Rand2013.nlogo

turtles-own [p q cumulatedPayoff numberOfOffsprings paired proba]
globals []
breed [offsprings offspring]
breed [parents parent]

;; Fig 1 in the original paper is obtained with u = 0.0562 and w = 0.0316, w = 0.1, w = 0.316 or w = 100


to setup
  clear-all
  ;;set seed new-seed ;; pick random number for seed, comment this line to always use the same seed
  
  random-seed seed  ;; set the seed of the random generator to seed
  
  create-turtles populationSize [ setxy random-xcor random-ycor ]
  ask turtles [
    set shape "person"
    set breed parents
  ]
  
  ask turtles [set p random-float 1 set q random-float 1]
  
  reset-ticks
end

to go
  if ticks = (numberOfGenerations + 1) 
  [ 
    stop 
  ]
  resetValues
  playUG
  
  
  reproduceAndDie
  update-plots
  tick-advance 1
end

to resetValues
  ask turtles [
    set cumulatedPayoff 0 ;; payoff since the beginning of life
    set numberOfOffsprings 1
    set breed parents
    set paired 0
  ]
end

to playUG
  let numberOfGames 0 let offer -1 let request -1 let myWho -1 let mySet []
  let proposer one-of turtles let responder one-of turtles
  foreach [who] of turtles ;; each turtle plays as proposer against all other turtles, which means that each turtle plays once as proposer and once as responder against each other turtle
  [
    set offer [p] of turtle ?
    set myWho [who] of turtle ?
    
    (foreach [who] of turtles with [who != myWho]
      [
        set request [q] of turtle ?
        if offer >= request
        [
          ask turtle myWho [set cumulatedPayoff (cumulatedPayoff + 1 - offer)]
          ask turtle ? [set cumulatedPayoff (cumulatedPayoff + offer)]
        ]        
        
      ]) 
  ]
  
end



to-report mutatedValue [gene]
  
  set gene (random-float 1)
  
  if gene > 1 [set gene 1] ;; if we go outside the bounds
  if gene < 0 [set gene 0]  ;; if we go outside the bounds
  
  report gene
end


to reproduceAndDie
  
  let averagePayoff mean [cumulatedPayoff] of turtles  
  let offspringTable []
  let offspringTableTurtle []
  if averagePayoff != 0 [
    ask turtles [ 
      set cumulatedPayoff (cumulatedPayoff / (2 * populationSize - 2)) ;; average payoff for each agent
      set numberOfOffsprings round (exp (cumulatedPayoff * w))  ;; w is the strength of selection
    ]
  ]
  
  ask turtles [
    set offspringTableTurtle n-values numberOfOffsprings [who] ;; create a table containing as many cases as offspring, with the who of the parent inside
    set offspringTable (sentence offspringTable offspringTableTurtle) ;; concatenate to the previous table
  ]
  
  let willReproduce one-of offspringTable   ;; draw one individual to reproduce
  
  ask turtle willReproduce [hatch-offsprings 1] ;; parents make offsprings in the breed offspring
  let willDie one-of parents
  ask willDie [die]  
  
  ask offsprings [ 
    setxy random-xcor random-ycor ;; to be able to right click on a turtle
    if random-float 1 <= u ;; u mutation rate in original paper
    [ 
      set p mutatedValue p
      set q mutatedValue q
    ]
  ]
  
end
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@#$#@#$#@
## WHAT IS IT?

This model is a replication of the model of the evolution of human fairness presented in the following paper:

1. Rand, D. G., Tarnita, C. E., Ohtsuki, H. & Nowak, M. a. Evolution of fairness in the one-shot anonymous Ultimatum Game. Proc. Natl. Acad. Sci. U. S. A. 110, 2581–6 (2013). http://www.pnas.org/content/110/7/2581.abstract

This replication is provided for comparison purposes as part of the review paper:

Debove, S., et al., Models of the evolution of fairness in the ultimatum game: a review and classification, Evolution and Human Behavior (2015), http://www.ehbonline.org/article/S1090-5138%2816%2900003-9/abstract 

The present Netlogo replication of the original model has thus NOT been created, reviewed or endorsed by any authors of the original paper. No guarantee is provided that the original model has been replicated with 100% accuracy.

For any question about the Netlogo code in this replication, please contact the first author of the review at email address     sd ((AT)) stephanedebove ((dot)) net     or on the following website : http://stephanedebove.net/contact

Download all models of the review at http://stephanedebove.net/?p=239 , or fork them on https://github.com/BigNoob/fairness-netlogo/


## HOW IT WORKS

See methods in the above mentioned original paper or in the supplementary information of the review.


## HOW TO USE IT

Default parameter values when you download the model correspond to the values used in the original paper. 


Non explicit parameters in the interface:

**seed**: displays the random seed used to initialize the model: write it down for replication purposes.
**w**: intensity of selection
**u**: mutation rate


Try changing the u and w parameters to see how their value affects the evolution of fairness.

In the original paper, Fig 1 is obtained with u = 0.0562 and w = 0.0316, w = 0.1, w = 0.316 or w = 100
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