Child of Version_20250927-1_Spread of Opinions Influenced by Group Effects reward metablock plus memes v2

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WHAT IS IT?

This simulator was developed by the Public Opinion Research Group to model how political and social opinions spread, polarize, and evolve within a connected population. It is based on an agent-based model (ABM) where each agent represents an individual characterized by their opinion, prevalence (depth of conviction), influence, and social links.

Agents interact over time through probabilistic influence, producing emergent collective behaviors such as polarization, convergence, or fragmentation.

The model was originally designed to study the Québec electorate, linking voters’ attitudes to their internal representations (memes) underlying support or opposition to political issues.


HOW TO USE IT

  1. Adjust the population size with the pop slider.
  2. Click Setup to initialize agents and their links (background set to black).
  3. Click Go to start or pause the simulation.

3D Representation

  • X-axis → Opinion (−1 to +1)
  • Y-axis → Prevalence (0–99)
  • Z-axis → Influence (0–1)

Colors:

  • Blue → positive/right-leaning opinion
  • Red → negative/left-leaning opinion
  • Yellow → meta-influencers
  • Green links → same sign (agreement)
  • Gray links → opposite sign (disagreement)

USER INTERFACE OVERVIEW

1. General Controls

  • Setup → initialize agents and reset view.
  • Go → start/stop simulation.
  • in_file → load saved agent data.
  • refresh / cumulative → control graph display.

2. Population and Iteration Parameters

  • pop → number of agents.
  • nb_try, max_iter, threshold → govern experiment repetitions.
  • choice_iter → select iteration to reload saved state.

3. Network Dynamics

Agents form and dissolve links depending on opinion similarity and probabilistic thresholds.

  • link-removal-threshold → above this distance, links may break.
  • link-formation-threshold → below this distance, new links may form.
  • prob → global probability of link change.
  • linksdown / linksup → limits number of link changes per tick.
  • bridge-prob → probability of creating “bridging” links between opposite camps.

4. Meta-Influencers

Special agents with fixed influence = 1 who can stabilize or polarize the network.

  • meta-influencers-selection → choose distribution (All, Left, Right).
  • meta-influencers → proportion of meta agents.
  • meta-links, meta-min, meta-max → number of links per meta agent.
  • meta-ok → enable or disable meta-influencers.
  • vary-influence → if ON, influence grows after successful transmission and decreases after failure.
  • metablock → if ON, meta-influencers cannot switch opinion polarity (they remain Left or Right).

5. Opinion Dynamics

Key Parameters

  • rate-infl → rate of influence adjustment per success.
  • modulation-prevalence → toggle prevalence variation with opinion change.
  • rate-modulation → rate of prevalence adaptation.
  • noise → probability of random opinion drift.
  • polarization-factor → reduces adoption probability across distant opinions.
  • prevalence-weight → adjusts how much prevalence influences adoption.
  • adoption-floor → minimal adoption probability, even with large opinion gaps.

6. Group Impact

Adoption probability is influenced by group alignment.

  • group-impact-modeall (all neighbours) or k-nearest (closest in opinion).
  • group-k → number of neighbours considered in k-nearest mode.
  • group-impact-weight → strength of group effect.
  • group-impact-alpha → non-linearity:

    • <1 → small minorities have strong influence.
    • =1 → linear response.
    • >1 → large majorities dominate influence.

7. Reward Mechanism

Agents gain or lose influence potential depending on success in persuasion.

  • reward-step → increment in transmission probability per success.
  • reward-cap → maximum bonus that can be accumulated.
  • reward-scope → defines which agents are rewarded (both, left-only, right-only).
  • reward-prev-delta → increase in the target’s prevalence after adoption.
  • reward-decay → gradual reduction of accumulated reward.

8. Meme-Based Cognition

When use-memes? is ON, opinions result from internal stocks of meme-plus and meme-minus, representing arguments “for” and “against”.

  • meme-max → maximum meme capacity per agent.
  • meme-gain → increase in memes after successful influence.
  • meme-anti-leak → erosion rate of opposite meme stock.
  • meme-decay → forgetting rate.

Derived variables:

  • Opinion = balance between positive and negative memes.
  • Prevalence = total memes held by the agent.

9. External Events (Including Repetition)

External shocks simulate real-world influences such as political crises or media waves.

