groupFormation3D.nlogo3d

;; This is the first model for the high-risk high-gain project with Mike and Ashley
;; Here we'd like to apply HK/Deffuant model in more than 1D and look how agents adapt in >1D opinion space and whether they form groups.
;; 3D Version: THIS IS OUR THE BEST MODEL SO FAR implemented in 3D

;; Created:  2021-10-21 FranCesko
;; Edited:   2021-11-26 FranCesko
;; Encoding: windows-1250
;; NetLogo:  6.2.0
;;

;; IDEA: What about simply employ Spiral of Silence?
;;       Just simply -- general parameter on scale (0; 1> and probability of speaking her attitude/opinion,
;;       baseline is p==1, everybody speaks always, if p==0.5 so everybody has 0.5 probability to speak her opinion/attitude at given step,
;;       if succeeds - speaks in given step, if not - falls silent for the respective step.
;;       In HK mechanism, agent computes mean opinion of all speaking agents who are inside 'opinion boundary' (are not further than threshold).
;;       In Defuant, agent randomly takes one speaking agent inside the 'opinion boundary' and sets opinon as average of their opinions.
;; DONE!
;;
;; IDEA: Choose, how many opinions agents update: sometimes 1, 2, 3, 4 ...
;; DONE!
;;
;; IDEA: Give weights to opinions... Taken from media, or from interpersonal communication:
;;          - agents pick opinion according the importance, and update importance according number of contacts regarding the opinion
;;
;; IDEA: Compute clusters
;; DONE!
;;


;; WISHLIST:
;;     - differentiate between interpersonal communication and social media communication -- two overlapping networks with their own rules
;;     - how radicalization is possible? How polarization happens?
;;     - differential attraction
;;     - repulsion
;;     - media exposure will be crucial…we can ask abt opinion consistent content, opinion contrary, and “mainstream/mixed”…
;;       how to we conceptualize/model those in ABM? Is this too simplistic (eg, think of the different flavors of conservative media,
;;       ranging from CDU type media to extremist hate groups).
;;     - how to think about social media influencers (eg Trump before deplatforming)…
;;       is it possible to designate “superagents” who influence everyone sharing certain beliefs and see their effects…
;;       both reach everyone in a group and their opinions are very highly weighted (or people vary in how much they weight that opinion?
;;       Could estimate Twitter effect that way!  Perhaps one could even model how movement towards an opinion might influence the superagent
;;       to increase communication or change focus…
;;     - Employ homophily/heterophily principle at model start.
;;     - Control degree of opinion randomness at the start (different mean and SD of opinion for different groups)
;;     - Mike was thinking…after we do “superagents”, the Trump/foxnews avatars…one thing that would be neat and represent social reality
;;       is to have some kind of attraction to those who share beliefs (including superagents), but that decreases with close proximity…
;;       that way we have less ability/willingness to select attitude consistent sources around us (eg can’t escape family and coworkers),
;;       but can seek them elsewhere.  That might allow us to look at what happens in a more or less diverse local opinion environment, among other things.
;;


;; Parameters:
;;   Small-world network (Watts-Strogatz)
;;   agents have more than 1 type of attitude
;;   opinion on scale <-1;+1>
;;   boundary -- defines range as fraction of maximum possible Eucleid distance in n-dimensional space, this maximum depends on number of opinions: sqrt(opinions * 4)
;;

