TrustSim.nlogo

;;Based on the the Smaldino model

;;TURTLE ATTRIBUTES----------------------------------------------------------------------------------------------

;;added 3 additional attributes: n_total, n_betrayed and trust
turtles-own[
 energy       ;turtles accumulated energy
 played?      ;has the turtle played this round?

  trust       ;; a list of the outcomes for interactions where the agent cooperates
              ;; an interaction is 1 for CC, 0 for CD
              ;; average of interactions is the "trust"
]

;;GLOBAL PARAMETERS----------------------------------------------------------------------------------------------

;;added 3 additional parameters: init-trust, init-coop-freq and init-rational-freq
globals[
  reproduce-threshold
  reproduce-cost
  payoff-CC
  payoff-DC
  payoff-DD
  max-energy
  init-trust             ;; prior belief that other agent's will co-operate
  init-coop-freq         ;; initial proportion of cooperative agents
  init-rational-freq     ;; initial proportion of rational (defecting) agents
]

;;SETUP PROCEDURES----------------------------------------------------------------------------------------------

;;Similar to the original one, except initialize-variables and create-agents
to setup
  clear-all
  ask patches [set pcolor white]
  initialize-variables
  create-agents num-agents init-adaptive-freq init-coop-freq init-rational-freq
  reset-ticks
end

;;VARIABLE INITIALIZATION----------------------------------------------------------------------------------------------

;;added three new variables: init-trust, init-coop-freq and init-rational-freq
to initialize-variables
  set init-trust (list 0.5 0.5 0.5 0.5 0.5)         ;; initialised to 0.5 representing no prior knowledge

  set init-coop-freq (0.5 -  init-adaptive-freq)    ;; set the cooperaters frequency based in the frequency of adaptive
  set init-rational-freq 0.5                        ;; set frequency of rational agents

  ;;the rest of the variables are the same as the Smaldino ones
  set reproduce-threshold 100
  set reproduce-cost 50
  set payoff-CC 3
  set payoff-DC 5
  set payoff-DD 0
  set max-energy 150
end

;;CREATE AGENTS----------------------------------------------------------------------------------------------

;;changed the original method to incorporate three types of agents: rational, coop and adaptive
to create-agents [n a-freq c-freq r-freq ]
  create-turtles n[
    let r random-float 1

    set shape "circle"
    set size 1.0

    move-to one-of patches with [not any? other turtles-here]
    set energy (random 50 + 1)

    ;; initialize agents based on previously set parameters
    (ifelse
        (r < r-freq)[
          set color blue ;; rational
        ]
        (r < r-freq + a-freq)[
          set color red ;; adaptive
          set trust init-trust
        ]
        ;otherwise
        [
          set color yellow ;; coop
        ])
  ]
end

;;BEHAVIOR PROCEDURES----------------------------------------------------------------------------------------------

to go
  if not any? turtles [ stop ] ;stop the simulation if everyone is dead
  ask turtles [set played? false]

  ask turtles[
    if energy <= 0
      [die] ;;die if not enough energy

    ;;play PD game and collect payoffs
    if (not played?) [interact]

    ;;reproduce
    if (energy >= reproduce-threshold and (count turtles < carrying-capacity))
      [reproduce]

    ;;energy loss
    set energy (energy - cost-of-living)
    if energy > max-energy
      [ set energy max-energy ]

    ;;movement
    if (not played?)
      [ move2 ] ;;if didn't find coplayer, move
  ]

  if ticks >= 2000 [stop]
  tick
end

;;Find an interaction partner and play PD game with them
to interact
  let partner (one-of ((turtles-on neighbors) with [played? = false]))
  ;;the rest only runs if a partner is found
  if partner != nobody [
    set played? true
    ask partner [set played? true]
    let action "C"
    let p_action "C"

    ifelse (color = blue or (color = red and random-float 1 > mean trust))
    [set action "D"]
    [set action "C"]

    ifelse ([color] of partner = blue or ([color] of partner = red and random-float 1 > mean [trust] of partner))
    [set p_action "D"]
    [set p_action "C"]

