Model 2.tmp.nlogo

extensions [array]
;; agents have a probablity to reproduce and a strategy
turtles-own [
  cooperate-with-same? ;; probability agents will cooperate with the same color
  cooperate-with-different? ;; probability agents will cooperate with a different color
  raw-wealth
  scaled-wealth
  original
  interactable
]

globals [
  ;; the remaining variables support the replication of published experiments
  meet                  ;; how many interactions occurred this turn
  meet-agg              ;; how many interactions occurred through the run
  last100meet           ;; meet for the last 100 ticks
  meetown               ;; what number of individuals met someone of their own color this turn
  meetown-agg           ;; what number of individuals met someone of their own color throughout the run
  last100meetown        ;; meetown for the last 100 ticks
  meetother             ;; what number of individuals met someone of a different color this turn
  meetother-agg         ;; what number of individuals met someone of a different color throughout the run
  last100meetother      ;; meetother for the last 100 ticks
  coopown               ;; how many interactions this turn were cooperating with the same color
  coopown-agg           ;; how many interactions throughout the run were cooperating with the same color
  last100coopown        ;; coopown for the last 100 ticks
  coopother             ;; how many interactions this turn were cooperating with a different color
  coopother-agg         ;; how many interactions throughout the run were cooperating with a different color
  defother              ;; how many interactions this turn were defecting with a different color
  defother-agg          ;; how many interactions throughout the run were defecting with a different color
  last100defother       ;; defother for the last 100 ticks
  last100cc             ;; how many cooperate-cooperate genotypes have there been in the last 100 ticks
  last100cd             ;; how many cooperate-defect genotypes have there been in the last 100 ticks
  last100dc             ;; how many defect-cooperate genotypes have there been in the last 100 ticks
  last100dd             ;; how many defect-defect genotypes have there been in the last 100 ticks
  last100consist-ethno  ;; how many interactions consistent with ethnocentrism in the last 100 ticks
  last100coop           ;; how many interactions have been cooperation in the last 100 ticks
  mine                  ;; the wealth that an agent has
  yours                 ;; the wealth that another agent has while interacting
  wealth-list          ;; a list of turtles
  percentile75          ;; the 75th percentile of wealth
  percentile50          ;; the 50th percentile of wealth
  percentile25          ;; the 25th percentile of wealth
  northneighborcolor    ;; color of the neighbor in north
  eastneighborcolor     ;; color of the neighbor in east
  southneighborcolor    ;; color of the neighbor in south
  westneighborcolor     ;; color of the neighbor in west
  scale

]

to setup-empty
  clear-all
  initialize-variables
  reset-ticks
end

;; creates a world with an agent on each patch
to setup-full
  clear-all
  initialize-variables
  ask patches [ create-turtle ]
  reset-ticks
end

to initialize-variables
  ;; initialize all the variables
  set meetown 0
  set meetown-agg 0
  set meet 0
  set meet-agg 0
  set coopown 0
  set coopown-agg 0
  set defother 0
  set defother-agg 0
  set meetother 0
  set meetother-agg 0
  set coopother 0
  set coopother-agg 0
  set last100dd []
  set last100cd []
  set last100cc []
  set last100dc []
  set last100coopown []
  set last100defother []
  set last100consist-ethno []
  set last100meetown []
  set last100meetother []
  set last100meet []
  set last100coop []
  set mine 0
  set yours 0
  set wealth-list []
  set percentile75 0
  set percentile50 0
  set percentile25 0
  set scale 1

end

;; creates a new agent in the world
to create-turtle  ;; patch procedure
  sprout 1 [
    set raw-wealth random-normal 50 25
    if raw-wealth > 75 [ set color white]
    if raw-wealth <= 75 and raw-wealth > 50 [ set color green ]
    if raw-wealth <= 50 and raw-wealth > 25 [ set color yellow ]
    if raw-wealth <= 25 [ set color red ]
    set scaled-wealth raw-wealth
    set original raw-wealth
    ;; determine the strategy for interacting with someone of the same color
    set cooperate-with-same? (random-float 1.0 < chance-cooperate-with-same)
    ;; determine the strategy for interacting with someone of a different color
    set cooperate-with-different? (random-float 1.0 < chance-cooperate-with-different)
    ;; change the shape of the agent on the basis of the strategy
    set interactable 0

    update-shape

  ]

end

to-report random-color
  report one-of [red white yellow green]
end

;; this is used to clear stats that change between each tick
to clear-stats
  set meetown 0
  set meet 0
  set coopown 0
  set defother 0
  set meetother 0
  set coopother 0
  ;;set northneighborcolor 0
  ;;set eastneighborcolor 0
  ;;set southneighborcolor 0
  ;;set westneighborcolor 0
end