Main Controls

  • event → triggers a one-time event.
  • auto_event → automates events.
  • tick-event → iteration at which next event occurs.
  • repeat-event → activates repeated events.
  • event-pace → defines interval (in ticks) between repeated events.
  • event-prob-max → maximum proportion of affected agents.
  • lowmeme / highmeme, low-prev / high-prev → restrict which agents are targeted.
  • event_size → magnitude of opinion shift.
  • prev_change → change in prevalence after event.

Event Repetition Example

If auto_event and repeat-event is ON and event-pace = 10, the same event reoccurs every 10 iterations. Combined with event-prob-max, this enables realistic modeling of recurring but partial media shocks.

  • 1.0 → global shock (entire population)
  • 0.1 → limited audience
  • 0.01 → local perturbations only

OUTPUTS

Monitors

  • % Left / Right agents
  • Median opinion, prevalence, influence
  • Number of inversions, links created/removed
  • Event frequency

Graph

Tracks evolution of global indicators over time.

CSV Export

If csv-export is ON, simulation results are recorded for each trial.


THINGS TO NOTICE

  • Observe polarization and convergence patterns.
  • Study how meta-influencers stabilize or destabilize clusters.
  • Analyze the role of memetic representation in resilience or volatility.
  • Test how repeated external events alter long-term opinion structures.

CREDITS

  • Concept: Public Opinion Research Group (GROP)
  • Implementation: Pierre-Alain Cotnoir (2015–2025)
  • AI-assisted design: GPT-4 / GPT-5 (2024–2025)
  • Contact: pacotnoir@gmail.com

Comments and Questions

Please start the discussion about this model! (You'll first need to log in.)

Click to Run Model

extensions [sound nw] ;; For using sound and Network package

globals [
  min-prevalence
  max-prevalence
  meta-influencers-droit
  meta-influencers-gauche
  iter change total inversion try major fractale
  ordonnee abcisse profondeur
  list_data file-in in_data repet_data
  links-dead links-create meta-agents meta-links meta-create Interactions %Major

  ;; === CSV export ===
  csv-export
  csv-basename
  csv-file
  csv-open?

  ;; === Paramètres d’inversion / ponts (sliders UI possibles) ===
  ;;prevalence-weight      ;; >= 0 ; amplification du rôle de Δprégnance
  ;;adoption-floor         ;; [0..1] ; plancher minimal pour la pénalité de polarisation
  ;;bridge-prob            ;; [0..1] ; probabilité de créer un lien-pont (opinion éloignée)

  ;; === Paramètres de RÉCOMPENSE (sliders/inputs UI) ===
  ;;reward-step        ;; palier d’augmentation du bonus à chaque succès (ex: 0.05)
  ;;reward-cap         ;; plafond du bonus cumulé (ex: 0.50)
  ;;reward-scope       ;; "both" | "left-only" | "right-only"
  ;;reward-prev-delta  ;; hausse de prégnance du ciblé au succès (ex: 0..5), 0 = off
  ;;reward-decay       ;; décroissance du bonus par tick (ex: 0..0.01), 0 = off
]

turtles-own [
  opinion         ;; [-1, 1]
  prevalence      ;; [min-prevalence, max-prevalence]
  agent-type      ;; "Right side" | "Left side"
  influence       ;; [0, 1]
  opinion-previous
  influence-previous
  x3d y3d z3d

  ;; Mèmes (stock pro/anti)
  meme-plus
  meme-minus

  ;; variables utilitaires
  old-opinion
  proposed-opinion

  ;; Récompense de transmission (bonus multiplicatif p-adopt côté émetteur)
  tx-bonus
]

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; SETUP
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to setup
  clear-all
  set repet_data false
  set iter 0
  set min-prevalence 0
  set max-prevalence 99
  set-default-shape turtles "person"
  set try 1
  set major 0
  set tick-event 50
  set links-dead 0
  set links-create 0
  set meta-create 0
  set meta-agents 0
  set change 0
  set total 0
  set inversion 0
  set fractale 0
  if vary-influence = true or meta-ok = true [ set meta-links meta-min ]

  ;; === Defaults CSV ===
  if not is-boolean? csv-export [ set csv-export false ]
  if (not is-string? csv-basename) or (csv-basename = "") [ set csv-basename "run" ]
  set csv-open? false

