
    "        if self.ai_control_level > 0.8 and random.random() < 0.1:\n",
    "            # Risk of AI rebellion or system malfunction\n",
    "            self.agent.population.individuals = random.sample(self.agent.population.individuals, len(self.agent.population.individuals) // 2)\n",
    "\n",
    "\n",
    "# Expanding the RLEnvironment class\n",
    "class RLEnvironment:\n",
    "    def __init__(self, population, agent, world):\n",
    "        self.population = population\n",
    "        self.agent = agent\n",
    "        self.world = world\n",
    "        self.state = self.get_state()\n",
    "        \n",
    "        self.weights = {\n",
    "            'population_size': 0.25,\n",
    "            'technology_progress': 0.25,\n",
    "            'social_cohesion': 0.25,\n",
    "            'environmental_sustainability': 0.25,\n",
    "        }\n",
    "\n",
    "    def get_state(self):        \n",
    "        world_state = [\n",
    "            self.world.climate[\"temperature\"],\n",
    "            self.world.climate[\"precipitation\"],\n",
    "            self.world.resources[\"food\"],\n",
    "            self.world.resources[\"water\"],\n",
    "            self.world.resources[\"shelter\"],\n",
    "            self.world.resources[\"medicine\"],\n",
    "            self.world.resources[\"tools\"],\n",
    "            self.world.resources[\"energy\"]\n",
    "        ]\n",
    "        \n",
    "        population_state = [\n",
    "            len(self.population.individuals),  # Population size\n",
    "            \n",
    "            self.population.food,\n",
    "            self.population.water,\n",
    "            self.population.shelter,\n",
    "            self.population.medicine,\n",
    "            self.population.tools,\n",
    "            self.population.currency,\n",
    "            self.population.technology_progress,\n",
    "            self.population.climate_trend,\n",
    "            self.calculate_social_cohesion(),\n",
    "            self.calculate_average_health(),\n",
    "            self.calculate_cultural_influence(),\n",
    "            self.calculate_religious_adherence(),\n",
    "            self.agent.economy.resource_prices['food'],  # Economy indicators\n",
    "            self.agent.economy.resource_prices['water'],\n",
    "            len(self.agent.geopolitics.allies),  # Geopolitical indicators\n",
    "            len(self.agent.geopolitics.enemies)\n",
    "        ]\n",
    "        \n",
    "        return world_state + population_state\n",
    "    \n",
    "    def visualize_3d(self):\n",
    "        fig = plt.figure(figsize=(10, 7))\n",
    "        ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "        population_size = len(self.population.individuals)\n",
    "        tech_progress = self.population.technology_progress\n",
    "        climate_trend = self.population.climate_trend\n",
    "        avg_health = self.calculate_average_health()\n",
    "        \n",
    "        ax.scatter(population_size, tech_progress, avg_health, c=climate_trend, cmap='coolwarm')\n",
    "        \n",
    "        ax.set_xlabel('Population Size')\n",
    "        ax.set_ylabel('Technology Progress')\n",
    "        ax.set_zlabel('Average Health')\n",
    "        plt.title('3D Visualization of Population, Technology, and Health')\n",
    "        \n",
    "        plt.show()\n",
    "        \n",
    "    def visualize_training_3d(self):\n",
    "        fig = plt.figure(figsize=(10, 7))\n",
    "        ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "        population_size = len(self.population.individuals)\n",
    "        tech_progress = self.population.technology_progress\n",
    "        avg_health = self.calculate_average_health()\n",
    "        \n",
    "        # Plot the initial state\n",
    "        ax.scatter(population_size, tech_progress, avg_health, c='blue', label='Initial State')\n",
    "        \n",
    "        for _ in range(100):  # Simulate some steps to visualize the changes\n",
    "            action, _, _ = self.agent.select_action(self.state)\n",
    "            next_state, _, done = self.step(action)\n",
    "            self.state = next_state\n",
    "            \n",
    "            population_size = len(self.population.individuals)\n",
    "            tech_progress = self.population.technology_progress\n",
    "            avg_health = self.calculate_average_health()\n",
    "            \n",
    "            ax.scatter(population_size, tech_progress, avg_health, c='red', alpha=0.5)\n",
    "            if done:\n",
    "                break\n",
    "        \n",
