FrantisekProMisuV10.nlogo

extensions [nw matrix]

breed [consumats consumat]

turtles-own [
  attitude ;; actual attitude of consumat
  cognition ;; stores code of cognitive process used present round for determining non/sorting
  sorting ;; actual sorting behavior of consumat

  Nit-1 ;; Needs' satisfaction previous round
  Nit   ;; Needs' satisfaction recent round
  NSik  ;; Satisfaction of social needs
  NPik  ;; Satisfaction of personal needs
  Ui    ;; Uncertainity
  beta  ;; Randomly distributed agent parameter determining how much personal needs are weighted versus social ones

  record ;; list with recorded values of SORTING since SETUP until the last round
  changed? ;; did turtle changed SORTING since the previous round?
  loadedAttitude ;; attitude index loaded from file
  finalBehavior ;; final behavior index loaded from file
  finalSorting ;; final sorting behavior loaded from file
  simSorting ;; sorting RECORD rocoded into ordinal variable equivalent to FINALSORTING
  diffSorting2 ;; squarred difference between FINALSORTING and SIMSORTING
]

globals [
  end? ;; stores TRUE if conditions for finishing simulation are satisfied
  globalDiffSorting ;; indicator of quality of model fit to ISSP data
  individualDiffSorting ;; secondary indicator of model fit
]
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;;;; reporters substituting globals ;;;;
;; sorting: no/yes
to-report no
  report 0
end

to-report yes
  report 1
end

;; cognitive processes
to-report repetition
  report 0
end

to-report imitation
  report 1
end

to-report deliberation
  report 2
end

to-report comparison
  report 3
end
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Startup and Setup
to startup
  ca
  setup
end

to setup
  ;; setup itself
  if randomSeed? [random-seed RS]
  turtle-setup
  check-cutoffs

  ;; Checking of right setting of CUTOFFs
  if cutOff12 < 0.005 [
    set globalDiffSorting 10
    set individualDiffSorting 10
    stop
  ]

  ;; reseting
  reset-ticks
  if randomSeed? [random-seed RS]
end
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to check-cutoffs
  if cutOff23 >= cutOff34 [set cutOff23 (cutOff34 - 0.01)]
  if cutOff12 >= cutOff23 [set cutOff12 (cutOff23 - 0.01)]
end

to turtle-setup
  ;; cleaning environment
  ca
  set-default-shape turtles "circle"
  ask patches [set pcolor white]

  ;; longer turtle procedures - better to separate code to blocks
  network-generation
  feeding-turtles-with-data
  cleaning-turtle-variables
end

to network-generation
  ;; Generation of Watts-Strogatz small-world network and loading data in turtles
  nw:generate-watts-strogatz turtles links 1841 ifelse-value (differentDegree?) [maxCloseLinks] [closeLinks] randomLinks [ ;; The first part of implementation of switch DIFFERENT-DEGREE?
    fd 33 ;; make a big circle
    set size 5 ;; be big
    set beta precision (random-float 1) 4 ;; set possible variables
    set record [] ;; initializing RECORD as a list
    set changed? false
  ]
  if differentDegree? [ask n-of (((maxCloseLinks - closeLinks) / maxCloseLinks) * count links) links [die]] ;; The idea of DIFFERENT-DEGREE? is to crate small-world with a lot of links (MAX-CLOSE-LINKS)
end  ;;  and then randomly cut them to the number according CLOSE-LINKS, so we still receive small-world network, but nodes have different numbers of edges.

to feeding-turtles-with-data
  ;; Loading data in turtles after creating of small-world network
  ifelse ( file-exists? "dataMisa4.txt" ) [
    file-close
    file-open "dataMisa4.txt"       ;; NOTE: DataMisa4.txt are sorted according Attitude,

    ;; firstly we have prepare the list od IDs in specified order: on even positions upper half, on odd positions lower half
    let feedingOrder [] ;; we need empty variable for feeding it by cycle
    (foreach (range 0 921) (range 1840 919 -1) [ [a b] ->
        set feedingOrder lput a feedingOrder
        if b > 920 [set feedingOrder lput b feedingOrder]
    ])
    if not homophily? [ ;; If Homophily? condition is FALSE we have to randomize the order of turtles feeding
      set feedingOrder shuffle feedingOrder
    ]