;; the main routine
to go
  clear-stats     ;; clear the turn based stats
  ;;immigrate       ;; new agents immigrate into the world
  ask turtles [update-state]
  ask turtles [resetraw]
  ;; reset the probability to reproduce
  ;;ask turtles [ set ptr initial-ptr ]
  set wealth-list []
  ;; have all of the agents interact with other agents if they can

  ask turtles [ interact ]
  ask turtles [addwealth]
  set wealth-list sort-by > wealth-list
  if length wealth-list > 0 [set scale first wealth-list]
  ask turtles[toscale]
  ;get-percentiles
  ;;ask turtles [colorandscale]
  ;; transact and then update your location
  ;;ask turtles with [ wealth > 0 ] [ transact ]
  ;; now they reproduce
  ;;ask turtles [ reproduce ]
  ;;death           ;; kill some of the agents
  update-stats    ;; update the states for the aggregate and last 100 ticks
  ;;ask turtles [recolor]
  recolor-turtles

  tick
  if ticks mod check-grades-every = 0 [
  ask turtles [
    mutate
  ]
]
  if ticks = 100 [stop]
end

to update-state
 if interactable > 0 [set interactable interactable - 1]
 let leave floor random-exponential 0.3  ;; maybe future use slider
 set interactable interactable + leave   
end
to resetraw
 set raw-wealth scaled-wealth
end
;to get-percentiles
 ; set percentile50 median wealth-list
  ;let ending length wealth-list
  ;let middle ending * .5
  ;let lower sublist wealth-list middle ending
  ;let upper sublist wealth-list 0 middle
  ;set percentile75 median upper
  ;set percentile25 median lower

;end

to addwealth
  set wealth-list fput raw-wealth wealth-list
end

to toscale
  set scaled-wealth raw-wealth / scale * 100
end
;; random individuals enter the world on empty cells
;;to immigrate
  ;;let empty-patches patches with [not any? turtles-here]
  ;; we can't have more immigrants than there are empty patches
  ;;let how-many min list immigrants-per-day (count empty-patches)
  ;;ask n-of how-many empty-patches [ create-turtle ]
;;end

;to recolor
 ; if raw-wealth > percentile75 [set color white]
  ;if raw-wealth <= percentile75 and raw-wealth > percentile50 [set color green]
  ;if raw-wealth <= percentile50 and raw-wealth > percentile25 [set color yellow]
  ;if raw-wealth <= percentile25 [set color red]
;end

;; random individuals enter the world on empty cells
;;to immigrate
  ;;let empty-patches patches with [not any? turtles-here]
  ;; we can't have more immigrants than there are empty patches
  ;;let how-many min list immigrants-per-day (count empty-patches)
  ;;ask n-of how-many empty-patches [ create-turtle ]
;;end

to transact
  ;; give a dollar to another turtle
  ;;set wealth wealth + 1
  ;;ask one-of other turtles [ set wealth wealth + 1 ]
  set mine raw-wealth
  ask one-of turtles-on neighbors4 [set yours raw-wealth]
  set raw-wealth raw-wealth  + yours * exchange_rate - raw-wealth * cost-of-giving
  ask one-of turtles-on neighbors4 [set raw-wealth raw-wealth + mine * exchange_rate - raw-wealth * cost-of-giving]
end

to interact  ;; turtle procedure
  let decider [[0 1] [1 0] [-1 0] [0 -1]]   ;; create list of locations
  set decider shuffle decider               ;; shuffle list
  
  while [interactable = 0 and length decider > 0]         ;; must still have locations and be interactable
  [
  let location first decider                              ;; take first location
    if (([interactable] of turtles-at first location last location) = 0)   ;; make sure other turtle interatcable
    [
      let neighborcolor ([color] of turtles-at first location last location) 
      set meet meet + 1
      set meet-agg meet-agg + 1
      if (neighborcolor = color)        
      [
           set meetown meetown + 1
           set meetown-agg meetown-agg + 1
          
          if (cooperate-with-same? and  ([cooperate-with-same?] of turtles-at first location last location))
          [   set coopown coopown + 1
              set coopown-agg coopown-agg + 1
              set mine raw-wealth
              set yours [raw-wealth] of turtles-at first location last location
              set raw-wealth raw-wealth  + yours * exchange_rate - mine * cost-of-giving   
              ask turtles-at first location last location                          ;; responder now gains
              [
                 set raw-wealth raw-wealth  + mine * exchange_rate - yours * cost-of-giving   
                 set interactable 1          
              ]
              set interactable 1     ;; no more interactions
          