;; TO-DO:
;; 1) constructing file name for recording initial and final state of simulation
;; DONE!
;; 2) implementing recording into the model -- into setup and final steps (delete component detection and just record instead)
;; DONE!
;;
;; 3) Reviewer's comments:
;; The reviewer for your computational model Simulating Components of the Reinforcing Spirals Model and Spiral of Silence v1.0.0 has recommended that changes be made to your model. These are summarized below:
;; Very interesting model! It needs better documentation though, both within the code as comments, and the accompanying narrative documentation. Please consider following the ODD protocol or equivalent to describe your model in sufficient detail so that another could replicate the model based on the documentation.
;; Has Clean Code:
;; The code should be cleaned up and have more comments describing the intent and semantics of the variables.
;; Has Narrative Documentation:
;; The info tab is empty and the supplementary doc does not include sufficient detail to replicate the model. For example documentation please see ODD examples from other peer reviewed models in the library.
;; Is Runnable:
;; Runs well.
;; On behalf of the editors@comses.net, thank you for submitting your computational model(s) to CoMSES Net! Our peer review service is intended to serve the community and we hope that you find the requested changes will improve your model’s accessibility and potential for reuse. If you have any questions or concerns about this process, please feel free to contact us.
;;



extensions [nw]

breed [ghosts ghost]

turtles-own [Opinion-position Speak? Uncertainty Record Last-opinion Pol-bias Initial-opinion]

globals [main-Record components positions]


;; Initialization and setup
to setup
  ;; We erase the world and clean patches
  ca
  ;ask patches [set pcolor patch-color]

  ;; We initialize small-world network with random seed
  if set-seed? [random-seed RS]
  nw:generate-watts-strogatz turtles links N-agents n-neis p-random [
    fd (max-pxcor - 1)
    set size (max-pxcor / 10)
  ]

  ;; To avoid some random artificialities due to small-world network generation,
  ;; we have to set random seed again.
  if set-seed? [random-seed RS]
  ;; Then we migh initialize agents/turtles
  ask turtles [
    set Opinion-position n-values opinions [precision (1 - random-float 2) 3]  ;; We set opinions...
    set Last-opinion Opinion-position  ;; ...set last opinion as present opinion...
    set Initial-opinion Opinion-position  ;; ...we record opinion as initial opinion ...
    set Record n-values record-length [0]  ;; ... we prepare indicator of turtle's stability, at all positions we set 0 as non-stability...
    set speak? speaking  ;; ...checking whether agent speaks...
    set Pol-bias get-bias  ;; ... setting value of political bias...
    set Uncertainty get-uncertainty  ;;... setting value of Uncertainty.
    getColor  ;; Coloring the agents according their opinion.
    getPlace  ;; Moving agents to the opinion space according their opinions.
  ]

  ;; Coloring patches according the number of agents/turtles on them.
  ask patches [set pcolor patch-color]
  ;; Hiding links so to improve simulation speed performance.
  ask links [set hidden? TRUE]
  ;; Setting the indicator of change for the whole simulation, again as non-stable.
  set main-Record n-values record-length [0]

  reset-ticks

  ;;;; Finally, we record initial state of simulation
  ;; If we want we could construct filename to contain all important parameters shaping initial condition, so the name is unique stamp of initial state!
  if construct-name? [set file-name (word "Sims/Sims02_" RS "_" N-agents "_" p-random "_" n-neis "_" opinions "_" updating "_" boundary "_" boundary-drawn "_" p-speaking "_" mode ".csv")]
  ;; recording itself
  if record? [record-state-of-simulation]
end


;; Sub-routine which opens/creates *.csv file and stores there states of all turtles
to record-state-of-simulation
  ;; setting working directory
  ;set-current-directory directory

  ;;;; File creation and opening
  ;; If file exists at the start we delete it to start with clean file
  if ticks = 0 and file-exists? file-name [file-delete file-name]
  file-open file-name ;; This opens existing file (at the end) or creates file if doesn't exist (at the begining)
  ;; Writng the variable names in the first row at the start
  if ticks = 0 [
    file-write (word "ID, Uncertainty, ")  ;; ID of turtle and its Uncertainty
    foreach range Opinions [i -> file-write (word "Opinion" (i + 1) ", ")]   ;; and its Opinions
    foreach range N-agents [i -> file-write (word "Nei" i ", ")]  ;; Varibles for each turtle in the simulation whether it's linked (1) or not (0)
    file-print "Step"  ;; Last variable, the Step of simulation (0 for start, >0 for final state)
  ]