    ;; Secondly, feeding itself
    foreach feedingOrder [  ;;      for every ID we find the turtle with respective WHO,
      [ID] -> ask turtle ID [ ;;    and ask the turtle to feed herself by the data
        set finalBehavior file-read
        set loadedAttitude file-read
        set finalSorting file-read
      ]
    ]
    file-close
  ][error "There is no dataMisa4.txt file in current directory!"]
end

to cleaning-turtle-variables
  ;; turtle variables
  ask turtles  [
    if finalSorting > 4 or finalSorting < 1 [die] ;; turtles with no data or without chance to sort die
    set finalSorting (5 - finalSorting) ;; NOTE: we reverse FINALSORTING here, for better comparability mean of RECORD distribution
    set attitude loadedAttitude ;; for code-testing purposes we separate attitude used in simulation and value loaded in turtle from data
    set sorting ifelse-value (attitude > random-float 1) [yes] [no]  ;; initialization with respect to ATTITUDE
    change-colour
  ]
  ask turtles [if count my-links = 0 [create-link-with one-of other turtles show "Link added!"]] ;; Every turtle must be connected

  ;; For computing level of satisfaction we need finished network and all consumats in the network with set behavior
  ask turtles  [
    set Nit-1 satisfaction ;; use routines for NPi and NSi and SATISFACTION
    set Nit satisfaction ;; use routines for NPi and NSi and SATISFACTION
    set Ui uncertainity ;; USE ROUTINE uncertainity
    set record lput sorting record ;; We record SORTING as the first value on RECORD
  ]
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; SETUP and GO reporters and routines
to-report uncertainity
  report sqrt( abs(Nit - Nit-1))
end

to-report satisfaction
  set NSik ((count link-neighbors with [sorting = [sorting] of myself]) / (count link-neighbors))
  set Nsik normalized (random-normal Nsik sigma)
  set NPik (1 - (abs (attitude - sorting)))
  let Nik (beta * NSik + (1 - beta) * NPik) ;; NSik and NPik weighted by BETA
  let price ((sortingPrice * sorting) + (1 - sorting)) ;; designed that for sorting==no equal to 1, for sorting==yes equal to SortingPrice
  set Nik (Nik / price) ;; Nik is weighted by sortingPrice
  set Nik (normalized (Nik)) ;; We check the Nik is in the interval <0 ; 1>, in case not, we cut value to the interval
  report Nik
end

to-report normalized [x]
  if x > 1 [set x 1]
  if x < 0 [set x 0]
  report precision x 4 ;; NOTE: here we round values to 4 digits
end

to-report longTimeSorting%
  report precision (100 * mean ([mean record] of turtles)) 2
end

to change-colour
  set color ifelse-value (sorting = yes) [green] [red]
end

to test
  repeat 5 [
    setup
    repeat 120 [go]
  ]
  print "Test passed! "
end
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to go
  ;; Checking of right setting of CUTOFFs
  if cutOff12 < 0.005 [
    set globalDiffSorting 10
    set individualDiffSorting 10
    stop
  ]

  ;; GO routine itself
  ask turtles [update-variables]
  ask turtles [choose-cognitive-process]
  ask turtles [update-behavior]
  ask turtles [update-consequences]
  if ticks >= 9 [update-globals]
  check-end
  tick
  if end? [
    ;do-regression
    stop
  ]
end

to update-globals
    set globalDiffSorting global-difference-in-sorting
    set individualDiffSorting individual-difference-in-sorting
end

to do-regression
    let y sort ([simSorting] of turtles)
    let x sort ([finalSorting] of turtles)
    Print "Regression coeficients - the first element of second list is R2:"
    print matrix:regress matrix:from-column-list (list y x)
end

to-report global-difference-in-sorting
  let x sort ([simSorting] of turtles)
  let y sort ([finalSorting] of turtles)
  let z (map [[a b] -> ((a - b) ^ 2)] x y)
  report precision (mean z) 6
end

to-report individual-difference-in-sorting
  let x ([diffSorting2] of turtles)
  report precision (mean x) 6
end

to check-end
  ;; let's suppose, conditions are satisfied :)
  set end? true

  ;; Checking turtles' changes
  ask turtles [
    set changed? false
    let x (length record) - 2
    if length record > 1 and last record != (item x record) [set changed? true]
  ]

