;----------------------------------------------;
;     "Preferences Falsification"              ;
;     Created by: F. León & J. Tena            ;
;     Code by: F.J. Miguel (LSDS-UAB)          ;
;     Version: 1.4 (no shocks) Mar. 2019       ;
;----------------------------------------------;

;;    "Preferences Falsification" (PreFalSim) its a simulation model based on Kuran's Model
;;    It allows to overcome some of the main limitations of Kuran's models.
;;
;;    This program is free software: you can redistribute it and/or modify
;;    it under the terms of the GNU General Public License as published by
;;    the Free Software Foundation, either version 3 of the License, or
;;    (at your option) any later version.
;;
;;    This program is distributed in the hope that it will be useful,
;;    but WITHOUT ANY WARRANTY; without even the implied warranty of
;;    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
;;    GNU General Public License for more details.
;;
;;    You should have received a copy of the GNU General Public License
;;    along with this program.  If not, see <http://www.gnu.org/licenses/>.
;;
;;    Contact information:
;;    Francesc J. MIGUEL QUESADA
;;    Laboratory for Socio-Historical Dynamics Simulation (LSDS)
;;    Universitat Autònoma de Barcelona, Catalunya, España.
;;    e-mail: lsds@uab.cat

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;     Variable Declaration        ;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; Globals
globals [
  newclass
  newcolor
  PHI
  GOt-1          ; "General opinion", x-private-opinion mean from previous turn
  Rsz
  Rsx
  fakers-0
  fakers-1
  fakers
  social-list
  ExpectedZ
  eee
  GOt-1-prov
  Xmean          ; x-private-opinion mean
  Xdev           ; x-private-opinion deviation
  Zmean          ; z-public-declaration mean
  Zdev           ; z-public-declaration deviation
  Fprob          ; fakers / population (0-1) or F-probability
  F1prob         ; fakers-1 / population (0-1) or F1-probability
  F0prob         ; fakers-0 / population (0-1) or F0-probability
  cumfakers-0
  cumfakers-1
  meanfakers-0
  meanfakers-1
]

;; Agents Attributes
patches-own [
  class
  group
  rank
  x-private-opinion
  y-threshold-falsification
  y-prov-threshold-falsification    ;;; for provisional round computations
  k-threshold-valid-rounds   ;;; Taken from k slider (default 3)
  v-last-valid-rounds
  z-public-declaration  ;;; {-/0/1}
  z-social-map          ;;; The opinion that j expressed in his LAST interaction with i, if no interaction the rank is used as a proxy.
  SVFcount              ;;; Succesive Valid Falsifications to count past "valid" falsifications.
  H-Festinger?          ;;; Coherence heuristic triggered {false/true}
  H-Goffman?            ;;; Impression Management heuristic triggered {false/true}
  my-Sj                 ;;; rank of current (t) higest rank neighbour
  my-ZSj                ;;; Last remembered public expression of current (t) higest rank neighbour
  my-p
  EOi
  ROi
  GOit-1                ;;; "Perceived general opinion", noise-added to GOt-1 (previous turn x-private-opinion mean)
  fake                  ;;; {no/0/1}
  Why-To-Fake           ;;; The reason to fake opinion {}
  round-declaration?    ;;; {false/true}
]

;;;;;;;;;;;;;;;;;;;;;;;
;;; Setup Functions ;;;
;;;;;;;;;;;;;;;;;;;;;;;