          ] 
         
    ]
    if (neighborcolor != color)
    [
       set meetother meetother + 1
       set meetother-agg meetother-agg + 1
        if (cooperate-with-different? and  ([cooperate-with-different?] of turtles-at first location last location))
       [  
          set coopother coopother + 1
          set coopother-agg coopother-agg + 1
          set mine raw-wealth                             ;; wealth of turtle initiaiting
          set yours [raw-wealth] of turtles-at first location last location   ; weath of responder
          set raw-wealth raw-wealth  + yours * exchange_rate - mine * cost-of-giving   ;; new wealth of initiator   
          ask turtles-at first location last location                          ;; responder now gains
              [
                 set raw-wealth raw-wealth  + mine * exchange_rate - yours * cost-of-giving   
                 set interactable 1          
              ]
          set interactable 1     ;; no more interactions
          
       ] 
    
    ]  
   
  ]
  set decider remove location decider  ;;remove location used
  ]
end

;; use PTR to determine if the agent gets to reproduce
;;to reproduce  ;; turtle procedure
  ;; if a random variable is less than the PTR the agent can reproduce
 ;; if random-float 1.0 < ptr [
    ;; find an empty location to reproduce into
   ;; let destination one-of neighbors4 with [not any? turtles-here]
    ;;if destination != nobody [
      ;; if the location exists hatch a copy of the current turtle in the new location
      ;;  but mutate the child
      ;;hatch 1 [
        ;;move-to destination
        ;;mutate
    ;;  ]
  ;;  ]
;;  ]
;;end

;; modify the children of agents according to the mutation rate
;;to mutate  ;; turtle procedure
  ;; mutate the color
  ;;if random-float 1.0 < mutation-rate [
    ;;let old-color color
    ;;while [color = old-color]
      ;;[ set color random-color ]
  ;;]
  ;; mutate the strategy flags;
  ;; use NOT to toggle the flag
  ;;if random-float 1.0 < mutation-rate [
    ;;set cooperate-with-same? not cooperate-with-same?
  ;;]
  ;;if random-float 1.0 < mutation-rate [
    ;;set cooperate-with-different? not cooperate-with-different?
  ;;]
  ;; make sure the shape of the agent reflects its strategy
  ;;update-shape
;;end

to mutate  ;; turtle procedure
  if color != white and random-float 1.0 < mutation-rate [
    set cooperate-with-same? not cooperate-with-same?
  ]
  if color != white and random-float 1.0 < mutation-rate [
    set cooperate-with-different? not cooperate-with-different?
  ]
   ;;make sure the shape of the agent reflects its strategy
  update-shape
end

;to death
  ;; check to see if a random variable is less than the death rate for each agent
  ;ask turtles [
    ;if random-float 1.0 < death-rate [ die ]
  ;]
;end

;; make sure the shape matches the strategy
to update-shape
  ;; if the agent cooperates with same they are a circle
  ifelse cooperate-with-same? [
    ifelse cooperate-with-different?
      [ set shape "circle" ]    ;; filled in circle (altruist)
      [ set shape "circle 2" ]  ;; empty circle (ethnocentric)
  ]
   ;; if the agent doesn't cooperate with same they are a square
  [
    ifelse cooperate-with-different?
      [ set shape "square" ]    ;; filled in square (cosmopolitan)
      [ set shape "square 2" ]  ;; empty square (egoist)
  ]
end


to recolor-turtles
  set wealth-list sort-on[raw-wealth]turtles
  set percentile75 [raw-wealth] of item (0.75 * length(wealth-list)) wealth-list      ;; fixed hardcode
  set percentile50 [raw-wealth] of item (0.50 * length(wealth-list)) wealth-list
  set percentile25 [raw-wealth] of item (0.25 * length(wealth-list)) wealth-list
  ask turtles
  [ifelse (raw-wealth >= percentile75)
    [set color white]
    [ifelse (raw-wealth < percentile75 and raw-wealth >= percentile50)
      [set color green]
      [ifelse (raw-wealth < percentile50 and raw-wealth >= percentile25)
        [set color yellow]
        [set color red] ] ] ]

end


;to scale100  ;; scaling everything to base 100
 ; set wealth-list sort-on[raw-wealth] turtles
 ; let counter 0
 ; let scale  [raw-wealth] of item 0 turtles-list / 100
 ; while [counter < length turtles-list]
  ;[
    ;;set [scaled-wealth] of item counter mylist  [wealth] of item counter mylist * scale
    ;; change list to array to make mutable
   ; set counter counter + 1
  ;]