  ;; === Defaults IMPACT DE GROUPE ===
  if (not is-string? group-impact-mode) [ set group-impact-mode "all" ]    ;; "all" | "k-nearest"
  if (not is-number? group-k) [ set group-k 10 ]
  if (not is-number? group-impact-weight) [ set group-impact-weight 0.5 ]
  if (not is-number? group-impact-alpha) [ set group-impact-alpha 1.0 ]

  ;; === Default switches ===
  if not is-boolean? show-links? [ set show-links? false ]
  if not is-boolean? metablock   [ set metablock false ]

  ;; === Defaults inversion/ponts ===
  if (not is-number? prevalence-weight) [ set prevalence-weight 1.5 ]
  if (not is-number? adoption-floor)    [ set adoption-floor 0.02 ]
  if (not is-number? bridge-prob)       [ set bridge-prob 0.10 ]

  ;; === Defaults REWARD ===
  if not is-number? reward-step       [ set reward-step 0.05 ]
  if not is-number? reward-cap        [ set reward-cap  0.50 ]
  if not is-string? reward-scope      [ set reward-scope "both" ]
  if not is-number? reward-prev-delta [ set reward-prev-delta 0 ]
  if not is-number? reward-decay      [ set reward-decay 0 ]

  ;; === Defaults MEMES ===
  if not is-boolean? use-memes?    [ set use-memes? false ]
  if not is-number? meme-max       [ set meme-max 100 ]
  if not is-number? meme-gain      [ set meme-gain 1.0 ]
  if not is-number? meme-anti-leak [ set meme-anti-leak 0.0 ]
  if not is-number? meme-decay     [ set meme-decay 0.0 ]

  set-background-black

  create
  rapport
end 

to create
  ;; Right side
  create-turtles pop / 2 [
    set agent-type "Right side"
    set opinion random-float 1                     ;; (0,1)
    set color blue
    set prevalence random-float (opinion * 100)
    set influence random-float 1
    set opinion-previous opinion
    set influence-previous influence
    set tx-bonus 0

    ;; init mèmes cohérente avec (prevalence, opinion)
    let tot initial-prevalence-to-memes prevalence
    ifelse opinion >= 0 [
      set meme-plus  tot * (0.5 + 0.5 * abs opinion)
      set meme-minus tot - meme-plus
    ] [
      set meme-minus tot * (0.5 + 0.5 * abs opinion)
      set meme-plus  tot - meme-minus
    ]
    update-3d self
  ]

  ;; Left side
  create-turtles pop / 2 [
    set agent-type "Left side"
    set opinion (random-float 1 - 1)               ;; (-1,0)
    set color red
    set prevalence random-float (abs opinion * 100)
    set influence random-float 1
    set opinion-previous opinion
    set influence-previous influence
    set tx-bonus 0

    ;; init mèmes cohérente avec (prevalence, opinion)
    let tot initial-prevalence-to-memes prevalence
    ifelse opinion >= 0 [
      set meme-plus  tot * (0.5 + 0.5 * abs opinion)
      set meme-minus tot - meme-plus
    ] [
      set meme-minus tot * (0.5 + 0.5 * abs opinion)
      set meme-plus  tot - meme-minus
    ]
    update-3d self
  ]

  ;; Méta-influenceurs initiaux
  influenceurs

  reset-ticks

  set total 0
  set change 0
  set Interactions 0
  set %Major 0
  update-networks

  recolor-links
  apply-link-visibility
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; SORTIES / RAPPORT
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to rapport
  if output = "Statistics" [
    output-print (word
      "Try ; " "Iter ; "
      "Opinion global ; "
      "Opinion right side ; "
      "Opinion left side ; "
      "Prevalence right side ; "
      "Prevalence left side ; "
      "Influence right side ; "
      "Influence left side ; "
      "Left % ; "  "Right % ; "
      "Links-Remove ; " "Links-Create ; "
      "Inversion % ; " "change ; " "total ; " "fractale")
  ]
  if output = "Values" [
    output-print (word "Try ; " "Ticks ; "  "Agents ; "
                        "Prevalence ; " "Opinion ; " "Influence ; " "meme droit ;" "meme plus ; " "meme minus")
  ]