;;; Inicializing the WORLD

to setup
 clear-all
print "-----------------"
print date-and-time
 file-close-all
 resize-world 0 39 0 39
 set eee 1.0E-10              ;;; Use eee to prevent division by zero
 ask patches with [pxcor < 10] [ set class "High" set pcolor blue ]
 ask patches with [pxcor > 9 and pxcor < 20] [ set class "Medium" set pcolor orange ]
 ask patches with [pxcor > 19 and pxcor < 30] [ set class "Low" set pcolor red ]
 ask patches with [pxcor > 29] [ set class "Medium" set pcolor orange ]
 ask n-of ( round ( ( 1 - segregation_level ) * count patches) ) patches [ set class "Change" ]
 ask patches with [ class = "Change" ]
     [
       ask one-of patches with [ class = "Change" and pcolor != [pcolor] of myself ]
           [
             set newclass class set newcolor pcolor
             set class [class] of myself
             set pcolor [pcolor] of myself
           ]
       set class newclass
       set pcolor newcolor
     ]
 ask patches with [ class = "Change" ]
     [
       if pcolor = blue [ set class "High" ]
       if pcolor = orange [ set class "Medium" ]
       if pcolor = red [ set class "Low" ]
     ]

;;; Inicializing AGENTS

  ask patches [ set rank -1 ]   ;;; rank not assigned
  ask n-of  17 patches with [ (class = "Low") AND (rank < 0) ] [ set rank 0.0 ]                          ;;;  17 => rank 0
  ask n-of  50 patches with [ (class = "Low") AND (rank < 0) ] [ set rank 0.05 + random-float 0.10 ]     ;;;  50 => rank U~[0.05,0.15]
  ask n-of 133 patches with [ (class = "Low") AND (rank < 0) ] [ set rank 0.15 + random-float 0.10 ]     ;;; 133 => rank U~[0.15,0.25]
  ask n-of 200 patches with [ (class = "Low") AND (rank < 0) ] [ set rank 0.25 + random-float 0.10 ]     ;;; 200 => rank U~[0.25,0.35]
  ask n-of 246 patches with [ (class = "Medium") AND (rank < 0) ] [ set rank 0.35 + random-float 0.10 ]  ;;; 246 => rank U~[0.35,0.45]
  ask n-of 308 patches with [ (class = "Medium") AND (rank < 0) ] [ set rank 0.45 + random-float 0.10 ]  ;;; 308 => rank U~[0.45,0.55]
  ask n-of 246 patches with [ (class = "Medium") AND (rank < 0) ] [ set rank 0.55 + random-float 0.10 ]  ;;; 246 => rank U~[0.55,0.65]
  ask n-of 200 patches with [ (class = "High") AND (rank < 0) ] [ set rank 0.65 + random-float 0.10 ]    ;;; 200 => rank U~[0.65,0.75]
  ask n-of 133 patches with [ (class = "High") AND (rank < 0) ] [ set rank 0.75 + random-float 0.10 ]    ;;; 133 => rank U~[0.75,0.85]
  ask n-of  50 patches with [ (class = "High") AND (rank < 0) ] [ set rank 0.85 + random-float 0.10 ]    ;;;  50 => rank U~[0.85,0.95]
  ask n-of  17 patches with [ (class = "High") AND (rank < 0) ] [ set rank 1.0 ]                         ;;;  17 => rank 1

 ask patches [
     set rank precision rank 2
     set x-private-opinion rank
     set z-public-declaration ifelse-value ( x-private-opinion < 0.5 ) [ 0 ] [ 1 ]
     set round-declaration? false
     set y-threshold-falsification                    ;;; if x>0.5 => U[0.0,0.5] and if x<0.5 => U[0.5,1.0] (v1.4)
         precision ( random-float 0.5 + ifelse-value ( x-private-opinion < 0.5 ) [ 0.5 ] [ 0 ] ) 2
     set k-threshold-valid-rounds k                   ;;; From k slider
     set v-last-valid-rounds 1 + random 10            ;;; INITIAL PREVIOUS (t-k) VALID ROUNDS random-uniform [1,10]
     set H-Festinger? false
     set H-Goffman? false
     set fake "no"
     set Why-To-Fake ""