;end
;; this routine calculates a moving average of some stats over the last 100 ticks
to update-stats
  ;;set last100dd        shorten lput (count turtles with [shape = "square 2"]) last100dd
  ;;set last100cc        shorten lput (count turtles with [shape = "circle"]) last100cc
  ;;set last100cd        shorten lput (count turtles with [shape = "circle 2"]) last100cd
  ;;set last100dc        shorten lput (count turtles with [shape = "square"]) last100dc
  set last100coopown   shorten lput coopown last100coopown
  set last100defother  shorten lput defother last100defother
  set last100meetown   shorten lput meetown last100meetown
  set last100coop      shorten lput (coopown + coopother) last100coop
  set last100meet      shorten lput meet last100meet
  set last100meetother shorten lput meetother last100meetother
end

;; this is used to keep all of the last100 lists the right length
to-report shorten [the-list]
  ifelse length the-list > 100
    [ report butfirst the-list ]
    [ report the-list ]
end

;; these are used in the BehaviorSpace experiments

to-report meetown-percent
  report meetown / max list 1 meet
end
to-report meetown-agg-percent
  report meetown-agg / max list 1 meet-agg
end
to-report coopown-percent
  report coopown / max list 1 meetown
end
to-report coopown-agg-percent
  report coopown-agg / max list 1 meetown-agg
end
to-report defother-percent
  report defother / max list 1 meetother
end
to-report defother-agg-percent
  report defother-agg / max list 1 meetother-agg
end
to-report consist-ethno-percent
  report (defother + coopown) / (max list 1 meet )
end
to-report consist-ethno-agg-percent
  report (defother-agg + coopown-agg) / (max list 1 meet-agg )
end
to-report coop-percent
  report (coopown + coopother) / (max list 1 meet )
end
to-report coop-agg-percent
  report (coopown-agg + coopother-agg) / (max list 1 meet-agg)
end
to-report cc-count
  report sum last100cc / max list 1 length last100cc
end
to-report cd-count
  report sum last100cd / max list 1 length last100cd
end
to-report dc-count
  report sum last100dc / max list 1 length last100dc
end
to-report dd-count
  report sum last100dd / max list 1 length last100dd
end
to-report cc-percent
  report cc-count / (max list 1 (cc-count + cd-count + dc-count + dd-count))
end
to-report cd-percent
  report cd-count / (max list 1 (cc-count + cd-count + dc-count + dd-count))
end
to-report dc-percent
  report dc-count / (max list 1 (cc-count + cd-count + dc-count + dd-count))
end
to-report dd-percent
  report dd-count / (max list 1 (cc-count + cd-count + dc-count + dd-count))
end
to-report last100coopown-percent
  report sum last100coopown / max list 1 sum last100meetown
end
to-report last100defother-percent
  report sum last100defother / max list 1 sum last100meetother
end
to-report last100consist-ethno-percent
  report (sum last100defother + sum last100coopown) / max list 1 sum last100meet
end
to-report last100meetown-percent
  report sum last100meetown / max list 1 sum last100meet
end
to-report last100coop-percent
  report sum last100coop / max list 1 sum last100meet
end
to-report northneighborcolor1
  report northneighborcolor
end
to-report eastneighborcolor1
  report eastneighborcolor
end
to-report southneighborcolor1
  report southneighborcolor
end
to-report westneighborcolor1
  report westneighborcolor
end


; Copyright 2003 Uri Wilensky.
; See Info tab for full copyright and license.
@#$#@#$#@
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Circles cooperate with same color\nSquares defect with same color\nFilled-in shapes cooperate with different color\nEmpty shapes defect with different color\n\nWhite is the 100th-75th percentile\nGreen is the 75th-50th percentile\nYellow is the 50th-25th percentile\nRed is the 25th-0th percentile
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@#$#@#$#@
## WHAT IS IT?

This model, due to Robert Axelrod and Ross A. Hammond, suggests that "ethnocentric" behavior can evolve under a wide variety of conditions, even when there are no native "ethnocentrics" and no way to differentiate between agent types.  Agents compete for limited space via Prisoner Dilemma's type interactions. "Ethnocentric" agents treat agents within their group more beneficially than those outside their group.  The model includes a mechanism for inheritance (genetic or cultural) of strategies.