  if output = "File" [
   ; ask turtles [
     ; let pre prevalence
      ;let mem opinion
     ; let infl influence
     ; let ti ticks
      output-print (word "ti ; "  "pre ;"  "mem ; " "infl ; " "agent")
   ; ]
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; META-INFLUENCEURS
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to influenceurs
  ;; All
  if meta-influencers-selection = "All" [
    let k round (count turtles * meta-influencers / 100)
    if k > 0 [
      ask n-of k turtles [
        if (prevalence >= prev-low and prevalence <= prev-high) [
          set influence 1
          set color yellow
          set meta-agents meta-agents + 1
        ]
      ]
    ]
  ]
  ;; Right side
  if meta-influencers-selection = "Right side" [
    set meta-influencers-droit round (count turtles * meta-influencers / 100)
    let candidates turtles with [opinion > 0]
    let k min list meta-influencers-droit count candidates
    if k > 0 [
      ask n-of k candidates [
        if (prevalence > prev-low and prevalence <= prev-high) [
          set influence 1
          set color yellow
          set meta-agents meta-agents + 1
        ]
      ]
    ]
  ]
  ;; Left side
  if meta-influencers-selection = "Left side" [
    set meta-influencers-gauche round (count turtles * meta-influencers / 100)
    let candidates turtles with [opinion < 0]
    let k min list meta-influencers-gauche count candidates
    if k > 0 [
      ask n-of k candidates [
        if (prevalence > prev-low and prevalence <= prev-high) [
          set influence 1
          set color yellow
          set meta-agents meta-agents + 1
        ]
      ]
    ]
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; OUTILS MÉTA / VETO
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; Un agent est "méta" s'il est jaune OU si son influence vaut 1

to-report meta?
  report (color = yellow) or (influence = 1)
end 

;; Change l'opinion en respectant le veto méta quand metablock = true.
;; Solution 1 (renforcement sans inversion) :
;; - si la nouvelle opinion change le signe d’un méta alors que metablock=ON,
;;   on conserve le signe ACTUEL et on prend la magnitude MAX(abs(old), abs(new)).

to maybe-set-opinion [ new-op ]
  let old-op opinion
  let bounded-op max list -1 min list 1 new-op
  if metablock and meta? and (sign old-op != sign bounded-op) [
    let mag max list (abs old-op) (abs bounded-op)
    set opinion (sign old-op) * mag
    stop
  ]
  set opinion bounded-op
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; BOUCLE PRINCIPALE
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to go
  ifelse (iter < max_iter) [
    if iter > 0 [ set Interactions (total / iter) ]
    if iter > 0 [ set %Major (major / iter * 100) ]
    set iter iter + 1
    set meta-create 0

    if (iter = 1 and csv-export and not csv-open?) [ csv-begin ]

    ifelse auto_event = true [ if (tick-event = iter) 
      [event if repeat-event [set tick-event (tick-event + event-pace)]] ] 
    [set tick-event (iter + event-pace)]
    if meta-ok = true [ meta ]

    update-opinions
    if network = true [ update-networks ]
    recolor-links
    apply-link-visibility

    if output = "Statistics" [
      let avg-opinion mean [opinion] of turtles
      let positive-opinion safe-median (turtles with [opinion >= 0]) "opinion"
      let negative-opinion safe-median (turtles with [opinion < 0])  "opinion"
      let positive-prevalence (safe-median (turtles with [opinion >= 0]) "prevalence") / 100
      let negative-prevalence (safe-median (turtles with [opinion < 0])  "prevalence") / 100
      let positive-influence safe-median (turtles with [opinion >= 0]) "influence"
      let negative-influence safe-median (turtles with [opinion < 0])  "influence"
      let Left%  (count turtles with [opinion < 0])  / (pop / 100)
      let Right% (count turtles with [opinion >= 0]) / (pop / 100)
      let ti iter
      output-print (word try " ; " ti " ; " avg-opinion " ; "
                        positive-opinion " ; " negative-opinion " ; "
                        positive-prevalence " ; " negative-prevalence " ; "
                        positive-influence " ; " negative-influence " ; "
                        Left% " ; " Right% " ; "
                        links-dead " ; " links-Create " ; "
                        inversion " ; " change " ; " total " ; " fractale)
    ]

    tick

    if (change > 1 and total > 1) [
      set fractale (ln total) / (ln change)
    ]

    if (cumulative = false) [
      set change 0
      set total 0
    ]

    colorer

    if (refresh = true) [
      if ticks > 200 [ reset-ticks clear-plot ]
    ]

    if threshold <= (count turtles with [opinion > 0]) / (pop / 100) [
      set major major + 1
    ]

    if csv-export [ csv-row ]