  ;; For writing state itself we firstly need to create list of sorted turtles 'srt'
  let srt sort turtles
  ;; Then we iterate over the list 'srt':
  foreach srt [t -> ask t [  ;; every turtle in the list...
    file-write (word "Nei" who ", " Uncertainty ", ")  ;; writes its ID, Uncertainty...
    foreach Opinion-position [op -> file-write (word precision(op) 3  ", ")] ;; Opinions ...
    foreach srt [tr -> file-write (word (ifelse-value (link-neighbor? tr) [1][0]) ", ")] ;; whether respective turtle is link neighbor (1) or not (0) ...
    file-print ticks ;; and finally, the step of simulation
    file-flush  ;; for larger simulations with many agents it will be safer flust the file after each row
  ]]

  ;; Finally, we close the file
  file-close
end


;; Sub-routine for assigning value of ideological bias
to-report get-bias
  ;; initialize 'bias' variable
  let bias 0

  ;; uniformly generating number from interval (0, + bias-margin) and then
  ;; moving it towards negative pole by (bias-margin * bias-of-bias),
  ;; bias-of-bias is fraction [0, 1], 0 means full bias towards '+' pole, resulting in interval of bias [0, +bias-margin],
  ;; 1 means full bias towards '-' pole, interval [-bias-margin, 0], 0.5 means ballanced, resulting in interval [-bias-margin/2, +bias-margin/2]
  if bias-drawn = "uniform" [set bias precision ((random-float (bias-margin)) - (bias-margin * bias-of-bias)) 3]

  ;; report the result back
  report bias
end


;; sub-routine for assigning value of uncertainty to the agent
to-report get-uncertainty
  ;; We have to initialize empty temporary variable
  let uValue 0

  ;; Then we draw the value according the chosen method
  if boundary-drawn = "constant" [set uValue boundary + random-float 0]  ;; NOTE! 'random-float 0' is here for consuming one pseudorandom number to cunsume same number of pseudorandom numbers as "uniform"
  if boundary-drawn = "uniform" [set uValue precision (min-boundary + random-float (2 * boundary - min-boundary)) 3]
  if boundary-drawn = "normal" [
    set uValue precision (random-normal boundary sigma) 3
    while [uValue < min-boundary or uValue > 1] [
      set uValue precision (random-normal boundary sigma) 3
    ]
  ]

  ;; reporting value back for assigning
  report uValue
end


;; sub-routine for graphical representation -- it takes two opinion dimension and gives the agent on XY coordinates accordingly
to getPlace
  ;; check whether our cosen dimension is not bigger than maximum of dimensions in the simulation
  if X-opinion > opinions [set X-opinion 1]
  if Y-opinion > opinions [set Y-opinion 1]
  if Z-opinion > opinions [set Z-opinion 1]


  ;; then we rotate the agent towards the future place
  facexyz ((item (X-opinion - 1) opinion-position) * max-pxcor) ((item (Y-opinion - 1) opinion-position) * max-pycor) ((item (Z-opinion - 1) opinion-position) * max-pzcor)

  ;; lastly we move agent on the place given by opinion dimensions chosen for X and Y coordinates
  set xcor (item (X-opinion - 1) opinion-position) * max-pxcor
  set ycor (item (Y-opinion - 1) opinion-position) * max-pycor
  set zcor (item (Z-opinion - 1) opinion-position) * max-pzcor
end


;; sub routine for coloring agents according their average opinion across all dimensions --
;; useful for distinguishing agents with same displayed coordinates, but differing in other opinion dimensions,
;; then we see at one place agents with different colors.
to getColor
  ;; speaking agents are colored from very dark red (average -1) through red (average 0) to very light red (average +1)
  ifelse speak? [
    set color 15 + 4 * mean(opinion-position)
    set size (max-pxcor / 10)
  ]
  ;; silent agent are white and of zero size, to just show the speaking one -- later we might parametrize this if we want...
  [
    set color white
    set size 0
  ]
end