     ] ;;; end of Inicializing AGENTS

  set GOt-1 precision mean [x-private-opinion] of patches 2

 ask patches [
     ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; INITIAL SVFcount as 0 (v1.4)
     set SVFcount 0
     ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; INITIAL general opinion (biassed)
     set GOit-1 noise GOt-1
     ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; INITIAL social-map (empty list),
     ;;;;;;;;;;;;;;;;;;;;;;;;;;;; will be builded in runtime as [ [ (patch 0 0) 0 ] [ (patch 0 1) 1 ] [ (patch 1 1) 0 ] ... [ (patch A B) Z ] ]
     set z-social-map []
     ]

 reset-ticks
end

;;;;;;;;;;;;;;;;;;;;;;;;;
;;; Runtime Functions ;;;
;;;;;;;;;;;;;;;;;;;;;;;;;

;;; Main Procedure
to go
  ifelse ticks < Rounds
    [
      make-groups
      ask patches
          [ festinger-heuristic ]   ;;; Coherence heuristic: “match your private opinion with the one you most frequently defended in public if lately they differed more often than they did not”.
      ask patches
          [ goffman-heuristic ]     ;;; Impression management heuristic: "opinions of low rank agents are consciously hidden when they face higher rank agents with different opinions.".
      ask patches
          [
            reference-opinion       ;;; expectations to face according to own memory of past interactions and knowledge of the general opinion of the population.
            set round-declaration? false
            social-proof-heuristic  ;;; “falsify your preferences if enough others support for the opinion that you dislike”.
            set round-declaration? true
          ]                         ;;; run both procedures: for allowing "later" agents to consider NOT the expectation BUT the actual declaration.

      ask patches
          [
            update-z-social-map     ;;; update the agents' "social-map" by scanning and processing the new neighborhood.
            register-round          ;;; check if present round is a "valid falsification" and update the SVFcount.
          ]
      ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Count fakers, Show if activated, and reset round-declaration
      ask patches [
          if any? turtles [ ask turtles-here [die] ]
          if ( x-private-opinion < 0.5 ) AND ( ROi >= y-prov-threshold-falsification )
             [ set fake 1
               if SeeFakers? [ sprout 1 [ set shape "1" ] ]
             ]
          if ( x-private-opinion >= 0.5 ) AND ( ROi < y-prov-threshold-falsification )
             [ set fake 0
               if SeeFakers? [ sprout 1 [ set shape "0"] ]
            ]
          ]
      ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;; Update ploting data
      set fakers-0 count patches with [ fake = 0 ]
      set cumfakers-0 cumfakers-0 + fakers-0
      set meanfakers-0 precision (cumfakers-0 / (ticks + eee)) 0
      set fakers-1 count patches with [ fake = 1 ]
      set cumfakers-1 cumfakers-1 + fakers-1
      set meanfakers-1 precision (cumfakers-1 / (ticks + eee)) 0
      set fakers fakers-0 + fakers-1

      set GOt-1 precision mean [z-public-declaration] of patches 2       ;;; update General Declaration (Z) to be used next round
      set Xmean precision mean [x-private-opinion] of patches 2          ;;; update General Opinion (X)

      set Xdev precision sqrt variance [x-private-opinion] of patches 2
      set Zmean GOt-1
      set Zdev precision sqrt variance [z-public-declaration] of patches 2
      set Fprob precision ( fakers / 1600 ) 2
      set F1prob precision ( fakers-1 / 1600 ) 2
      set F0prob precision ( Fprob - F1prob ) 2

      tick
    ]
    [
      stop
    ]
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;; Auxiliar Functions   ;;;
;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to festinger-heuristic                    ;;; Coherence heuristic. Triggered when previous k successive "valid" falsifications.
                                          ;;; Review for v1.0: "simply make it a weighted average of private vs recently expressed opinion"
  ifelse SVFcount = k-threshold-valid-rounds     ;;; COHERENCE condition trigger
     [
      set H-Festinger? true
      set SVFcount 0                  ;;;Case B2 Festinger (fake & k=3) = ERASE
      set x-private-opinion precision ( (0.5 * z-public-declaration) + (0.5 * x-private-opinion) ) 2
      set y-threshold-falsification precision ( 1 - x-private-opinion ) 2
     ] ;;;end of COHERENCE condition
     [
      set H-Festinger? false
     ]