  ] [
    ifelse (try < nb_try) [
      if csv-export [ csv-end ]

      set try try + 1
      set major 0
      clear-turtles
      clear-plot
      set change 0
      set total 0
      set fractale 0
      set meta-links meta-min
      set iter 0
      set tick-event 50
      set links-create 0
      set links-dead 0
      set meta-create 0
      set meta-agents 0
      set min-prevalence 0
      set max-prevalence 99
      ifelse (repet_data = true) [
        data
      ] [
        create
        set meta-links meta-min
      ]
    ] [
      if csv-export [ csv-end ]
      sound:play-note "Tubular Bells" 60 64 1
      stop
    ]
   
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; MISE À JOUR DES OPINIONS (effet de groupe + récompenses + mèmes + veto méta)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to update-opinions
  ask turtles [
    set opinion-previous opinion
    let target one-of link-neighbors
    if target != nobody [
      ;; Δprégnance (tolérance 1)
      let raw-dprev ([prevalence] of target) - prevalence
      if raw-dprev < 1 [ set raw-dprev 0 ]
      let dprev raw-dprev / max-prevalence

      if dprev > 0 [
        ;; distance mémique en valeur absolue des signes
        let dmem abs(abs(opinion) - abs([opinion] of target))

        ;; base-prob + pénalité de polarisation
        let base-prob dprev * prevalence-weight
        let pol-penalty max list adoption-floor (1 - polarization-factor * dmem)

        ;; probabilité brute (sans groupe), pondérée par influence & tx-bonus de l’émetteur
        let p-adopt base-prob * pol-penalty * [influence] of target * (1 + [tx-bonus] of target)

        ;; effet de groupe
        let sgn-emetteur sign ([opinion] of target)
        let gprob group-alignment-effective self sgn-emetteur
        let w group-impact-weight
        let alpha group-impact-alpha
        set p-adopt p-adopt * ((1 - w) + (w * (gprob ^ alpha)))

        ;; borne [0,1]
        if p-adopt < 0 [ set p-adopt 0 ]
        if p-adopt > 1 [ set p-adopt 1 ]

        ;; tirage
        if random-float 1 < p-adopt [
          set old-opinion opinion
          set proposed-opinion [opinion] of target

          ifelse use-memes? [
            ;; renforcement des mèmes du receveur selon le signe de l’émetteur
            transmit-memes target
            ;; opinion & prevalence dérivées des mèmes (protégées par metablock)
            recompute-from-memes
          ] [
            ;; adoption « historique » protégée par veto
            maybe-set-opinion proposed-opinion
          ]

          ;; si veto/reflexion n’a pas modifié le signe et que rien n’a changé → pas de reward
          if opinion = old-opinion [ stop ]

          ;; succès de transmission
          set total total + 1

          ;; récompense à l’émetteur si éligible
          let emitter-sign sign ([opinion] of target)
          let eligible? (reward-scope = "both") or
                         (reward-scope = "left-only"  and emitter-sign < 0) or
                         (reward-scope = "right-only" and emitter-sign >= 0)
          if eligible? [
            ask target [
              set tx-bonus min (list reward-cap (tx-bonus + reward-step))
            ]
          ]

          ;; option : hausse de prégnance du ciblé
          if reward-prev-delta > 0 [
            set prevalence min (list max-prevalence (prevalence + reward-prev-delta))
          ]

          ;; dynamique d’influence (logique existante)
          set influence-previous influence
          if vary-influence = true [
            if abs(old-opinion) > abs(opinion) [
              set influence min (list 1 (influence + rate-infl))
              if (influence-previous < 1 and influence = 1) [
                if meta-ok = true [
                  if meta-links < meta-max [ set meta-links meta-links + 1 ]
                  set meta-agents meta-agents + 1
                ]
                set color yellow
              ]
            ]
            if abs(old-opinion) < abs(opinion) [
              set influence max (list 0 (influence - rate-infl))
              if (influence < influence-previous and influence-previous = 1) [
                if meta-ok = true [
                  set meta-agents meta-agents - 1
                  ifelse opinion >= 0 [ set color blue ] [ set color red ]
                ]
              ]
            ]
          ]

          ;; comptage des inversions (si non bloquée par veto/réflexion)
          if (sign old-opinion) != (sign opinion) [
            set change change + 1
          ]
        ]
      ]
    ]