## HOW IT WORKS

Each agent has three traits: a) color, b) whether they cooperate with same colored agents, and c) whether they cooperate with different colored agents.  An "ethnocentric" agent is one which cooperates with same colored agents, but does not cooperate with different colored agents. An "altruist" cooperates with all agents, while an "egoist" cooperates with no one.  A "cosmopolitan" cooperates with agents of a different color but not of their own color.

At each time step, the following events occur:

1. Up to IMMIGRANTS-PER-DAY, new agents appear in random locations with random traits.

2. Agents start with an INITIAL-PTR (Potential-To-Reproduce) chance of reproducing.  Each pair of adjacent agents interact in a one-move Prisoner's Dilemma in which each chooses whether or not to help the other.  They either gain, or lose some of their potential to reproduce.

3. In random order, each agent is given a chance to reproduce.  Offspring have the same traits as their parents, with a MUTATION-RATE chance of each trait mutating.  Agents are only allowed to reproduce if there is an empty space next to them.  Each agent's birth-rate is reset to the INITIAL-PTR.

4. The agent has a DEATH-RATE chance of dying, making room for future offspring and immigrants.

## HOW TO USE IT

To prepare the simulation for a new run, press SETUP EMPTY.  Press GO to start the simulation running, press GO again to stop it.

SETUP FULL will allow you to start with a full world of random agents.

COST-OF-GIVING indicates how much it costs an agent to cooperate with another agent.

GAIN-OF-RECEIVING indicates how much an agent gains if another agent cooperates with them.

IMMIGRANT-CHANCE-COOPERATE-WITH-SAME indicates the probability that an immigrating agent will have the COOPERATE-WITH-SAME? variable set to true.

IMMIGRANT-CHANCE-COOPERATE-WITH-DIFFERENT indicates the probability that an immigrating agent will have the COOPERATE-WITH-DIFFERENT? variable set to true.

The STRATEGY COUNTS plot tracks the number of agents that utilize a given cooperation strategy:

CC --- People who cooperate with everyone
CD --- People who cooperate only with people of the same type
DD --- People who do not cooperate with anyone
DC --- People who only cooperate with people of different types

## THINGS TO NOTICE

Agents appear as circles if they cooperate with the same color.  They are filled in if they also cooperate with a different color (altruists) or empty if they do not (ethnocentrics).  Agents are squares if they do not cooperate with the same color.  The agents are filled in if they cooperate with a different color (cosmopolitans) or empty if they do not (egoists).

Observe the interaction along the edge of a group of ethnocentric agents, and non-ethnocentric agents.  What behaviors do you see?  Is one more stable?  Does one expand into the other group?

Observer the STRATEGY COUNTS plot.  Does one strategy occur more than others?  What happens when we change the model?

## THINGS TO TRY

Set the IMMIGRANT-CHANCE-COOPERATE sliders both to 1.0.  This means there are only altruists created.  Do ethnocentrics and other strategies ever evolve?  Do they ever out compete the altruists?

Change the values of COST-OF-GIVING and GAIN-OF-RECEIVING and observe the effects on the model and the level of ethnocentricity.

This model comes with a group of BehaviorSpace experiments defined.  You can access them by choosing BehaviorSpace on the Tools menu.  These are the original experiments that Axelrod and Hammond ran to test the robustness of this model. These experiments vary lots of parameters like the size of the world, IMMIGRANTS-PER-DAY and COST-OF-GIVING.  These experiments are detailed at   http://www-personal.umich.edu/~axe/Shared_Files/Axelrod.Hammond/index.htm

## EXTENDING THE MODEL

Add more colors to the model.  Does the behavior change?

Make some patches richer than others, so that agents on them have a higher chance of reproducing.  Distribute this advantage across the world in different ways such as randomly, in blobs, or in quarters.

Tag patches with a color.  distribute the colors across the world in different ways: blobs, randomly, in discrete quarters.  Agents use the patch color under other agents to determine whether to cooperate with them or not.

## NETLOGO FEATURES

To ensure fairness, the agents should run in random order.  Agentsets in NetLogo are always in random order, so no extra code is needed to achieve this.

## RELATED MODELS

 * Segregation
 * PD Basic
 * Ethnocentrism - Alternative Visualization

## CREDITS AND REFERENCES

This model is a NetLogo version of the ethnocentrism model presented by Robert Axelrod at Northwestern University at the NICO (Northwestern Institute on Complex Systems) conference on October 25th, 2003.