end

to goffman-heuristic
if rank < 0.5                        ;;; Impression Management Heuristic is restricted to low-rank agents.
  [
   set H-Goffman? false
   let Ij max-one-of other patches with [ group = [group] of myself ] [rank]  ;;; Highest rank agent in the activated neighborhood
   let Sj [rank] of Ij                ;;; rank of Ij
   let Xj [x-private-opinion] of Ij     ;;; Opinion of Ij
   let Zj round Xj                      ;;; "My remembered" public opinion of Ij (by defaulf, same of Xj for the initial rounds -no z-social-map)
   foreach z-social-map [ [?1] -> if member? Ij ?1 [ set Zj item 1 ?1 ] ]
   set my-Sj Sj
   set my-ZSj Zj

   if ( (x-private-opinion < 0.5) AND (Zj = 1) ) [ set H-Goffman? true  ]          ;;; CASE "A"
   if ( (x-private-opinion >= 0.5) AND (Zj = 0) ) [ set H-Goffman? true  ]         ;;; CASE "B"

    ifelse H-Goffman?
      [ ;;; Goffman triggered
        let Dij ( 1 + abs (rank - Sj) )
        if Dij = 0 [set Dij eee]
        let aux ( ( abs ( y-threshold-falsification - x-private-opinion ) ) / Dij )

        ifelse Zj = 1
            [ set y-prov-threshold-falsification ( x-private-opinion + aux ) ]   ;;; CASE "A"
            [ set y-prov-threshold-falsification ( x-private-opinion - aux ) ]   ;;; CASE "B"

        set y-prov-threshold-falsification precision y-prov-threshold-falsification 2
      ]
      [ ;;; Goffman NOT triggered
        set y-prov-threshold-falsification precision y-threshold-falsification 2
      ]
  ] ;;; close rank filter
end

to reference-opinion
   let SumWjZt-1 0
   let SumWj 0
   let Wj 0

   ask other patches with [ group = [group] of myself ]                     ;;; The reference group MN neighborhood for t
            [
              ifelse ticks > 0
                     [ foreach [z-social-map] of myself
                        [ [?1] ->
                          if member? self ?1 [ set ExpectedZ item 1 ?1 ]    ;;; Use z-social-map expected Z value for group members
                        ]
                       ;;; BUT if the group memeber has already "declared", replace if for Z actual value.
                       if round-declaration? [ set ExpectedZ z-public-declaration ]
                     ]
                     [ set ExpectedZ rank ]                               ;;; Proxy for tick 0 interactions
              set SumWjZt-1 ( SumWjZt-1 + ExpectedZ )                       ;;; Use Z from the z-social-map
              set SumWjZt-1 precision SumWjZt-1 2
            ]
   set EOi precision ( SumWjZt-1 / 16 ) 2
   set ROi precision ( ( p * GOit-1 ) + ( ( 1 - p ) * EOi ) ) 2
end

to social-proof-heuristic
     set z-public-declaration ifelse-value ( ROi >= y-prov-threshold-falsification ) [ 1 ] [ 0 ]

     ;;; Registering the Falsification event for the present round
     if z-public-declaration = round x-private-opinion [ set fake "no" ]
     if z-public-declaration > round x-private-opinion [ set fake "1" ]
     if z-public-declaration < round x-private-opinion [ set fake "0" ]
end

to register-round  ;;; Register present round as "valid" falsification or not, or reset the counter.