    ;; modulation de la prévalence
    if modulation-prevalence = true [
      if prevalence > abs opinion * 100 [
        set prevalence prevalence - abs(opinion - opinion-previous) * influence * Rate-modulation
      ]
      if prevalence < abs opinion * 100 [
        set prevalence prevalence + abs(opinion - opinion-previous) * influence * Rate-modulation
      ]
      if prevalence < min-prevalence [ set prevalence min-prevalence ]
      if prevalence > max-prevalence [ set prevalence max-prevalence ]
    ]

    ;; bruit additif (protégé par veto/réflexion)
    if random-float 1 < noise [
      let delta (random-float 0.4 - 0.2)
      maybe-set-opinion (opinion + delta)
    ]

    ;; décroissance des mèmes éventuelle
    if use-memes? [ decay-memes ]

    ;; update 3D
    update-3d self

    ;; logging
    if (output = "Values" or output = "File") [
      compute-statistics
    ]
  ]

  ;; décroissance du bonus (optionnelle)
  if reward-decay > 0 [
    ask turtles [
      set tx-bonus max (list 0 (tx-bonus - reward-decay))
    ]
  ]

  ;; inversion %
  ifelse (total > 0)
    [ set inversion (100 * change / total) ]
    [ set inversion 0 ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; COLORATION
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to colorer
  ask turtles [
    ifelse meta? [
      set color yellow
    ] [
      ifelse opinion >= 0 [ set color blue ] [ set color red ]
    ]
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; MISE À JOUR DU RÉSEAU (suppression/formation + ponts)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to update-networks
  ;; suppression
  let doomed links with [
    abs([opinion] of end1 - [opinion] of end2) > (link-removal-threshold / 100)
  ]
  let doomedProb doomed with [ random-float 1 < prob ]
  let n-remove min (list linksdown count doomedProb)
  if n-remove > 0 [
    ask n-of n-remove doomedProb [ die ]
    set links-dead links-dead + n-remove
  ]

  ;; formation
  let j linksup
  while [j > 0] [
    let t one-of turtles
    if t = nobody [ stop ]
    ask t [
      let myop opinion
      let candidates other turtles with [ not link-neighbor? myself ]
      let pool-homo candidates with [ abs(opinion - myop) < (link-formation-threshold / 100) ]
      let pool-bridge candidates with [ (sign opinion) != (sign myop) ]

      let friend nobody
      if any? pool-bridge and (random-float 1 < bridge-prob) [
        set friend max-one-of pool-bridge [ abs(opinion - myop) ]
      ]
      if (friend = nobody) and any? pool-homo [
        set friend min-one-of pool-homo [ abs(opinion - myop) ]
      ]

      if friend != nobody and (random-float 1 < prob) [
        create-link-with friend
        set links-create links-create + 1
        let same-sign? (sign opinion) = (sign [opinion] of friend)
        ask link-with friend [
          set color (ifelse-value same-sign? [ green ] [ gray ])
          set thickness linktick
          if show-links? [ show-link ]
        ]
      ]
    ]
    set j j - 1
  ]
end 

to meta
  if not network [ stop ]
  ask turtles [
    let pool other turtles with [
      color = yellow and
      not link-neighbor? myself and
      (count link-neighbors) < meta-links
    ]
    if any? pool [
      let friend one-of pool
      create-link-with friend
      let same-sign? (sign opinion) = (sign [opinion] of friend)
      ask link-with friend [
        set color (ifelse-value same-sign? [ green ] [ gray ])
        set thickness linktick
        if show-links? [ show-link ]
      ]
    ]
  ]
end 

to apply-link-visibility
  ifelse show-links? [
    ask links [ show-link ]
  ] [
    ask links [ hide-link ]
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; STATISTIQUES RUNTIME
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to compute-statistics
  if output = "Values" [
    let pre prevalence
    let mem opinion
    let infl influence
    let ag who
    let ti ticks
    let ess try
    let memed (count turtles with [opinion > 0]) / (pop / 100)
    let maj major
    let mplus meme-plus
    let mminus meme-minus
    
    output-print (word ess " ; " ti " ; "  ag " ; " pre " ; "  mem " ; " infl " ; " memed " ; " mplus " ; " mminus)
  ]
  if output = "File" [
    let pre prevalence
    let mem opinion
    let infl influence
    let ti ticks
    let ag who
        
