exemplars.py

# Copyright (c) 2012 Stellenbosch University, 2012
# This source code is released under the Academic Free License 3.0
# See https://github.com/gvrooyen/SocialLearning/blob/master/LICENSE for the full text of the license.
# Author: G-J van Rooyen <gvrooyen@sun.ac.za>

"""
This module contains some examplary state graphs, that are likely to give good initial results when
simulated. These are intended to be used to seed the initial genetic programming population.
"""

import traits

def BifurcateDiscrete():

	self_traits = {}

	self_traits['PioneeringBi'] = traits.PioneeringBi.PioneeringBi()
	self_traits['PioneeringBi'].N_rounds = 3

	self_traits['Specialisation'] = traits.Specialisation.Specialisation()
	self_traits['SpecialisationB'] = traits.SpecialisationB.SpecialisationB()

	self_traits['DiscreteDistribution'] = traits.DiscreteDistribution.DiscreteDistribution()
	self_traits['DiscreteDistributionB'] = traits.DiscreteDistributionB.DiscreteDistributionB()
	self_traits['DiscreteDistributionC'] = traits.DiscreteDistributionC.DiscreteDistributionC()
	self_traits['DiscreteDistributionD'] = traits.DiscreteDistributionD.DiscreteDistributionD()
	self_traits['DiscreteDistributionE'] = traits.DiscreteDistributionE.DiscreteDistributionE()
	self_traits['DiscreteDistributionF'] = traits.DiscreteDistributionF.DiscreteDistributionF()
	self_traits['DiscreteDistributionG'] = traits.DiscreteDistributionG.DiscreteDistributionG()
	self_traits['DiscreteDistributionH'] = traits.DiscreteDistributionH.DiscreteDistributionH()

	state = [('PioneeringBi', ['Specialisation', 'SpecialisationB']),
	         ('Specialisation', ['DiscreteDistribution','DiscreteDistributionB','DiscreteDistributionC','DiscreteDistributionD']),
	         ('SpecialisationB', ['DiscreteDistributionE','DiscreteDistributionF','DiscreteDistributionG','DiscreteDistributionH']),
	         ('DiscreteDistribution', []),
	         ('DiscreteDistributionB', []),
	         ('DiscreteDistributionC', []),
	         ('DiscreteDistributionD', []),
	         ('DiscreteDistributionE', []),
	         ('DiscreteDistributionF', []),
	         ('DiscreteDistributionG', []),
	         ('DiscreteDistributionH', []),
	        ]
	
	return (self_traits, state)


def Simple():

	self_traits = {}

	self_traits['Pioneering'] = traits.Pioneering.Pioneering()
	self_traits['DiscreteDistribution'] = traits.DiscreteDistribution.DiscreteDistribution()

	state = [('Pioneering', ['DiscreteDistribution']),
		     ('DiscreteDistribution', [])
		    ]

	return (self_traits, state)


def SpecialPioneers():

	self_traits = {}

	self_traits['PioneeringBi'] = traits.PioneeringBi.PioneeringBi()

	# Pioneers should emphasize innovation
	self_traits['DiscreteDistributionB'] = traits.DiscreteDistributionB.DiscreteDistributionB()

	# Non-pioneers should emphasize exploitation
	self_traits['DiscreteDistributionD'] = traits.DiscreteDistributionD.DiscreteDistributionD()

	state = [('PioneeringBi', ['DiscreteDistributionB', 'DiscreteDistributionD']),
		     ('DiscreteDistributionB', []),
		     ('DiscreteDistributionD', [])
		    ]
		    
	return (self_traits, state)


def ContinuousProfessionalDevelopment():
	"""
	After the pioneering stage, pioneers immediately start exploiting their strongest act, until payoffs
	drop, at which stage they go into a study phase, then back to greedy exploitation.

	Non-pioneers start with a study phase, then go into greedy exploitation, and back to study as necessary.
	"""

	self_traits = {}

	T = traits.PioneeringBi.PioneeringBi()
	T.N_rounds = 14
	self_traits['PioneeringBi'] = T

	self_traits['ExploitGreedy'] = traits.ExploitGreedy.ExploitGreedy()

	T = traits.Study.Study()
	T.N_rounds = 14
	T.Pi = 0.6465
	T.Po = 0.2549
	T.Pr = 0.6606
	self_traits['Study'] = T

	self_traits['ExploitGreedyB'] = traits.ExploitGreedyB.ExploitGreedyB()

	T = traits.StudyB.StudyB()
	T.N_rounds = 0
	T.Pi = 0.1487
	T.Po = 0.1435
	T.Pr = 0.3003
	self_traits['StudyB'] = T

	state = [('PioneeringBi', ['ExploitGreedy', 'StudyB']),
		     ('ExploitGreedy', ['Study']),
		     ('Study', ['ExploitGreedy']),
		     ('ExploitGreedyB', ['StudyB']),
		     ('StudyB', ['ExploitGreedyB']),
		    ]

	return (self_traits, state)


def Beatnik():

	self_traits = {}

	T = traits.Pioneering.Pioneering()
	T.N_rounds = 8
	self_traits['Pioneering'] = T

	T = traits.InnovationBeat.InnovationBeat()
	T.N_Seq = 8
	T.Pa = 0.4187
	T.seq_A = [1, 1, 1, 1, 1, 2, 1, 1, 1, 1]
	T.seq_B = [0, 0, 2, 1, 1, 1, 1, 1, 0, 2]
	self_traits['InnovationBeat'] = T

	state = [('Pioneering', ['InnovationBeat']),
		     ('InnovationBeat', [])
		    ]

	return (self_traits, state)


def BeatnikInSpace():
	self_traits = {}

	T = traits.Pioneering.Pioneering()
	T.N_rounds = 8
	self_traits['Pioneering'] = T

	T = traits.InnovationBeatSpatial.InnovationBeatSpatial()
	T.N_Seq = 8
	T.Pa = 0.4187
	T.seq_A = [1, 1, 1, 1, 1, 1, 1, 1, 2, 1]
	T.seq_B = [0, 1, 2, 1, 1, 1, 1, 1, 1, 1]
	self_traits['InnovationBeatSpatial'] = T

	T = traits.Study.Study()
	T.N_rounds = 14
	T.Pi = 0.6145
	T.Po = 0.2549
	T.Pr = 0.6606
	self_traits['Study'] = T

	state = [('Pioneering', ['InnovationBeatSpatial']),
		     ('InnovationBeatSpatial', ['Study']),
		     ('Study', ['InnovationBeatSpatial'])
		    ]

	return (self_traits, state)


def GoodReferences():
	self_traits = {}

	self_traits['Pioneering'] = traits.Pioneering.Pioneering()

	T = traits.Reference.Reference()
	T.Pr = 0.05
	T.N_innovate = 1
	T.threshold = 1.0
	self_traits['Reference'] = T

	state = [('Pioneering', ['Reference']),
			 ('Reference', [])
	]

	return (self_traits, state)


exemplar_list = [BifurcateDiscrete, Simple, SpecialPioneers, ContinuousProfessionalDevelopment, Beatnik, BeatnikInSpace, GoodReferences]