ifelse fake = "no"
  [ ;;;Case B1 (if fake = "no")
    set SVFcount 0
    set Why-To-Fake ""
  ]
  [ ;;; Valid falsifications (+1)
    ifelse H-Goffman?
    [
      ifelse ROi > y-threshold-falsification
         [ ;;;Case F3 (fake & Goffman & ROi>y) = VALID
           set SVFcount SVFcount + 1
           set Why-To-Fake "Irrelevant Goffman (F3)"
         ]
         [
           set Why-To-Fake "Due to Goffman (F2)"
         ]
    ]
    [ ;;;Case F1 (fake & NO-Goffman) = VALID
      set SVFcount SVFcount + 1
      set Why-To-Fake "Without Goffman (F1) "
    ]
  ]
end

to make-groups
   ask patches [set group ""]
   ask one-of patches [ sprout 1
       [
       set hidden? true
       set heading 0
       let aux-group 0
       while [ aux-group < 100 ]
         [
          repeat 10
                 [
                 set aux-group aux-group + 1
                 ask patch-here [
                     set group aux-group
                     ( foreach [ 1 2 3 0 1 2 3 0 1 2 3 0 1 2 3] [ 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3 ]
                           [ [?1 ?2] -> ask patch-at ?1 ?2 [ set group aux-group ] ] )
                     ]
                 fd 4
                 ]
          rt 90 fd 4 rt -90
          ]
        die
        ]
       ]
end

to update-z-social-map
   set social-list []
   ask other patches with [ group = [group] of myself ]                               ;;; The reference group MN neighborhood for t
       [ set social-list lput ( list self z-public-declaration ) social-list ]        ;;; Build social-list as a list of neighbors and Z's [ [(agent 0 0) 0] [(agent 0 1) 1] ... ]
   if length z-social-map = 0 [ set z-social-map social-list ]                        ;;; First round: Inicialize z-social-map

   ;;; The "new" social map will contain ALL the current round novelties (the social-list)...
   let new-social-map social-list
   ;;; ...plus ALL the "historical" information that WAS at the previous z-social-map BUT NOT at the current social-list.
   foreach z-social-map                                                               ;;; Explore each z-social-map agent...
       [ [?1] ->
         let sl 0
         while [ sl < length social-list ]                                            ;;; Enter loop for exploring each z-social-map agent
               [
                 if ( item 0 ?1 = item 0 item sl social-list )                         ;;; If an agent is BOTH at z-social-map and social-list...
                    [ set sl length social-list ]                                     ;;; ...nothing happens, explore next z-social-map agent
                 set sl sl + 1                                                        ;;; continue exploring the rest of social-list agents.
               ]
         if sl = length social-list                                                   ;;; If exit the loop WITHOUT find a z-social-map agent in the new social-list...
            [
              set new-social-map lput ?1 new-social-map
            ]
       ]
   set z-social-map new-social-map                                                    ;;; Update z-social-map
end

to-report noise [ g ]                                         ;;; Each agent misperceive the general opinion
  let aux precision ( g + (random-normal 0.5 0.05) - 0.5 ) 2  ;;; Add some "noise"
  report min (list max (list aux 0 ) 1 )                      ;;; Truncate to (0,1)
end
@#$#@#$#@
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Preference Falsification v1.4\n==================
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RUN VERSION\n=====================\n- experiments at Behavoiur Space\n- no debuging extra outputs\n- no programable shocks\n
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@#$#@#$#@
## WHAT IS IT?

(a general understanding of what the model is trying to show or explain)

## HOW IT WORKS

(what rules the agents use to create the overall behavior of the model)

## HOW TO USE IT

(how to use the model, including a description of each of the items in the Interface tab)

## THINGS TO NOTICE

(suggested things for the user to notice while running the model)

## THINGS TO TRY

(suggested things for the user to try to do (move sliders, switches, etc.) with the model)

## EXTENDING THE MODEL

(suggested things to add or change in the Code tab to make the model more complicated, detailed, accurate, etc.)