    output-print (word ti " ; " pre " ; " mem " ; " infl " ; " ag)
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; I/O : LECTURE FICHIER D’AGENTS
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to in_file
  carefully [
    set file-in user-file
    if (file-in != false) [
      set list_data []
      file-open file-in
      while [not file-at-end?] [
        set list_data sentence list_data (list (list file-read file-read file-read file-read))
      ]
      file-close
      user-message "File uploaded!"
      set in_data true
    ]
  ] [
    user-message "File read error"
  ]
  data
end 

to data
  clear-turtles
  clear-links
  let tick_to_load choice_iter

  ifelse (is-list? list_data) [
    let filtered_data filter [ row -> first row = tick_to_load ] list_data

    create-turtles length filtered_data [
      let my_index who
      let agent_data item my_index filtered_data

      set prevalence item 1 agent_data
      set opinion    item 2 agent_data
      set influence  item 3 agent_data
      if influence = 1 [ set meta-agents meta-agents + influence ]
      set opinion-previous opinion
      set influence-previous influence
      set tx-bonus 0

      if opinion < 0 [ set color red  set agent-type "Left side"  ]
      if opinion > 0 [ set color blue set agent-type "Right side" ]
      if influence = 1 [ set color yellow ]

      ;; init mèmes en cohérence
      let tot initial-prevalence-to-memes prevalence
      ifelse opinion >= 0 [
        set meme-plus  tot * (0.5 + 0.5 * abs opinion)
        set meme-minus tot - meme-plus
      ] [
        set meme-minus tot * (0.5 + 0.5 * abs opinion)
        set meme-plus  tot - meme-minus
      ]

      update-3d self
    ]
    ;influenceurs
  ] [
    set in_data false
    user-message "Read error"
  ]

  update-networks
  apply-link-visibility
  recolor-links

  influenceurs
  update-opinions
  set repet_data true
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; ÉVÉNEMENT EXTERNE (protégé par veto/réflexion)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to event

  ask turtles [
     let event-prob random-float 1                     ;; (0,1)
  if event-prob <= event-prob-max [
    ifelse meme_set = true [
      if (to_left = false) [
        if agent-type = "Right side" [
          if opinion < 0 [
            maybe-set-opinion (opinion + event_size)
          ]
        ]
      ]
      if (to_left = true) [
        if agent-type = "Left side" [
          if opinion > 0 [
            maybe-set-opinion (opinion - event_size)
          ]
        ]
      ]
    ] [
      if (to_left = false) [
        if (opinion < high_meme and opinion > low_meme and prevalence < high-prev and prevalence > low-prev) [
          maybe-set-opinion (opinion + event_size)
          if (prev_change != 0) [ set prevalence min (list max-prevalence (prevalence + prev_change)) ]
        ]
      ]
      if (to_left = true) [
        if (opinion > low_meme and opinion < high_meme and prevalence > low-prev and prevalence < high-prev) [
          maybe-set-opinion (opinion - event_size)
          if (prev_change != 0) [ set prevalence min (list max-prevalence (prevalence + prev_change)) ]
        ]
      ]
    ]
    ;; init mèmes cohérente avec (prevalence, opinion)
    if use-memes? [  
    let tot initial-prevalence-to-memes prevalence
    ifelse opinion >= 0 [
      set meme-plus  tot * (0.5 + 0.5 * abs opinion)
      set meme-minus tot - meme-plus
    ] [
      set meme-minus tot * (0.5 + 0.5 * abs opinion)
      set meme-plus  tot - meme-minus
      ]]
  ]
]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; UTILITAIRES
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to set-background-black
  ask patches [ set pcolor black ]
end 

to update-3d [agt]
  ask agt [
    set x3d opinion * 16
    set y3d prevalence / 6
    set z3d influence * 16
    setxyz x3d y3d z3d
  ]
end 

to-report safe-median [agentset varname]
  if not any? agentset [ report 0 ]
  report median [ runresult varname ] of agentset
end 

to-report sign [x]
  ifelse x > 0 [ report 1 ] [ ifelse x < 0 [ report -1 ] [ report 0 ] ]
end 

to recolor-links
  ask links [
    let s1 sign [opinion] of end1
    let s2 sign [opinion] of end2
    ifelse s1 = s2
      [ set color green ]
      [ set color gray ]
    set thickness linktick
  ]
end 

