Child of Version_20260115-2_Simulator v6_EN

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WHAT IS IT?

This simulator models how opinions spread and evolve in a connected population (multi-agent system). Each agent has an opinion (−1 to +1), a prevalence (0–99), an influence (0–1), and social links. The model tracks the co-evolution of opinions, prevalence (depth of internal representations), influence, and the network structure.

3D Representation

X: opinion (−1 left, +1 right)
Y: prevalence (0–99)
Z: influence (0–1) Colors: blue (right), red (left), yellow (meta-influencer). Links: green (same sign), gray (opposite signs).

HOW TO USE

Choose the population size with pop.
Click Setup (creates agents, black background, initializes tick-event to event-init).
Click Go to run/pause.

GENERAL CONTROLS

Setup: initialize agents/network; sets tick-event ← event-init.
Go: start/stop the simulation.
in_file: load agent states from a file (for data).
refresh: resets plots after ~200 ticks.
cumulative: if OFF, resets change/total every tick.
output: None | Statistics | Values | File.

POPULATION & ITERATIONS

pop: number of agents (e.g., 100–5000).
nb_try, max_iter, threshold: repetitions, trial length, majority threshold.
choice_iter: iteration to replay when loading from file.

SOCIAL NETWORK (link dynamics)

link-removal-threshold: opinion distance (in %) above which a link may be cut.
link-formation-threshold: maximum distance to allow a new link.
prob: probability applied to deletions/formations.
linksdown / linksup: caps on links removed/created per tick.
bridge-prob: chance to create bridges across opposing camps.
show-links?: toggle link visibility; linktick: visual thickness.

META-INFLUENCERS

Agents with fixed high influence (influence = 1).

meta-influencers-selection: All / Right side / Left side.
meta-mode / meta-influencers: share of agents promoted to “meta”.
prev-low / prev-high: prevalence eligibility bounds.
meta-min / meta-max / meta-links: min/max and current quota of meta links.
meta-ok: enables dynamic meta linking even if vary-influence = OFF.
vary-influence: if ON, influence increases after successes and decreases after setbacks.
metablock: if ON, metas cannot flip sign (veto on polarity changes).

At initialization (setup) and at the start of each new trial, the model sets meta-links ← meta-min if vary-influence = true or meta-ok = true (as in your code).

OPINION & PREVALENCE DYNAMICS

rate-infl: speed of influence updates after adoption.
noise: probability of additive opinion noise.
polarization-factor: penalizes adoption across large opinion gaps.
prevalence-weight: weight of prevalence differences in adoption.
adoption-floor: minimum adoption probability (avoids strict zero).

Prevalence modulation (renamed in code)

mod-prev (formerly modulation-prevalence): ON to adapt prevalence to current vs previous opinion.
Rate-mod (formerly rate-modulation): adjustment intensity.

GROUP EFFECT

group-impact-mode: all (all linked neighbors) or k-nearest.
group-k: number of neighbors in k-nearest mode.
group-impact-weight: weight of neighborhood alignment in adoption.
group-impact-alpha: non-linearity
- <1: small aligned clusters matter more,
- =1: linear,
- >1: only large aligned majorities matter.

REWARD MECHANISM

A successful emitter (who convinces a neighbor) receives a temporary bonus (tx-bonus) that boosts future persuasion.

reward-step: bonus increment per success.
reward-cap: cap on cumulative bonus.
reward-scope: both / left-only / right-only.
reward-prev-delta: increase in the target’s prevalence after adoption (optional).
reward-decay: bonus decay over ticks.

Meme-Based Representation (Weighted Memes + Targeted Injection)

When use-memes? is ON, opinions and prevalence are computed from internal “meme” stocks.
This version distinguishes meme quantity (how many representations an agent holds) from meme weight (how strongly each representation shapes the opinion).

1) Two levels: Quantity vs Weight

Meme quantity → Prevalence

meme-plus, meme-minus store how many pro/anti memes the agent holds.
Prevalence (0–99) is derived from the total quantity:
meme-plus + meme-minus rescaled to 0–99.