  ;; Checking length of simulation
  if count turtles with [changed?] > 0 [set end? false]  ;; we continue in simulation if here is some change
  if ticks <= 10 [set end? false] ;; anyway, we continue in simulation for 10 ticks
  ;if length ([record] of one-of turtles) = steps [set end? true] ;; anyway, we stop simulation at 100 ticks
  if ticks >= steps [set end? true] ;; anyway, we stop simulation at STEPS ticks
end

to update-variables
  set Nit-1 Nit ;; we copy Nit as past satisfaction
  set Nit satisfaction ;; we compute recent satisfaction via reporter SATISFACTION
  set Ui uncertainity ;; we compute recent uncertainity via reporter UNCERTAINITY
end

to choose-cognitive-process
  ;; here we just set value of variable COGNITION according NIT and UI values,
  ;; behavior itself would be chosen elsewhere
  ifelse Nit < tauN [
    set cognition ifelse-value (Ui > tauU) ["do-comparison"] ["do-deliberation"]
  ][set cognition ifelse-value (Ui > tauU) ["do-imitation"] ["do-repetition"]]
end

to update-behavior
  ;; running right cognitive routine, NOTE! the name of the procedure is already stored in COGNITION, we just have to find a way how run the string
  run cognition

  ;; Randomly reversing of planned behavior
  ;; Not only reverse behavior, we have also recalculate NIT of reversed behavior, but may be NOT...
  ifelse diffReversion? [
    if sorting = yes and pReverseSorting > random-float 1 [ ;; Just for start, take it the P-REVERSE-SORTING is only for change from YES to NO, later we might also prepare the other change
      set sorting no
      if recalculateNit? [set Nit satisfaction] ;; let us try it and experiment with difference in recalculation - now it seems there is no substantial effect of recalculation...
    ]
    if sorting = no and 0.1 > random-float 1 [ ;; Just fr start hard-wired value
      set sorting yes
      if recalculateNit? [set Nit satisfaction] ;; let us try it and experiment with difference in recalculation - now it seems there is no substantial effect of recalculation...
    ]
  ][
    if pReverseSorting > random-float 1 [
      set sorting (1 - sorting)
      if recalculateNit? [set Nit satisfaction] ;; let us try it and experiment with difference in recalculation - now it seems there is no substantial effect of recalculation...
    ]
  ]
end

to update-consequences
  ;; recording behavior
  set record lput sorting record ;; We record new SORTING value as the last value on RECORD
  if length record > 100 [set record (but-first record)] ;; triming RECORD list to the last 100 records

  ;; Updating simulated answer to the sorting question
  if ticks >= 9 [
    let value (mean record)
    ifelse value < cutoff23 [
      set simSorting ifelse-value (value < cutoff12) [1][2]
    ][set simSorting ifelse-value (value < cutoff34) [3][4]
    ]
    set diffSorting2 ((simSorting - finalSorting) ^ 2)
  ]

  ;; Re-color
  change-colour
end

to do-repetition ;; consumat repeats SORTING
  ;; sorting is set from previous round, so there is no need to do anything :)
  ;; at least now :)
end

to do-imitation ;; consumat imitates the most common value of SORTING
  ;; we use MODES function, which returns list of most common values,
  ;; in case more values are the most frequent it returns list of all the most common values
  let mostCommonValues modes [sorting] of link-neighbors

  ;; from these most common values we choose randomly by function ONE-OF,
  ;; in case the list contain only one value, ONE-OF turns list of one value into the value
  set sorting one-of mostCommonValues
end

to do-deliberation ;; consumat compares NIT valueas for both values of SORTING
  ;;;; We set SORTING to NO, compute NIT, then we set it to YES, compute again and compare results
  ;; Computing for NO
  set sorting no
  let NitNo satisfaction

  ;; Computing for YES
  set sorting yes
  let NitYes satisfaction

  ;; Setting SORTING to better value
  ifelse NitNo = NitYes [
    set sorting one-of (list no yes)  ;; in case of tie we choose randomly
  ][set sorting ifelse-value (NitYes > NitNo) [yes] [no]] ;; else we choose better value
end

to do-comparison ;; consumat compares SORTING
  ;;;; Firstly we compute NIT for actual SORTING,
  ;;;; then secondly for the most common SORTING in the neighborhood,
  ;;;; lastly we choose the better value.
  ;; Computing for my SORTING
  let myNit Nit ;; SORTING is already set, NIT was already computed, we just store it in new variable for sure
  let mySorting sorting ;; we have to store respective SORTING