;; IMPACT DE GROUPE (tous les voisins liés)

to-report group-alignment-all [agt sign-ref]
  let nbrs [link-neighbors] of agt
  if not any? nbrs [ report 0.5 ]
  let same count nbrs with [ (sign opinion) = sign-ref ]
  report same / count nbrs
end 

;; IMPACT DE GROUPE (k plus proches)

to-report group-alignment-k [agt sign-ref k]
  let nbrs [link-neighbors] of agt
  let deg count nbrs
  if deg = 0 [ report 0.5 ]
  let kk max list 1 min list deg floor k
  let agop [opinion] of agt
  let pool min-n-of kk nbrs [ abs(opinion - agop) ]
  if not any? pool [ report 0.5 ]
  let same count pool with [ (sign opinion) = sign-ref ]
  report same / count pool
end 

;; Sélecteur de mode

to-report group-alignment-effective [agt sign-ref]
  ifelse (group-impact-mode = "k-nearest")
  [ report group-alignment-k agt sign-ref group-k ]
  [ report group-alignment-all agt sign-ref ]
end 

;; Mapping prevalence -> stock initial de mèmes

to-report initial-prevalence-to-memes [prev]
  report (prev / 99) * meme-max
end 

;; Recalcule opinion & prevalence à partir des mèmes (protégé par veto/réflexion)

to recompute-from-memes
  let tot meme-plus + meme-minus
  if tot < 1e-6 [ set tot 1e-6 ]
  set proposed-opinion ((meme-plus - meme-minus) / tot)
  maybe-set-opinion proposed-opinion
  let scaled  (tot / meme-max) * 99
  if scaled < 0 [ set scaled 0 ]
  if scaled > 99 [ set scaled 99 ]
  set prevalence scaled
end 

;; Décroissance des mèmes

to decay-memes
  if meme-decay <= 0 [ stop ]
  set meme-plus  max list 0 (meme-plus  * (1 - meme-decay))
  set meme-minus max list 0 (meme-minus * (1 - meme-decay))
end 

;; Transmission des mèmes d’un émetteur vers le receveur (self)

to transmit-memes [emitter]
  let sgn sign [opinion] of emitter
  ifelse sgn >= 0 [
    set meme-plus  meme-plus + meme-gain
    set meme-minus max list 0 (meme-minus - meme-anti-leak * meme-gain)
  ] [
    set meme-minus meme-minus + meme-gain
    set meme-plus  max list 0 (meme-plus - meme-anti-leak * meme-gain)
  ]
  ;; plafonner en douceur
  let tot meme-plus + meme-minus
  if tot > meme-max [
    let factor meme-max / tot
    set meme-plus  meme-plus  * factor
    set meme-minus meme-minus * factor
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; EXPORT CSV (par essai)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to csv-begin
  if not csv-export [ stop ]
  set csv-file (word csv-basename "-" try ".csv")
  file-close-all
  if file-exists? csv-file [ file-delete csv-file ]
  file-open csv-file
  set csv-open? true
  file-print "try,iter,tick,left_pct,right_pct,avg_opinion,med_op_right,med_op_left,med_prev_right,med_prev_left,med_infl_right,med_infl_left,links_remove,links_create,inversion_pct,change,total,fractale,major"
end 

to csv-row
  if not csv-open? [ stop ]
  let avg-opinion     mean [opinion] of turtles
  let opR             safe-median (turtles with [opinion >= 0]) "opinion"
  let opL             safe-median (turtles with [opinion < 0])  "opinion"
  let prevR           (safe-median (turtles with [opinion >= 0]) "prevalence") / 100
  let prevL           (safe-median (turtles with [opinion < 0])  "prevalence") / 100
  let inflR           safe-median (turtles with [opinion >= 0]) "influence"
  let inflL           safe-median (turtles with [opinion < 0])  "influence"
  let leftpct         (count turtles with [opinion < 0])  / (pop / 100)
  let rightpct        (count turtles with [opinion >= 0]) / (pop / 100)
  file-print (word try "," iter "," ticks ","
              leftpct "," rightpct "," avg-opinion ","
              opR "," opL "," prevR "," prevL ","
              inflR "," inflL ","
              links-dead "," links-create ","
              inversion "," change "," total "," fractale "," major)
end 

to csv-end
  if csv-open? [
    file-close
    set csv-open? false
  ]
end 

There is only one version of this model, created 3 days ago by Pierre-Alain Cotnoir.

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