      (ifelse
      (action = "C" and p_action = "C") [
          set energy (energy + payoff-CC)
          if color = red
          [
            if stm
            [set trust but-first trust]      ;; remove the oldest interaction if using short term memory
            set trust lput 1 trust
          ]
          ask partner [
            set energy (energy + payoff-CC)
            if [color] of partner = red
            [
              if stm
              [set trust but-first trust]
              set trust lput 1 trust
            ]
          ]
      ]
      (action = "C" and p_action = "D") [
        set energy (energy + payoff-CD)
          if color = red
          [
            if stm
            [set trust but-first trust]
            set trust lput 0 trust
          ]
        ask partner [
          set energy (energy + payoff-DC)
        ]
      ]
      (action = "D" and p_action = "C") [
        set energy (energy + payoff-DC)
        ask partner [
          set energy (energy + payoff-CD)
          if [color] of partner = red
          [
            if stm
            [set trust but-first trust]
            set trust lput 0 trust
          ]
        ]
      ]
      ;;otherwise
      [
        set energy (energy + payoff-DD)
        ask partner [
          set energy (energy + payoff-DD)
        ]
      ]
      )
  ]
end

;;move to a random new location if no one to play with
;;for now, just move to any available space in the Moore neigborhood.
to move
  let new-patch (one-of neighbors with [ not any? turtles-here ])
  if (new-patch != nobody)
  [ move-to new-patch ]
end

;;movement more in line with original Am Nat model
;;agent chooses square to move to, and only then checks if it's occupied
to move2
  let new-patch (one-of neighbors)
  if (not any? turtles-on new-patch)
  [ move-to new-patch ]
end

;;attempt to place a duplicate agent in a neighboring cell
to reproduce
  let new-patch (one-of neighbors with [ not any? turtles-here ])
  if (new-patch != nobody)
  [
    set energy (energy - reproduce-cost)
    hatch 1 [
      set energy reproduce-cost
      move-to new-patch
      set played? false
  ]
  ]
end

;;random agents lose energy if population exceeds carrying capacity
to cull-herd
  if (count turtles > carrying-capacity)
  [
    ask one-of turtles [
      set energy ( energy - 10 )
    ]
  ]
end


;;for plotting

to-report rep-rational-freq
  let Nb (count turtles with [ color = blue ])
  report Nb / (count turtles)
end


;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Copyright 2017 Paul Smaldino
;; See info tab for details
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
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@#$#@#$#@
## WHAT IS IT?

This model is based on the one  presented in Smaldino, Schank, & McElreath (2013) and Smaldino (2013). In the original model the agents play games with neighbors for energy payoffs, which they use to reproduce locally. This model builds on those features by introducing trust and reciprocity elements, as well as a new "adaptive" agent. We did not change any environment variables, hence being alive costs energy, and so agents must receive cooperation to stay alive. 

## HOW IT WORKS

In each simulation there is initially a total of 1000 agents and the carrying capacity of the environment is 4000. There is a moderate cost of living, in which all types of interactions are sustainable except DD and CD. 50% of the agents are rational, and the remaining agents are cooperative and adaptive agents. 

The initial proportion of adaptive agents is a<sub>0</sub>∈[0, 0.5] and the initial proportion of cooperative agents is 0.5−a<sub>0</sub>. On initialisation, the adaptive agents’ are assumed to have no prior knowledge of the behavior of any other agents, therefore the trust, t, is initialised as 0.5. Trust is then updated based on the proportion of interactions where the agent chooses to cooperate, and it’s partner reciprocates.

In the long term memory model (LTM), memory is updated indefinitely for all interactions, whereas in the short term model (STM), trust is based only on the last five interactions. 


## CREDITS AND REFERENCES

This model is a replication of one previously presented in the following two papers: 

Smaldino PE, Schank JC, McElreath R (2013) Increased costs of cooperation help cooperators in the long run. American Naturalist 181: 451-463.

Smaldino PE (2013) Cooperation in harsh environments and the emergence of spatial patterns. Chaos, Solitons, and Fractals 56: 6-12.

This NetLogo instatiation was created by Paul Smaldino in 2017.

## COPYRIGHT AND LICENSE

Copyright 2017 Paul Smaldino.

![CC BY-NC-SA 3.0](http://ccl.northwestern.edu/images/creativecommons/byncsa.png)

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License.  To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.
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