;; Sub routine for dissolving whether agent speaks at the given round/step or not
to-report speaking
  ;; We just generate random number and compare it with parameter 'p-speaking' -- this code directly produces values TRUE or FALSE
  report p-speaking > random-float 1
end


;; sub-routine for visual purposes -- colors empty patches white, patches with some agents light green, with many agents dark green, with all agents black
to-report patch-color
  report 59.9 - (9.8 * (ln(1 + count turtles-here) / ln(N-agents)))
end


;; Main routine
to go

  ;; Check whether we set properly parameter 'updating' --
  ;; if we want update more dimensions than exists in simulation, then we set 'updating' to max of dimensions, i.e. 'opinions'
  if updating > opinions [set updating opinions]

  ask turtles [
    ;; speaking and coloring
    set speak? speaking
    getColor
    getPlace

    ;; storing previous opinion position as 'Last-opinion'
    set Last-opinion Opinion-position

    ;; Mechanism of opinion change
    if model = "HK" [
      change-opinion-HK
    ]
    ;; Note: Now here is only Hegselmann-Krause algorithm, but in the future we might easily employ other algorithms here!
  ]

  ;; Patches update color according the number of turtles on it.
  ask patches [set pcolor patch-color]

  ;; We have to check here the change of opinions, resp. how many agents changed,
  ;; and record it for each agent and also for the whole simulation
  ;; Turtles update their record of changes:
  ask turtles [
    ;; we take 1 if opinion is same, we take 0 if opinion changes, then
    ;; we put 1/0 on the start of the list Record, but we omit the last item from Record
    set Record fput ifelse-value (Last-opinion = Opinion-position) [1][0] but-last Record
  ]
  ;; Then we might update it for the whole:
  set main-Record fput precision (mean [mean Record] of turtles) 3 but-last main-Record

  tick

  ;; Finishing condition:
  ;; 1) We reached state, where no turtle changes for RECORD-LENGTH steps, i.e. average of MAIN-RECORD (list of averages of turtles/agents RECORD) is 1 or
  ;; 2) We reached number of steps specified in MAX-TICKS
  if mean main-Record = 1 or ticks = max-ticks [record-state-of-simulation stop]
end


;; sub-routine for updating opinion position of turtle according the Hegselmann-Krause (2002) model
to change-opinion-HK
  ;; initialization of agent set 'influentials' containing agents whose opinion positions uses updating agent
  let influentials nobody
  ;; 1) updating agent uses only visible link neighbors in small-world network
  let visibles other link-neighbors with [color != white]

  ;; 2) we have different modes for finding influentials:
  ;; 2.1) in mode "I got them!" agent looks inside his boundary (opinion +/- uncertainty),
  ;;      i.e. agent takes opinions not that much far from her opinion
  if mode = "openly-listen" [
    ;; we compute 'lim-dist' -- it is the numerical distance in given opinion space
    let lim-dist (Uncertainty * sqrt(opinions * 4))
    ;; we set as influentials agents with opinion not further than 'lim-dist'
    set influentials visibles with [opinion-distance <= lim-dist]
  ]

  ;; 2.1) in mode "They got me!" agent looks inside whose boundaries (opinion +/- uncertainty)
  ;;       she is, i.e. agents takes opinions spoken with such a big uncertainty that it matches her own opinion
  if mode = "vaguely-speak"  [
    ;; Note: Here is used the 'Uncetainty' value of called agent, agent who might be used for updating,
    ;;       not 'Uncertainty' of calling agent who updates her opinion.
    set influentials visibles with [opinion-distance <= (Uncertainty * sqrt(opinions * 4))]
  ]