  ;; Computing for their SORTING - it would be possible use here DO-IMITATION routine,
  ;; but for sure, we write it here again
  ;; (may be in the future versions of DO-IMITATION will not be possible or suitable use this routine).
  set sorting one-of modes [sorting] of link-neighbors  ;; very condensed version of code used for DO-IMITATION
  let theirNit satisfaction ;; we compute NIT for the most common value of SORTING
  let theirSorting sorting ;; we have to store respective SORTING

  ;; Setting SORTING to better value
  ifelse theirNit = myNit [
    set sorting one-of (list mySorting theirSorting) ;; in case of tie we choose randomly
  ][set sorting ifelse-value (myNit > theirNit) [mySorting] [theirSorting]] ;; else we choose better value
end

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
@#$#@#$#@
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@#$#@#$#@
## PREDECESORS
The code is ispired by the code of Giangiacomo Bravo (2013), who modeled ecological behavior, but with 4 different behaviors in 3 different cathegories. We are modeling here just one behavior, so the majority of code is done from the scratch.

Idea of the model, equations etc. is based on papers of Wander Jäger and Marco Janssen from the start of the millenium (2001, 2002) and their idea of consumat (ibid.).

Sorry for not filling the rest of info tab, we will do it... probably not soon, but definitely one day :)

