Andre2011.nlogo

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

; refractory state is equal to paired = 1
; susceptible state is equal to paired = 0

to setup
  clear-all
  ;;set seed new-seed ;; pick random number for seed, comment this line to always run with the same seed (for debug for example)
  
  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 p pStart set q qStart
    set breed parent
  ]
  reset-ticks
end

to go
  if ticks = (numberOfGenerations + 1) 
  [ 
    stop 
  ]
  resetValues ;; reset turtle variables
  playUG  ;; turtles play ultimatum games
  
  update-plots
  reproduceAndDie ;; turtles reproduce and die
  tick-advance 1
end

to resetValues
  ask turtles [
    set cumulatedPayoff 0 ;; payoff since the beginning of life
    set numberOfOffsprings 1
    set breed parent
    set paired 1 ;; paired = 1 means in refractory period
    set drawn 1 ;; drawn = 0 means not yet draw to play the UG when paired = 0
    set t 0 ;; number of time steps since an individual switched from paired = 1 to paired = 0
  ]
end

to playUG
  let numberOfSteps 0 let offer -1 let request -1
  let proposer one-of turtles let responder one-of turtles
  while [numberOfSteps < numberStepsL] ; timesteps 
  [
    ask turtles with [paired = 1] [if random-float 1 < rho [set paired 0]] ;; switch from refractory to susceptible state
    ask turtles with [paired = 0] [set drawn 0] ;; to know what turtles have already been drawn
    while [count turtles with [drawn = 0] > 1] ;; we randomly pair all turtles
    [
      set proposer one-of turtles with [drawn = 0]
      ask proposer [ set drawn 1 ]
      set responder one-of turtles with [drawn = 0]
      ask responder [ set drawn 1]
      
      set offer [p] of proposer ;; important to have this, working on [p] or [q] directly increases the risk to make errors on whose p is it
      set request [q] of responder
      if offer >= request
      [
        ask proposer [set cumulatedPayoff (cumulatedPayoff + ((1 - offer) * (delta ^ t))) set paired 1 set t 0]
        ask responder [set cumulatedPayoff (cumulatedPayoff + (offer * (delta ^ t))) set paired 1 set t 0]
      ]
    ]
    ask turtles with [paired = 0] [set t (t + 1)] ;; number of time steps past since an individual moved from paired = 1 to paired = 0
    set numberOfSteps (numberOfSteps + 1)
  ]
  ask turtles [set cumulatedPayoff cumulatedPayoff + nonSocialPayoff]
end



to reproduceAndDie
  
  let averagePayoff mean [cumulatedPayoff] of turtles  
  let offspringTable []
  let offspringTableTurtle []
  if averagePayoff != 0 [
    ask turtles [ set numberOfOffsprings round (500 * cumulatedPayoff / averagePayoff)] ;; sets number of offspring for each turtle
  ]
  
  ask turtles [
    set offspringTableTurtle n-values numberOfOffsprings [who] ;; creates a table containing as many cells as offspring, each cell containing the "who" of the parent
    set offspringTable (sentence offspringTable offspringTableTurtle) ;; concatenates to the previous table
  ]
  
  let newGeneration n-values populationSize [one-of offspringTable]   ;; random drawing with replacement in the above table
                                                                      
  (foreach newGeneration
    [
      ask turtle ? [hatch-offspring 1] ;; parents produce offsprings (in the breed offspring)
    ]) 
  ask turtles with [breed = parent] [die] ;; parents die
  
  ask turtles [ ;; mutations on offspring
    setxy random-xcor random-ycor ;; to be able to right click on a turtle
    set p applyMutation p
    set q applyMutation q
  ]
end



to-report applyMutation [gene]
  ifelse random-float 1 <= mutationRate [ report mutatedValue gene ][ report gene ]
end

to-report mutatedValue [gene]
  ifelse random-float 1 <= probabilityUniformMutation 
    [ ;; the new value comes from a uniform distribution
      set gene (random-float 1)
    ]
    [ ;; the new value comes from a normal distribution
      set gene gene + random-normal 0 mutationStd
    ]
  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
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INPUTBOX
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Number

@#$#@#$#@
## WHAT IS IT?

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

1. André, J.-B. & Baumard, N. Social opportunities and the evolution of fairness. J. Theor. Biol. 289, 128–35 (2011). http://www.ncbi.nlm.nih.gov/pubmed/21907216

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

If the simulation runs slowly, try decreasing the population size (and mutation rate accordingly).

Non explicit parameters in the interface:

**seed**: displays the random seed used to initialize the model: write it down for replication purposes.
**pStart and qStart**: initial values for p and q
**probabilityUniformMutation**: probability that the mutation will be drawn in a uniform law between 0 and 1; otherwise normal law.
**numberStepsL**: number of steps in the lifespan (L parameter in the original paper)
**delta**: cost of postponing the interaction
**rho**: probability to move from refractory state to susceptible state
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@#$#@#$#@
NetLogo 5.1.0
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