## NETLOGO FEATURES

(interesting or unusual features of NetLogo that the model uses, particularly in the Code tab; or where workarounds were needed for missing features)

## RELATED MODELS

(models in the NetLogo Models Library and elsewhere which are of related interest)

## CREDITS AND REFERENCES

(a reference to the model's URL on the web if it has one, as well as any other necessary credits, citations, and links)
@#$#@#$#@
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Circle -7500403 true true 130 147 38
Circle -7500403 true true 192 85 38
Circle -7500403 true true 85 40 38
Circle -7500403 true true 177 40 38
Circle -7500403 true true 177 132 38
Circle -7500403 true true 70 85 38
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Circle -7500403 true true 96 51 108
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Rectangle -13840069 true false 45 225 75 240

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person
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target
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tree
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triangle
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Rectangle -7500403 true true 181 185 214 194
Circle -16777216 true false 144 174 42
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Polygon -10899396 true false 132 85 134 64 107 51 108 17 150 2 192 18 192 52 169 65 172 87
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wheel
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Line -7500403 true 150 285 150 15
Line -7500403 true 15 150 285 150
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Line -7500403 true 40 84 269 221
Line -7500403 true 40 216 269 79
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wolf
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Polygon -7500403 true true 2 194 13 197 30 191 38 193 38 205 20 226 20 257 27 265 38 266 40 260 31 253 31 230 60 206 68 198 75 209 66 228 65 243 82 261 84 268 100 267 103 261 77 239 79 231 100 207 98 196 119 201 143 202 160 195 166 210 172 213 173 238 167 251 160 248 154 265 169 264 178 247 186 240 198 260 200 271 217 271 219 262 207 258 195 230 192 198 210 184 227 164 242 144 259 145 284 151 277 141 293 140 299 134 297 127 273 119 270 105
Polygon -7500403 true true -1 195 14 180 36 166 40 153 53 140 82 131 134 133 159 126 188 115 227 108 236 102 238 98 268 86 269 92 281 87 269 103 269 113

