Version_20250913-1_Spread of Opinions Influenced by Group Effects

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WHAT IS IT?

This simulator was designed from research by the Public Opinion Research Group.
It models how opinions on political or social issues spread inside a population, using a multi-agent system.

This work has focused on modelling the Quebec electorate facing political issues. Surveys have revealed the close relationship among the electorate between the bias level on these issues and the importance of representations underlying the accession of voters to these issues, i.e. the significance of this adhesion for each voter.

The operation of this multi-agent simulator is based on this relationship. It models the transmission in a population of a bipolar view.

Four factors are used to simulate the rules of meme transmission: prevalence, polarization of opinion, influence, and social links.

  • Prevalence simulates the quality and quantity of mental representations an individual may have about an issue or “meme”. A meme can be more or less prominent or polarised in the mind of its owner.
  • Polarization expresses how strongly opinions are opposed.
  • Influence is the ability of individuals to convince others.
  • Social links simulate the relations of proximity or randomness among individuals who share similar memes or hold opposing opinions.

Agents carry: - an opinion (−1 to +1), - a prevalence (strength/salience), - an influence (capacity to convince), - and a set of social links.

The model studies the co-evolution of: 1. Individual convictions, 2. Salience of issues (prevalence), 3. Influence capacities, 4. Social network structure.


HOW TO USE IT

Basic operation

  1. Choose the population size in pop.
  2. Press Setup to generate agents and their initial links (the background is set to black).
  3. Press Go to run or pause the simulation.

Agents in 3D: - X axis: opinion (–1 = left, +1 = right), - Y axis: prevalence (0–99), - Z axis: influence (0–1).

Links (ties) are coloured: - Green: both agents share the same sign (homophily), - Gray: opposite signs (bridges).

Their thickness is controlled by the slider linktick.
Use the show-links? switch to display or hide ties.


Social network dynamics

Links are created or removed according to opinion distance:

  • link-removal-threshold: maximum opinion gap above which a link can be removed.
  • link-formation-threshold: maximum gap for forming new ties.
  • prob: probability applied to link removal/creation.
  • linksdown: maximum number of links removed per tick.
  • linksup: maximum number of links created per tick.

The bridge-prob slider lets you add a probability of creating bridges between opposite camps (see below).


Loading data

Use in_file to import a text file with space-separated columns:

choice_iter selects which iteration to load, allowing you to replay or branch from a saved configuration.


Meta-influencers

Meta-influencers are agents given a very high ability to convince others (influence = 1 at setup).
You can configure them as follows:

  • meta-influencers-selection: choose their group (All, Left, or Right).
  • meta-influencers: proportion of the population designated as meta-influencers.
  • prev-low / prev-high: restrict eligible agents based on prevalence.
  • meta-links, meta-min, meta-max: control how many extra social links they can create.
  • meta-ok: toggle their activation during the simulation.
  • The Influent button allows you to add new ones during a run.

Influence dynamics when vary-influence is ON

Meta-influencers (and any agent initially set with influence = 1) do not keep a fixed persuasion strength.
Their ability to convince others adapts based on how effective they are:

  • When an agent successfully changes the target’s opinion, its influence value increases (up to 1).
  • When the attempt fails, its influence value decreases (down to 0).

Over time, agents who frequently persuade others become stronger influencers, while those who fail repeatedly lose their power.


Prevalence & influence dynamics

  • modulation-prevalence + rate-modulation: adjust prevalence as opinions shift.
  • rate-infl: speed at which influence increases or decreases after adoption attempts.
  • noise: introduces random opinion drift (external shocks).
  • polarization-factor: lowers adoption probability when opinions are very far apart.

External events

You can perturb the system:

  1. Define opinion bounds (low_meme, high_meme) and prevalence bounds (low-prev, high-prev).
  2. Choose event_size (opinion shift) and prev_change (prevalence shift).
  3. Trigger:
    • Manually with the event button,
    • Automatically by enabling auto_event and setting tick-event.
      > Meme_set can restrict the event to agents classified at startup as left or right.

NEW & ENHANCED FEATURES

  • 3D view: agents plotted by opinion (X), prevalence (Y), and influence (Z).
  • Dynamic links: continuously updated as opinions evolve.
  • Link colouring:
    • Green → same-signed opinions,
    • Gray → opposite signs (including links with meta-influencers).
  • Meta-influencer links managed with limits (meta-minmeta-max).
  • Prevalence modulation and noise integrated into adoption rules.
  • CSV export per trial: logs statistics every tick.
  • Toggle show-links? to display or hide ties.
  • Background set to black at setup.