See also Ross A. Hammond and Robert Axelrod, The Evolution of Ethnocentrism, http://www-personal.umich.edu/~axe/research/AxHamm_Ethno.pdf

## HOW TO CITE

If you mention this model or the NetLogo software in a publication, we ask that you include the citations below.

For the model itself:

* Wilensky, U. (2003).  NetLogo Ethnocentrism model.  http://ccl.northwestern.edu/netlogo/models/Ethnocentrism.  Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Please cite the NetLogo software as:

* Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

## COPYRIGHT AND LICENSE

Copyright 2003 Uri Wilensky.

![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.

Commercial licenses are also available. To inquire about commercial licenses, please contact Uri Wilensky at uri@northwestern.edu.

This model was created as part of the projects: PARTICIPATORY SIMULATIONS: NETWORK-BASED DESIGN FOR SYSTEMS LEARNING IN CLASSROOMS and/or INTEGRATED SIMULATION AND MODELING ENVIRONMENT. The project gratefully acknowledges the support of the National Science Foundation (REPP & ROLE programs) -- grant numbers REC #9814682 and REC-0126227.

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    <enumeratedValueSet variable="max-pxcor">
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    <enumeratedValueSet variable="max-pycor">
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  <experiment name="Experiment 111" repetitions="10" runMetricsEveryStep="false">
    <setup>setup-empty</setup>
    <go>go</go>
    <timeLimit steps="2000"/>
    <metric>coopown-percent</metric>
    <metric>defother-percent</metric>
    <metric>consist-ethno-percent</metric>
    <metric>meetown-percent</metric>
    <metric>coop-percent</metric>
    <metric>last100coopown-percent</metric>
    <metric>last100defother-percent</metric>
    <metric>last100consist-ethno-percent</metric>
    <metric>last100meetown-percent</metric>
    <metric>last100coop-percent</metric>
    <metric>cc-percent</metric>
    <metric>cd-percent</metric>
    <metric>dc-percent</metric>
    <metric>dd-percent</metric>
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    <enumeratedValueSet variable="mutation-rate">
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    <enumeratedValueSet variable="cost-of-giving">
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    <enumeratedValueSet variable="immigrant-chance-cooperate-with-different">
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    <enumeratedValueSet variable="death-rate">
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    <enumeratedValueSet variable="max-pxcor">
      <value value="50"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="max-pycor">
      <value value="50"/>
    </enumeratedValueSet>
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  <experiment name="Experiment 113" repetitions="10" runMetricsEveryStep="false">
    <setup>setup-empty</setup>
    <go>go</go>
    <timeLimit steps="2000"/>
    <metric>coopown-percent</metric>
    <metric>defother-percent</metric>
    <metric>consist-ethno-percent</metric>
    <metric>meetown-percent</metric>
    <metric>coop-percent</metric>
    <metric>last100coopown-percent</metric>
    <metric>last100defother-percent</metric>
    <metric>last100consist-ethno-percent</metric>
    <metric>last100meetown-percent</metric>
    <metric>last100coop-percent</metric>
    <metric>cc-percent</metric>
    <metric>cd-percent</metric>
    <metric>dc-percent</metric>
    <metric>dd-percent</metric>
    <enumeratedValueSet variable="gain-of-receiving">
      <value value="0.03"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="initial-ptr">
      <value value="0.12"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="immigrants-per-day">
      <value value="2"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="immigrant-chance-cooperate-with-same">
      <value value="0.5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="mutation-rate">
      <value value="0.005"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cost-of-giving">
      <value value="0.01"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="immigrant-chance-cooperate-with-different">
      <value value="0.5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="death-rate">
      <value value="0.1"/>
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    <enumeratedValueSet variable="max-pxcor">
      <value value="50"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="max-pycor">
      <value value="50"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="experiment" repetitions="1" runMetricsEveryStep="true">
    <setup>setup-full</setup>
    <go>go</go>
    <timeLimit steps="5"/>
    <exitCondition>ticks = 100</exitCondition>
    <metric>count raw-wealth</metric>
    <enumeratedValueSet variable="immigrants-per-day">
      <value value="1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="gain-of-receiving">
      <value value="0.53"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="exchange_rate">
      <value value="0.75"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="chance-cooperate-with-same">
      <value value="0.45"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cost-of-giving">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="mutation-rate">
      <value value="0.005"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="chance-cooperate-with-different">
      <value value="0.45"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="death-rate">
      <value value="0.1"/>
    </enumeratedValueSet>
  </experiment>
</experiments>
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