  ;; 3) we also add the updating agent into 'influentials'
  set influentials (turtle-set self influentials)
  if social-bias [
    ;; in case Social-bias is effective, then updating agent will create 'ghost',
    ;; its younger self with same opinion which agent had at the start of simulation (result of its socialization).
    hatch-ghosts 1 [set opinion-position [Initial-opinion] of myself]
    ;; Then we include this 'ghost' into the agent-set 'influentials'
    set influentials (turtle-set ghosts influentials)
  ]

  ;; we check whether there is someone else then calling/updating agent in the agent set 'influentials'
  if count influentials > 1 [

    ;; here we draw a list of dimensions which we will update:
    ;; by 'range opinions' we generate list of integers from '0' to 'opinions - 1',
    ;; by 'n-of updating' we randomly take 'updating' number of integers from this list
    ;; by 'shuffle' we randomize order of the resulting list
    let op-list shuffle n-of updating range opinions

    ;; we initialize counter 'step'
    let step 0

    ;; we go through the while-loop 'updating' times:
    while [step < updating] [
      ;; we initialize/set index of updated opinion dimension according the items on the 'op-list',
      ;; note: since we use while-loop, we go through each item of the 'op-list', step by step, iteration by iteration.
      let i (item step op-list)

      ;; them we update dimension of index 'i' drawn from the 'op-list' in the previous line:
      ;; 1) we compute average position in given dimension of the calling/updating agent and all other agents from agent set 'influentials'
      ;;    by the command '(mean [item i opinion-position] of influentials)', and
      ;; 2) we update average value through the pol-bias
      ;; 3) we set value as new opinion position by command 'set opinion-position replace-item i opinion-position X' where 'X' is the mean opinion (ad 1, see line above)

      ;; ad 1: averge position computation
      let val  precision (mean [item i opinion-position] of influentials) 3 ;; NOTE: H-K model really assumes that agent adopts immediatelly the 'consesual' position
      ;; ad 2: application of bias
      if pol-bias < 0 [set val (((1 + val) * (1 + pol-bias)) - 1)]
      if pol-bias > 0 [set val (1 - ((1 - val) * (1 - pol-bias)))]
      ;; ad 3: assigning the value 'val'
      set opinion-position replace-item i opinion-position val

      ;; advancement of counter 'step'
      set step step + 1
    ]
  ]

  ;; In case there are some ghosts, kill them!
  if (count ghosts) > 0 [ask ghosts [die]]
end


;; sub-routine for computing opinion distance of two comparing agents
to-report opinion-distance
  ;; we store in temporary variable the opinion of the called and compared agent
  let my opinion-position

  ;; we store in temporary variable the opinion of the calling and comparing agent
  let her [opinion-position] of myself

  ;; we initialize counter of step of comparison -- we will compare as many times as we have dimensions
  let step 0

  ;; we initialize container where we will store squared distance in each dimension
  let dist 0

  ;; while loop going through each dimension, computiong distance in each dimension, squarring it and adding in the container
  while [step < opinions] [
    ;; computiong distance in each dimension, squarring it and adding in the container
    set dist dist + (item step my - item step her) ^ 2

    ;; advancing 'step' counter by 1
    set step step + 1
  ]

  ;; computing square-root of the container 'dist' -- computing Euclidean distance -- and setting it as 'dist'
  set dist sqrt dist

  ;; reporting Euclidean distance
  report dist
end
@#$#@#$#@
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SLIDER
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PLOT
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NIL
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"" ""
PENS
"default" 0.001 1 -16777216 true "" "histogram [pol-bias] of turtles"

SWITCH
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INPUTBOX
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SLIDER
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INPUTBOX
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directory
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BUTTON
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RECORD!
record-state-of-simulation
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OBSERVER
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SWITCH
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construct-name?
construct-name?
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SWITCH
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record?
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SLIDER
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max-ticks
max-ticks
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100
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SLIDER
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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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@#$#@#$#@
NetLogo 3D 6.2.0
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
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@#$#@#$#@
@#$#@#$#@
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@#$#@#$#@
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