František (c) 2019

## 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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@#$#@#$#@
NetLogo 6.1.0
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
<experiments>
  <experiment name="experimentV01" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>count turtles with [simSorting = 1]</metric>
    <metric>count turtles with [simSorting = 2]</metric>
    <metric>count turtles with [simSorting = 3]</metric>
    <metric>count turtles with [simSorting = 4]</metric>
    <steppedValueSet variable="RS" first="1" step="1" last="30"/>
    <enumeratedValueSet variable="pReverseSorting">
      <value value="0.085"/>
      <value value="0.125"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauN">
      <value value="0.35"/>
      <value value="0.45"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauU">
      <value value="0.5"/>
      <value value="0.6"/>
    </enumeratedValueSet>
    <steppedValueSet variable="closeLinks" first="1" step="1" last="5"/>
    <enumeratedValueSet variable="randomLinks">
      <value value="0.05"/>
      <value value="0.1"/>
      <value value="0.15"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomSeed?">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sigma">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sortingPrice">
      <value value="1.05"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff12">
      <value value="0.095"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff23">
      <value value="0.495"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff34">
      <value value="0.805"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="steps">
      <value value="105"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="experimentV02" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>count turtles with [simSorting = 1]</metric>
    <metric>count turtles with [simSorting = 2]</metric>
    <metric>count turtles with [simSorting = 3]</metric>
    <metric>count turtles with [simSorting = 4]</metric>
    <steppedValueSet variable="RS" first="31" step="1" last="100"/>
    <enumeratedValueSet variable="pReverseSorting">
      <value value="0.085"/>
      <value value="0.125"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauN">
      <value value="0.35"/>
      <value value="0.45"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauU">
      <value value="0.5"/>
      <value value="0.6"/>
    </enumeratedValueSet>
    <steppedValueSet variable="closeLinks" first="1" step="1" last="5"/>
    <enumeratedValueSet variable="randomLinks">
      <value value="0.05"/>
      <value value="0.1"/>
      <value value="0.15"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomSeed?">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sigma">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sortingPrice">
      <value value="1.05"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff12">
      <value value="0.095"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff23">
      <value value="0.495"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff34">
      <value value="0.805"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="steps">
      <value value="105"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="experimentV03" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>count turtles with [simSorting = 1]</metric>
    <metric>count turtles with [simSorting = 2]</metric>
    <metric>count turtles with [simSorting = 3]</metric>
    <metric>count turtles with [simSorting = 4]</metric>
    <steppedValueSet variable="RS" first="101" step="1" last="200"/>
    <enumeratedValueSet variable="pReverseSorting">
      <value value="0.085"/>
      <value value="0.125"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauN">
      <value value="0.35"/>
      <value value="0.45"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauU">
      <value value="0.5"/>
      <value value="0.6"/>
    </enumeratedValueSet>
    <steppedValueSet variable="closeLinks" first="1" step="1" last="5"/>
    <enumeratedValueSet variable="randomLinks">
      <value value="0.05"/>
      <value value="0.1"/>
      <value value="0.15"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomSeed?">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sigma">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sortingPrice">
      <value value="1.05"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff12">
      <value value="0.095"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff23">
      <value value="0.495"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff34">
      <value value="0.805"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="steps">
      <value value="105"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="experimentV04" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>count turtles with [simSorting = 1]</metric>
    <metric>count turtles with [simSorting = 2]</metric>
    <metric>count turtles with [simSorting = 3]</metric>
    <metric>count turtles with [simSorting = 4]</metric>
    <steppedValueSet variable="RS" first="201" step="1" last="400"/>
    <enumeratedValueSet variable="pReverseSorting">
      <value value="0.085"/>
      <value value="0.125"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauN">
      <value value="0.35"/>
      <value value="0.45"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauU">
      <value value="0.5"/>
      <value value="0.6"/>
    </enumeratedValueSet>
    <steppedValueSet variable="closeLinks" first="1" step="1" last="5"/>
    <enumeratedValueSet variable="randomLinks">
      <value value="0.05"/>
      <value value="0.1"/>
      <value value="0.15"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomSeed?">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sigma">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sortingPrice">
      <value value="1.05"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff12">
      <value value="0.095"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff23">
      <value value="0.495"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff34">
      <value value="0.805"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="steps">
      <value value="105"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="experimentV05" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>count turtles with [simSorting = 1]</metric>
    <metric>count turtles with [simSorting = 2]</metric>
    <metric>count turtles with [simSorting = 3]</metric>
    <metric>count turtles with [simSorting = 4]</metric>
    <steppedValueSet variable="RS" first="401" step="1" last="600"/>
    <enumeratedValueSet variable="pReverseSorting">
      <value value="0.085"/>
      <value value="0.125"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauN">
      <value value="0.35"/>
      <value value="0.45"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauU">
      <value value="0.5"/>
      <value value="0.6"/>
    </enumeratedValueSet>
    <steppedValueSet variable="closeLinks" first="1" step="1" last="5"/>
    <enumeratedValueSet variable="randomLinks">
      <value value="0.05"/>
      <value value="0.1"/>
      <value value="0.15"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomSeed?">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sigma">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sortingPrice">
      <value value="1.05"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff12">
      <value value="0.095"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff23">
      <value value="0.495"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff34">
      <value value="0.805"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="steps">
      <value value="105"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="experimentV07" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>count turtles with [simSorting = 1]</metric>
    <metric>count turtles with [simSorting = 2]</metric>
    <metric>count turtles with [simSorting = 3]</metric>
    <metric>count turtles with [simSorting = 4]</metric>
    <steppedValueSet variable="RS" first="1" step="1" last="600"/>
    <enumeratedValueSet variable="pReverseSorting">
      <value value="0.1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauN">
      <value value="0.4"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauU">
      <value value="0.5"/>
    </enumeratedValueSet>
    <steppedValueSet variable="closeLinks" first="3" step="1" last="3"/>
    <enumeratedValueSet variable="randomLinks">
      <value value="0.1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomSeed?">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sigma">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sortingPrice">
      <value value="1.05"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff12">
      <value value="0.105"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff23">
      <value value="0.495"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff34">
      <value value="0.875"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="steps">