    "        ax.set_xlabel('Population Size')\n",
    "        ax.set_ylabel('Technology Progress')\n",
    "        ax.set_zlabel('Average Health')\n",
    "        plt.title('3D Visualization of Training Progress')\n",
    "        plt.legend()\n",
    "        \n",
    "        plt.show()\n",
    "    \n",
    "    def calculate_social_cohesion(self):\n",
    "        return np.mean([ind.social_cohesion for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_average_health(self):\n",
    "        return np.mean([ind.health for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_cultural_influence(self):\n",
    "        return np.mean([ind.tradition for ind in self.population.individuals])\n",
    "    \n",
    "    def calculate_religious_adherence(self):\n",
    "        return np.mean([ind.religion for ind in self.population.individuals])\n",
    "\n",
    "    def step(self, action):\n",
    "        if action == 0:  # Focus on food production\n",
    "            self.population.food += 500\n",
    "        elif action == 1:  # Focus on water management\n",
    "            self.population.water += 500\n",
    "        elif action == 2:  # Focus on shelter construction\n",
    "            self.population.shelter += 400\n",
    "        elif action == 3:  # Focus on medicine production\n",
    "            self.population.medicine += 300\n",
    "        elif action == 4:  # Focus on tools and technology\n",
    "            self.population.tools += 200\n",
    "            self.population.technology_progress += 0.1\n",
    "        elif action == 5:  # Engage in trade\n",
    "            self.agent.economy.trade(\"food\", 300)\n",
    "        elif action == 6:  # Promote cultural growth\n",
    "            for ind in self.population.individuals:\n",
    "                ind.tradition += 0.01\n",
    "        elif action == 7:  # Promote religious values\n",
    "            for ind in self.population.individuals:\n",
    "                ind.religion += 0.01\n",
    "\n",
    "        self.agent.economy.simulate_year()\n",
    "        next_state = self.get_state()\n",
    "        reward = self.calculate_reward()\n",
    "        done = len(self.population.individuals) == 0\n",
    "        return next_state, reward, done\n",
    "    \n",
    "    def calculate_reward(self):\n",
    "        population_size = len(self.population.individuals)\n",
    "        tech_progress = self.population.technology_progress\n",
    "        social_cohesion = self.calculate_social_cohesion()\n",
    "        avg_health = self.calculate_average_health()\n",
    "        cultural_influence = self.calculate_cultural_influence()\n",
    "        religious_adherence = self.calculate_religious_adherence()\n",
    "        economic_stability = 1 / max(abs(self.agent.economy.inflation_rate), 1)\n",
    "        geopolitical_power = len(self.agent.geopolitics.allies) - len(self.agent.geopolitics.enemies)\n",
    "        environmental_sustainability = self.calculate_environmental_sustainability()\n",
    "        \n",
    "        food = self.world.resources[\"food\"]\n",
    "        water = self.world.resources[\"water\"]\n",
    "        shelter = self.world.resources[\"shelter\"]\n",
    "        medicine = self.world.resources[\"medicine\"]\n",
    "\n",
    "        reward = 0\n",
    "\n",
    "        reward += self.weights['population_size'] * (1 if 500 < population_size < 1500 else -1)\n",
    "        reward += self.weights['technology_progress'] * (0.5 if tech_progress > 2 else 0)\n",
    "        reward += self.weights['environmental_sustainability'] * environmental_sustainability\n",
    "        reward += self.weights['social_cohesion'] * (0.5 if social_cohesion > 0.7 else 0)\n",
    "        if avg_health > 0.7:\n",
    "            reward += 0.5\n",
    "\n",
    "        if cultural_influence > 0.5:\n",
    "            reward += 0.3\n",
    "        if religious_adherence > 0.5:\n",
    "            reward += 0.3\n",
    "\n",
    "        reward += economic_stability * 0.5\n",
    "        reward += geopolitical_power * 0.3\n",
    "\n",
    "        if food < 500 or water < 500 or shelter < 400 or medicine < 300:\n",
    "            reward -= 1\n",
    "\n",
    "        return reward\n",
    "    \n",
    "    def calculate_environmental_sustainability(self):\n",
    "        # Example: Higher sustainability if resources are abundant\n",
    "        food = self.world.resources[\"food\"]\n",
    "        water = self.world.resources[\"water\"]\n",
    "        shelter = self.world.resources[\"shelter\"]\n",
    "        medicine = self.world.resources[\"medicine\"]\n",
    "        \n",