Group impact parameters

These controls determine how much the alignment of an agent’s neighbours affects adoption.

group-impact-weight

Strength of the group effect (0 = none, 1 = full).

🔢 group-k – Number of Neighbours Considered

Defines how many linked neighbours of the target are examined when calculating the group alignment (only if group-impact-mode = "k-nearest").

  • 1: only the single closest neighbour.
  • 3–5: focus on the most similar contacts.
  • 20+: approximate the behaviour of "all".

If k exceeds the agent’s number of neighbours, all are used.

group-impact-alpha

Controls how the level of consensus influences adoption:

[ f(g) = g^{\alpha} ]

| Symbol | Definition | Role | |--------|------------|------| | g | Fraction of neighbours aligned with the influencer’s sign. | Measures consensus strength. | | α | Non-linearity (group-impact-alpha): 1 = linear, <1 = concave (minorities matter), >1 = convex (requires strong majority). | Shapes the weight of consensus. | | f(g) | Group impact factor applied to the base probability. | |

Combined scaling

[ P' = P \times \big[(1 - w) + w \times (g^{\alpha})\big] ]

| Symbol | Meaning | |--------|---------| | P = base adoption probability (prevalence gap, distance, influence). | | w = group-impact-weight, how much group effect matters. | | g = alignment fraction. | | α = non-linearity parameter. | | P' = final probability after group effect. |

💡 Small α gives power to small aligned minorities; large α means only strong consensus affects adoption.


Additional parameters: prevalence-weight, adoption-floor, bridge-prob

prevalence-weight

Sets how strongly the prevalence gap boosts adoption chances.

  • Low (0–0.3): prevalence barely matters.
  • Medium (0.4–0.6): balanced.
  • High (0.7–1.0): large prevalence advantage can overcome sign difference.

adoption-floor

Guarantees a minimum probability of adoption, preventing deadlocks in highly segregated networks.

bridge-prob

Adds random bridges between agents of opposite signs, bypassing the formation threshold.

| Slider | Role | Effect on links | Effect on inversions | |--------|------|-----------------|----------------------| | prevalence-weight | Strength of prevalence advantage | — | Higher chance of adoption when neighbour’s prevalence is high | | adoption-floor | Minimum adoption probability | — | Keeps change possible across camps | | bridge-prob | Cross-camp link probability | Creates “bridges” | Increases exposure to opposite signs |


USER INTERFACE CONTROLS

General commands

  • Setup, Go
  • in_file, auto_event
  • refresh, cumulative

Population & iterations

pop, nb_try, max_iter, threshold, tick-event

External events

event, On_to_left, meme_set, event_size, prev_change

Meta-influencers

meta-influencers, meta-influencers-selection, meta-links, meta-min, meta-max, prev-low, prev-high, vary-influence, meta-ok

Opinion & prevalence

rate-infl, modulation-prevalence, rate-modulation, noise, polarization-factor

Social network

prob, linksdown, linksup, link-removal-threshold, link-formation-threshold

Group impact

group-impact-weight, group-impact-alpha, group-k, group-impact-mode

Advanced sliders

prevalence-weight, adoption-floor, bridge-prob

Links & display

show-links?, linktick — link thickness
Colours: green (same sign), gray (opposite).

Monitors & graph

Track proportions, medians (opinion, prevalence, influence), inversions, fractal dimension, and link stats.


THINGS TO NOTICE

  • Observe how opinions converge or polarize depending on prevalence, influence, and network structure.
  • Meta-influencers grow or lose power based on persuasion success when vary-influence is enabled.
  • Bridges and adoption-floor help avoid stagnation, while prevalence-weight controls how strongly salience tips adoption.
  • Link colours: green = homophily, gray = cross-camp ties.

NETLOGO FEATURES

  • 3D visualization of agents and links.
  • Export results via file or CSV.

CREDITS AND REFERENCES

  • Original concept: Public Opinion Research Group
  • NetLogo implementation & enhancements: Pierre-Alain Cotnoir (2023–2025)
  • AI-assisted design: GPT-4 & GPT-5
  • Email: 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

  ;; === CSV export ===
  csv-export        ;; bool: activer/désactiver l’export CSV par essai (widget UI: switch)
  csv-basename      ;; string: préfixe fichier CSV (widget UI: input), ex: "run"
  csv-file          ;; nom du fichier CSV de l’essai courant
  csv-open?         ;; bool: fichier CSV ouvert ?