      <value value="105"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="experimentV06" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>count turtles with [simSorting = 1]</metric>
    <metric>count turtles with [simSorting = 2]</metric>
    <metric>count turtles with [simSorting = 3]</metric>
    <metric>count turtles with [simSorting = 4]</metric>
    <steppedValueSet variable="RS" first="601" step="1" last="1600"/>
    <enumeratedValueSet variable="pReverseSorting">
      <value value="0.1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauN">
      <value value="0.4"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauU">
      <value value="0.5"/>
    </enumeratedValueSet>
    <steppedValueSet variable="closeLinks" first="1" step="1" last="5"/>
    <enumeratedValueSet variable="randomLinks">
      <value value="0.05"/>
      <value value="0.1"/>
      <value value="0.15"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomSeed?">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sigma">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sortingPrice">
      <value value="1.05"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff12">
      <value value="0.105"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff23">
      <value value="0.495"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff34">
      <value value="0.875"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="steps">
      <value value="105"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="sensitivityTest" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>count turtles with [simSorting = 1]</metric>
    <metric>count turtles with [simSorting = 2]</metric>
    <metric>count turtles with [simSorting = 3]</metric>
    <metric>count turtles with [simSorting = 4]</metric>
    <steppedValueSet variable="RS" first="601" step="1" last="1600"/>
    <enumeratedValueSet variable="pReverseSorting">
      <value value="0.05"/>
      <value value="0.15"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauN">
      <value value="0.3"/>
      <value value="0.5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauU">
      <value value="0.4"/>
      <value value="0.6"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="closeLinks">
      <value value="3"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomLinks">
      <value value="0.1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomSeed?">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sigma">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sortingPrice">
      <value value="1"/>
      <value value="1.1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff12">
      <value value="0.105"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff23">
      <value value="0.495"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff34">
      <value value="0.875"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="steps">
      <value value="105"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="experimentV08" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>count turtles with [simSorting = 1]</metric>
    <metric>count turtles with [simSorting = 2]</metric>
    <metric>count turtles with [simSorting = 3]</metric>
    <metric>count turtles with [simSorting = 4]</metric>
    <steppedValueSet variable="RS" first="1" step="1" last="300"/>
    <enumeratedValueSet variable="pReverseSorting">
      <value value="0.1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauN">
      <value value="0.4"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauU">
      <value value="0.5"/>
    </enumeratedValueSet>
    <steppedValueSet variable="closeLinks" first="1" step="1" last="5"/>
    <enumeratedValueSet variable="randomLinks">
      <value value="0.1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomSeed?">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomNeis?">
      <value value="true"/>
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sigma">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sortingPrice">
      <value value="1.05"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff12">
      <value value="0.105"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff23">
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    <enumeratedValueSet variable="steps">
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  <experiment name="experimentV08b" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>count turtles with [simSorting = 1]</metric>
    <metric>count turtles with [simSorting = 2]</metric>
    <metric>count turtles with [simSorting = 3]</metric>
    <metric>count turtles with [simSorting = 4]</metric>
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    <enumeratedValueSet variable="pReverseSorting">
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    <enumeratedValueSet variable="tauN">
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    <enumeratedValueSet variable="tauU">
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    </enumeratedValueSet>
    <steppedValueSet variable="closeLinks" first="6" step="1" last="10"/>
    <enumeratedValueSet variable="randomLinks">
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    <enumeratedValueSet variable="randomSeed?">
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    <enumeratedValueSet variable="randomNeis?">
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      <value value="false"/>
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    <enumeratedValueSet variable="sigma">
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    <enumeratedValueSet variable="sortingPrice">
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    <enumeratedValueSet variable="cutoff12">
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    <enumeratedValueSet variable="cutoff23">
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    <enumeratedValueSet variable="cutoff34">
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    <enumeratedValueSet variable="steps">
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  <experiment name="experimentV10" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>count turtles with [simSorting = 1]</metric>
    <metric>count turtles with [simSorting = 2]</metric>
    <metric>count turtles with [simSorting = 3]</metric>
    <metric>count turtles with [simSorting = 4]</metric>
    <metric>longTimeSorting%</metric>
    <metric>globalDiffSorting</metric>
    <steppedValueSet variable="RS" first="1" step="1" last="500"/>
    <enumeratedValueSet variable="pReverseSorting">
      <value value="0.1"/>
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    <enumeratedValueSet variable="tauN">
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    <enumeratedValueSet variable="tauU">
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    </enumeratedValueSet>
    <steppedValueSet variable="closeLinks" first="1" step="1" last="10"/>
    <enumeratedValueSet variable="randomLinks">
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    <enumeratedValueSet variable="randomSeed?">
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    <enumeratedValueSet variable="homophily?">
      <value value="true"/>
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sigma">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sortingPrice">
      <value value="1.05"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff12">
      <value value="0.355"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff23">
      <value value="0.605"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff34">
      <value value="0.805"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="steps">
      <value value="115"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="testOfParamsV10" repetitions="1" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <metric>globalDiffSorting</metric>
    <steppedValueSet variable="RS" first="1" step="1" last="3000"/>
    <enumeratedValueSet variable="pReverseSorting">
      <value value="0.1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauN">
      <value value="0.35"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="tauU">
      <value value="0.5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="closeLinks">
      <value value="3"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomLinks">
      <value value="0.1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="randomSeed?">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="homophily?">
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sigma">
      <value value="0"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="sortingPrice">
      <value value="1.05"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff12">
      <value value="0.355"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff23">
      <value value="0.605"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="cutoff34">
      <value value="0.805"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="steps">
      <value value="115"/>
    </enumeratedValueSet>
  </experiment>
</experiments>
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