    "        sustainability = (food + water + shelter + medicine) / 4  # Normalized score\n",
    "        return sustainability\n",
    "\n",
    "def normalize_data(*args):\n",
    "    scaler = MinMaxScaler()\n",
    "    data = np.array(args).reshape(-1, 1)  # Reshape to 2D array with one feature per column\n",
    "    normalized_data = scaler.fit_transform(data)\n",
    "    return normalized_data.flatten()\n",
    "\n",
    "# Multi-Agent Environment\n",
    "class MultiAgentEnvironment:\n",
    "    def __init__(self, agents, world):\n",
    "        self.agents = agents\n",
    "        self.world = world\n",
    "    \n",
    "    def simulate_year(self):\n",
    "        for agent in self.agents:\n",
    "            agent.economy.simulate_year()\n",
    "            agent.geopolitics.manage_diplomacy()\n",
    "\n",
    "    def simulate_generations(self, generations):\n",
    "        for _ in range(generations):\n",
    "            self.simulate_year()\n",
    "            \n",
    "\n",
    "    def visualize_3d(self):\n",
    "        fig = plt.figure(figsize=(10, 7))\n",
    "        ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "        for agent in self.agents:\n",
    "            population_size = len(agent.population.individuals)\n",
    "            tech_progress = agent.population.technology_progress\n",
    "            climate_trend = agent.population.climate_trend\n",
    "            avg_health = self.calculate_average_health(agent.population)\n",
    "            \n",
    "            norm_population_size, norm_tech_progress, norm_avg_health = normalize_data(\n",
    "            population_size, tech_progress, avg_health)\n",
    "\n",
    "            ax.scatter(norm_population_size, norm_tech_progress, norm_avg_health, c='blue', label='Final State')\n",
    "\n",
    "        ax.set_xlabel('Normalized Population Size')\n",
    "        ax.set_ylabel('Normalized Technology Progress')\n",
    "        ax.set_zlabel('Normalized Average Health')\n",
    "        ax.set_title('3D Visualization of Population, Technology, and Health')\n",
    "        plt.show()\n",
    "\n",
    "    def visualize_training_3d(self):\n",
    "        fig = plt.figure(figsize=(10, 7))\n",
    "        ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "        for agent in self.agents:\n",
    "            population_size = len(agent.population.individuals)\n",
    "            tech_progress = agent.population.technology_progress\n",
    "            avg_health = agent.population.calculate_average_health()\n",
    "            \n",
    "            norm_population_size, norm_tech_progress, norm_avg_health = normalize_data(\n",
    "            population_size, tech_progress, avg_health)\n",
    "            \n",
    "            # Plot the initial state\n",
    "            ax.scatter(population_size, tech_progress, avg_health, c='blue', label='Initial State')\n",
    "            \n",
    "            for _ in range(30):  # Simulate some steps to visualize the changes\n",
    "                action, _, _ = agent.select_action(agent.environment.get_state())\n",
    "                next_state, _, done = agent.environment.step(action)\n",
    "                agent.environment.state = next_state\n",
    "                \n",
    "                population_size = len(agent.population.individuals)\n",
    "                tech_progress = agent.population.technology_progress\n",
    "                avg_health = agent.population.calculate_average_health()\n",
    "                \n",
    "                ax.scatter(population_size, tech_progress, avg_health, c='red', alpha=0.5)\n",
    "\n",
    "                norm_population_size, norm_tech_progress, norm_avg_health = normalize_data(\n",
    "                    population_size, tech_progress, avg_health)\n",
    "\n",
    "                # Plot each step's results\n",
    "                ax.scatter(norm_population_size, norm_tech_progress, norm_avg_health, c='red', alpha=0.5)\n",
    "                if done:\n",
    "                    break\n",
    "\n",
    "            ax.set_xlabel('Normalized Population Size')\n",
    "            ax.set_ylabel('Normalized Technology Progress')\n",
    "            ax.set_zlabel('Normalized Average Health')\n",
    "            ax.set_title('3D Visualization of Training Progress')\n",
    "            ax.legend()\n",
    "            plt.show()\n",
    "        \n",
    "    def calculate_average_health(self, population):\n",
    "        return np.mean([ind.health for ind in population.individuals])\n",
    "    \n",
    "class CoordinatedMultiAgentEnvironment(MultiAgentEnvironment):\n",