  ;; === Paramètres d’inversion / ponts (peuvent être des sliders UI) ===
  ;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)
]

turtles-own [
  opinion         ;; [-1, 1]
  prevalence      ;; [min-prevalence, max-prevalence]
  agent-type      ;; "Right side" | "Left side"
  influence       ;; [0, 1]
  opinion-previous
  influence-previous
  ;; Coordonnées 3D propres à chaque agent
  x3d y3d z3d
]

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; 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 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 [ set meta-links meta-min ]

  ;; === Defaults CSV si widgets pas encore ajoutés ===
  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 (si widgets absents) ===
  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 ]  ;; 0..1
  if (not is-number? group-impact-alpha) [ set group-impact-alpha 1.0 ]    ;; >=0.1

  ;; === Default show-links? si widget absent ===
  if not is-boolean? show-links? [ set show-links? false ]

  ;; === Defaults inversions/ponts (si pas de sliders) ===
  if (not is-number? prevalence-weight) [ set prevalence-weight 1.5 ]  ;; amplification Δprégnance
  if (not is-number? adoption-floor)    [ set adoption-floor 0.02 ]    ;; plancher pénalité polarisation
  if (not is-number? bridge-prob)       [ set bridge-prob 0.10 ]       ;; probabilité de créer un "pont"

  set-background-black

  create
  rapport
end 

to create
  ;; Créer les agents 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
    update-3d self
  ]

  ;; Créer les agents 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
    update-3d self
  ]

  ;; Création des méta-influenceurs (selon vos réglages UI)
  influenceurs

  reset-ticks

  ;; Initialisation réseau via vos règles (créera des liens si conditions réunies)
  set total 0
  set change 0
  update-networks

  ;; Colorer/afficher les liens dès l’initialisation
  recolor-links
  apply-link-visibility
end 

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

to rapport
  ;; titles for Statistics or Values inside compute-statistics
  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")
  ]

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

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

to go

  ifelse (iter < max_iter) [
    set iter iter + 1
    set meta-create 0  ;; plafond de création de liens autour des méta-influenceurs par tick

    ;; Ouvrir le CSV au premier tick de l’essai
    if (iter = 1 and csv-export and not csv-open?) [ csv-begin ]

    if auto_event = true [
      if (tick-event = iter) [ event ]
    ]
    if meta-ok = true [ meta ]

    update-opinions
    if network = true [ update-networks ]
    recolor-links
    apply-link-visibility   ;; montrer/cacher les liens selon show-links?

    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

    ;; “Fractale” via changement de base sûr
    if (change > 1 and total > 1) [
      set fractale (ln total) / (ln change)
    ]

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

    colorer

    ;; rafraîchir le graphique
    if (refresh = true) [
      if ticks > 200 [ reset-ticks clear-plot ]
    ]

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

    ;; Écrire une ligne CSV par tick
    if csv-export [ csv-row ]

  ] [
    ifelse (try < nb_try) [
      ;; Fin d’essai: fermer le CSV de l’essai courant
      if csv-export [ csv-end ]

      ;; réinitialisation pour l’essai suivant
      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 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
        ;;set meta-agents 0
      ]
    ]
    [
      ;; Fin de toutes les répétitions: fermer CSV si encore ouvert
      if csv-export [ csv-end ]
      sound:play-note "Tubular Bells" 60 64 1
      stop
    ]
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; MISE À JOUR DES OPINIONS (intègre l'effet de groupe + correctifs)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

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

      if dprev > 0 [
        ;; distance sur le signe absolu (favorise des inversions quand Δprégnance est fort)
        let dmem abs(abs(opinion) - abs([opinion] of target))

        ;; base-prob amplifiée par prevalence-weight (PAS de seconde division)
        let base-prob dprev * prevalence-weight

        ;; pénalité de polarisation bornée par adoption-floor
        let pol-penalty max list adoption-floor (1 - polarization-factor * dmem)

        ;; influence du voisin
        let p-adopt base-prob * pol-penalty * [influence] of target

        ;; effet de groupe (voisins du RECEVEUR alignés avec le SIGNE de l'émetteur)
        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)))

        ;; garde-fous
        if p-adopt < 0 [ set p-adopt 0 ]
        if p-adopt > 1 [ set p-adopt 1 ]

        ;; tirage d'adoption
        if random-float 1 < p-adopt [
          let old-opinion opinion
          set opinion [opinion] of target
          set total total + 1