x
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Polygon -7500403 true true 30 75 75 30 270 225 225 270
@#$#@#$#@
NetLogo 6.0.4
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
<experiments>
  <experiment name="PF_100x_ticks" repetitions="100" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>got-1</metric>
    <metric>xmean</metric>
    <metric>fakers-0</metric>
    <metric>fakers-1</metric>
    <enumeratedValueSet variable="p">
      <value value="0.1"/>
      <value value="0.3"/>
      <value value="0.5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="segregation_level">
      <value value="0.1"/>
      <value value="0.5"/>
      <value value="0.9"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Rounds">
      <value value="300"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="PF_100x_runs" repetitions="100" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>got-1</metric>
    <metric>xmean</metric>
    <metric>meanfakers-0</metric>
    <metric>meanfakers-1</metric>
    <enumeratedValueSet variable="p">
      <value value="0.1"/>
      <value value="0.3"/>
      <value value="0.5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="segregation_level">
      <value value="0.1"/>
      <value value="0.5"/>
      <value value="0.9"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Rounds">
      <value value="300"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="PF_100x3_ticks" repetitions="3" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>got-1</metric>
    <metric>xmean</metric>
    <metric>fakers-0</metric>
    <metric>fakers-1</metric>
    <enumeratedValueSet variable="p">
      <value value="0.1"/>
      <value value="0.3"/>
      <value value="0.5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="segregation_level">
      <value value="0.1"/>
      <value value="0.5"/>
      <value value="0.9"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Rounds">
      <value value="100"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="PF_1x1000_ticks" repetitions="1" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>got-1</metric>
    <metric>xmean</metric>
    <metric>fakers-0</metric>
    <metric>fakers-1</metric>
    <enumeratedValueSet variable="p">
      <value value="0.1"/>
      <value value="0.3"/>
      <value value="0.5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="segregation_level">
      <value value="0.1"/>
      <value value="0.5"/>
      <value value="0.9"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Rounds">
      <value value="1000"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="PF_1x1000_ticks_p01" repetitions="1" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>got-1</metric>
    <metric>xmean</metric>
    <metric>fakers-0</metric>
    <metric>fakers-1</metric>
    <enumeratedValueSet variable="p">
      <value value="0.1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="segregation_level">
      <value value="0.1"/>
      <value value="0.5"/>
      <value value="0.9"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Rounds">
      <value value="1000"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="PF_1x1000_ticks_P03" repetitions="1" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>got-1</metric>
    <metric>xmean</metric>
    <metric>fakers-0</metric>
    <metric>fakers-1</metric>
    <enumeratedValueSet variable="p">
      <value value="0.3"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="segregation_level">
      <value value="0.1"/>
      <value value="0.5"/>
      <value value="0.9"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Rounds">
      <value value="1000"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="PF_1x1000_ticks_P05" repetitions="1" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>got-1</metric>
    <metric>xmean</metric>
    <metric>fakers-0</metric>
    <metric>fakers-1</metric>
    <enumeratedValueSet variable="p">
      <value value="0.5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="segregation_level">
      <value value="0.1"/>
      <value value="0.5"/>
      <value value="0.9"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Rounds">
      <value value="1000"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="PFS_XZF_xSxP_t500_rep100" repetitions="100" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>Xmean</metric>
    <metric>Xdev</metric>
    <metric>Zmean</metric>
    <metric>Zdev</metric>
    <metric>Fprob</metric>
    <steppedValueSet variable="p" first="0" step="0.1" last="1"/>
    <steppedValueSet variable="segregation_level" first="0" step="0.1" last="1"/>
    <enumeratedValueSet variable="Rounds">
      <value value="500"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="output?">
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="SeeFakers?">
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="crises">
      <value value="&quot;b 0 0 0\nc 0 0 0 L\nd 0 0 0\nh 0 0 0&quot;"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="a">
      <value value="1"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="PFS_ZXF1F0_3Sx3P_t300_rep100" repetitions="100" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>Xmean</metric>
    <metric>Zmean</metric>
    <metric>F1prob</metric>
    <metric>F0prob</metric>
    <enumeratedValueSet variable="p">
      <value value="0.1"/>
      <value value="0.3"/>
      <value value="0.5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="segregation_level">
      <value value="0.1"/>
      <value value="0.5"/>
      <value value="0.9"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Rounds">
      <value value="300"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="output?">
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="SeeFakers?">
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="crises">
      <value value="&quot;b 0 0 0\nc 0 0 0 L\nd 0 0 0\nh 0 0 0&quot;"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="a">
      <value value="1"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="PFS_XZF_xSxP_t300_rep20" repetitions="20" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>Xmean</metric>
    <metric>Xdev</metric>
    <metric>Zmean</metric>
    <metric>Zdev</metric>
    <metric>Fprob</metric>
    <steppedValueSet variable="p" first="0" step="0.1" last="1"/>
    <steppedValueSet variable="segregation_level" first="0" step="0.1" last="1"/>
    <enumeratedValueSet variable="Rounds">
      <value value="300"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="crises">
      <value value="&quot;b 0 0 0\nc 0 0 0 L\nd 0 0 0\nh 0 0 0&quot;"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="k">
      <value value="3"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="PFS_ZXF1F0_3Sx5P_t300_rep100" repetitions="100" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <metric>Xmean</metric>
    <metric>Zmean</metric>
    <metric>F1prob</metric>
    <metric>F0prob</metric>
    <enumeratedValueSet variable="p">
      <value value="0.1"/>
      <value value="0.3"/>
      <value value="0.5"/>
      <value value="0.7"/>
      <value value="0.9"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="segregation_level">
      <value value="0.1"/>
      <value value="0.5"/>
      <value value="0.9"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="Rounds">
      <value value="300"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="k">
      <value value="3"/>
    </enumeratedValueSet>
  </experiment>
</experiments>
@#$#@#$#@
@#$#@#$#@
default
0.0
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0.0 1 1.0 0.0
0.2 0 0.0 1.0
link direction
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Line -7500403 true 150 150 210 180
@#$#@#$#@
0
@#$#@#$#@