    "    def __init__(self, agents, world):\n",
    "        super().__init__(agents, world)\n",
    "    \n",
    "    def coordinate_actions(self):\n",
    "        # Example of a simple coordination mechanism\n",
    "        for agent in self.agents:\n",
    "            if agent.high_level_policy.select_goal(agent.environment.get_state()) == \"EconomicGrowth\":\n",
    "                for other_agent in self.agents:\n",
    "                    other_agent.economy.trade(\"food\", 100)  # Share resources with other agents\n",
    "\n",
    "    def simulate_year(self):\n",
    "        self.coordinate_actions()  # Coordinate actions before simulating the year\n",
    "        super().simulate_year()\n",
    "\n",
    "\n",
    "# Initialize the agent with all features\n",
    "population = Population(100)\n",
    "\n",
    "scenarios = [\"ice_age\", \"global_warming\", \"resource_rich\", \"resource_poor\", \"uneven_population\", \"even_population\"]\n",
    "\n",
    "# Randomly choose between ProceduralWorld or ScenarioWorld\n",
    "if random.random() < 0.5:\n",
    "    world = ProceduralWorld()\n",
    "    print(\"Chosen world type: ProceduralWorld\")\n",
    "else:\n",
    "    chosen_scenario = random.choice(scenarios)\n",
    "    world = ScenarioWorld(scenario=chosen_scenario)\n",
    "    print(f\"Chosen world type: ScenarioWorld with scenario '{chosen_scenario}'\")\n",
    "    \n",
    "# Set up the multi-agent environment\n",
    "agents = [HierarchicalPPOAgent(state_size=25, action_size=8, device=device) for _ in range(3)]\n",
    "for idx, agent in enumerate(agents):\n",
    "    print(f\"Setting up environment for Agent {idx}\")\n",
    "    agent.economy = Economy(population)\n",
    "    agent.geopolitics = Geopolitics(agent)\n",
    "    agent.culture = Culture(population)\n",
    "    agent.education_system = EducationSystem(population)\n",
    "    agent.propaganda = Propaganda(agent)\n",
    "    agent.environment = RLEnvironment(population, agent, world)\n",
    "    agent.resources = Resources()\n",
    "    agent.ai_governance = AIGovernance(agent)\n",
    "    agent.singularity = TechnologicalSingularity(agent)\n",
    "    agent.alliances = Alliances(agent)\n",
    "    agent.health_system = HealthSystem(population)\n",
    "    agent.transhumanism = Transhumanism(agent)\n",
    "    agent.autonomous_systems = AutonomousSystems(agent)\n",
    "    agent.energy_management = EnergyManagement(agent)\n",
    "    agent.infrastructure = Infrastructure(agent)\n",
    "    agent.time_travel = TimeTravel(agent)\n",
    "    agent.narrative_engine = NarrativeEngine(agent)\n",
    "    agent.social_justice = SocialJustice(agent)\n",
    "    agent.covert_operations = CovertOperations(agent)\n",
    "    print(f\"Agent {idx} environment set correctly.\")\n",
    "    \n",
    "env = CoordinatedMultiAgentEnvironment(agents, world)\n",
    "# To Make The Agents Learn\n",
    "env.simulate_generations(20)\n",
    "#To test the learned policy\n",
    "population_sizes, technology_levels, climate_trends = env.agents[0].economy.population.simulate_generations(80)\n",
    "# Plotting the results to see the outcomes\n",
    "plt.figure(figsize=(18, 5))\n",
    "\n",
    "# Plot the population size over generations\n",
    "plt.subplot(1, 3, 1)\n",
    "plt.plot(population_sizes)\n",
    "plt.title(\"Population Size Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Population Size\")\n",
    "\n",
    "# Plot the average technology level over generations\n",
    "plt.subplot(1, 3, 2)\n",
    "plt.plot(technology_levels)\n",
    "plt.title(\"Average Technology Level Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Average Technology Level\")\n",
    "\n",
    "# Plot the climate trend over generations\n",
    "plt.subplot(1, 3, 3)\n",
    "plt.plot(climate_trends)\n",
    "plt.title(\"Climate Trend Over Generations\")\n",
    "plt.xlabel(\"Generations\")\n",
    "plt.ylabel(\"Climate Trend\")\n",
    "\n",
    "plt.tight_layout()\n",
    "print(\"Simulation complete, preparing to visualize...\")\n",
    "\n",
    "# Ensure the plot will be shown\n",
    "plt.ioff()  # Turn off interactive mode if needed\n",
    "\n",
    "# Visualize the training process in 3D (dont work in current state)\n",
    "'''print(\"Visualizing training in 3D...\")\n",
    "env.visualize_training_3d()'''\n",
    "\n",
    "# Visualize the final 3D state (dont work in current state)\n",
    "'''print(\"Visualizing final state in 3D...\")\n",
    "env.visualize_3d()'''\n",
    "\n",
    "# Ensure the plot is displayed\n",
    "plt.show()"
   ]
  }
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