          ;; dynamique d'influence
          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 (changement de signe)
          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
    if random-float 1 < noise [
      set opinion opinion + (random-float 0.4 - 0.2)
      if opinion > 1  [ set opinion 1 ]
      if opinion < -1 [ set opinion -1 ]
    ]

    ;; mise à jour position 3D
    update-3d self

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

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



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

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

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; MISE À JOUR DU RÉSEAU (robuste + ponts + coloration + switch show-links?)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to update-networks
  ;; suppression de liens
  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 de liens
  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
  ;; 1) On n'agit pas si le réseau est gelé
  if not network [ stop ]

  ;; 2) Pour chaque agent, tenter un lien vers un méta
  ask turtles [
    ;; candidats = méta-influenceurs (jaunes) non encore liés à moi,
    ;; et qui n'ont pas dépassé leur plafond individuel de liens (meta-links)
    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

      ;; couleur/épaisseur cohérentes
      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 


;; Applique la visibilité globale des liens selon le switch show-links?

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

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; STATISTIQUES RUNTIMES
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

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
    output-print (word ess " ; " ti " ; "  ag " ; " pre " ; "  mem " ; " infl " ; " memed)
  ]
  if output = "File" [
    let pre prevalence
    let mem opinion
    let infl influence
    let ti ticks
    output-print (word ti " " pre " " mem " " infl)
  ]
end 

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

to in_file  ;; File d'entrée
  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

      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 ]

      ;; Position initiale (2D/3D)
      update-3d self
    ]
  ] [
    set in_data false
    user-message "Read error"
  ]

  ;; créer des liens selon vos règles
  update-networks
  apply-link-visibility
  recolor-links

  influenceurs
  update-opinions
  set repet_data true
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; ÉVÉNEMENT EXTERNE
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to event  ;; moving agents to the right or left side by increasing or decreasing the prevalence
  ask turtles [
    ifelse meme_set = true [
      if (to_left = false) [
        if agent-type = "Right side" [
          if opinion < 0 [
            set opinion opinion + event_size
            if opinion > 1 [ set opinion 1 ]
          ]
        ]
      ]
      if (to_left = true) [
        if agent-type = "Left side" [
          if opinion > 0 [
            set opinion opinion - event_size
            if opinion < -1 [ set opinion -1 ]
          ]
        ]
      ]
    ] [
      if (to_left = false) [
        if (opinion < high_meme and opinion > low_meme and prevalence < high-prev and prevalence > low-prev) [
          set opinion opinion + event_size
          if (prev_change != 0) [ set prevalence prevalence + prev_change ]
          if opinion > 1 [ set opinion 1 ]
        ]
      ]
      if (to_left = true) [
        if (opinion > low_meme and opinion < high_meme and prevalence > low-prev and prevalence < high-prev) [
          set opinion opinion - event_size
          if (prev_change != 0) [ set prevalence prevalence + prev_change ]
          if opinion < -1 [ set opinion -1 ]
        ]
      ]
    ]
  ]
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)
;; Retourne la proportion de voisins liés dont le signe d'opinion = sign-ref.
;; Si aucun voisin lié : retourne 0.5 (neutre).
;; ---------------------------------------------------------------------------

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 en opinion)
;; Choisit les k voisins liés les plus proches en opinion de agt,
;; puis renvoie la même proportion (même signe = sign-ref).
;; Si aucun voisin lié : 0.5 (neutre).
;; ---------------------------------------------------------------------------

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 

;; ---------------------------------------------------------------------------
;; IMPACT DE GROUPE EFFECTIF selon le mode sélectionné ("all" | "k-nearest")
;; ---------------------------------------------------------------------------

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 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; 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
  ;; En-tête standardisé
  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 are 6 versions of this model.

Uploaded by When Description Download
Pierre-Alain Cotnoir about 9 hours ago More explicit version of Info Download this version
Pierre-Alain Cotnoir about 9 hours ago Explanations for group-k slider Download this version
Pierre-Alain Cotnoir about 10 hours ago Adding explanations in Group effect in Info Download this version
Pierre-Alain Cotnoir about 11 hours ago New % scale 1%-50% for silder of meta-influencers creation Download this version
Pierre-Alain Cotnoir about 14 hours ago An other small bug on infile reading Download this version
Pierre-Alain Cotnoir about 15 hours ago Initial upload Download this version

Attached files

File Type Description Last updated
Version_20250913-1_Spread of Opinions Influenced by Group Effects.png preview Preview for 'Version_20250913-1_Spread of Opinions Influenced by Group Effects' about 15 hours ago, by Pierre-Alain Cotnoir Download

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