Interpretation: prevalence approximates how “rich” an agent’s internal representation system is (how many arguments/frames are available).

Meme weight → Opinion

meme-plus-w, meme-minus-w store the cumulative weighted strength of pro/anti memes.
Opinion is computed from the weighted balance:

[ opinion = \frac{meme\text{-}plus\text{-}w - meme\text{-}minus\text{-}w}{meme\text{-}plus\text{-}w + meme\text{-}minus\text{-}w} ]

If pro-weights dominate → opinion moves toward +1
If anti-weights dominate → opinion moves toward −1
If balanced → opinion stays near 0

A tiny denominator safeguard is used to avoid division by zero.

2) Weighted transmission during interactions

When an agent is influenced, the receiver gets: - a quantity increment (meme-gain) on the side of the emitter (plus or minus), - and a weight increment proportional to that quantity.

Weight distribution parameters

meme-weight-mean (typical 0.2–3.0): average strength of newly acquired memes.
- Low values → many memes are needed to polarize opinions.
- High values → opinions polarize faster, even with small meme quantities.
meme-weight-sd (typical 0.0–1.0): heterogeneity (memes differ in strength).
- 0.0 → all memes are equally strong.
- Higher values → mixed populations with “weak” and “strong” memes.
meme-weight-min / meme-weight-max: hard bounds preventing unrealistic weights.

3) Meme anti-leak and decay (optional)

meme-anti-leak (0–1): when one side grows, a fraction of the opposite stock is reduced.
High values create “winner-takes-more” dynamics (polarization reinforcement).
meme-decay (0–0.05 typical): forgetting rate applied each tick to quantities and weights.

Meme Injection (Targeted diffusion of new representations)

Beyond “events” that shift opinions directly, the simulator can inject memes into a selected subgroup to simulate the introduction and diffusion of a new narrative, argument, or frame.

1) Targeting bounds (preferred controls)

Agents are eligible for injection if they satisfy:

inject-low_meme ≤ opinion ≤ inject-high_meme
inject-low-prev ≤ prevalence ≤ inject-high-prev

This allows injection into: - moderates only (e.g., −0.2 to +0.2), - one camp only (e.g., +0.2 to +1), - low-prevalence agents only (e.g., 0 to 30).

2) Injection strength and reach

inject-prob-max (0–1): maximum share of eligible agents that actually receive the injected memes.
inject-sign: "plus" or "minus" (which direction the injected memes support).
inject-amount (typical 1–10): how many memes are injected (quantity → raises prevalence).
inject-weight (typical 0.2–5.0): how strong injected memes are (weight → shifts opinion more sharply).

Rule of thumb:
- Increase inject-amount to raise prevalence (more representations).
- Increase inject-weight to raise polarization intensity (stronger conviction shift).

3) Scheduling injection

auto_inject?: if ON, injection occurs when ticks = inject-tick.
inject-tick: the tick at which injection happens.
repeat-inject?: if ON, injection repeats every inject-pace ticks.
inject-pace: the interval between repeated injections.

Example scenarios

One-shot targeted campaign:
auto_inject? = ON, repeat-inject? = OFF, inject-tick = 50, inject-prob-max = 0.2,
inject-low_meme = -0.2, inject-high_meme = 0.2, inject-sign = "plus",
inject-amount = 3, inject-weight = 2.0.
Slow diffusion of a weak but persistent narrative:
repeat-inject? = ON, inject-pace = 25, inject-prob-max = 0.05,
inject-amount = 1, inject-weight = 0.5.
High-impact shock on a small subgroup:
inject-prob-max = 0.02, inject-amount = 2, inject-weight = 5.0,
targeting a narrow opinion band.

Parameter Ranges — Key Sliders (Overview)

The table below summarizes the recommended value ranges for the nine main sliders governing opinion transmission, meme dynamics, and external shocks.
Ranges are indicative and meant to support exploratory, comparative, and pedagogical simulations rather than strict calibration.

| Slider name | Typical range | What it controls | Low values → expected effects | High values → expected effects | |------------|---------------|------------------|-------------------------------|--------------------------------| | prevalence-weight | 0.0 – 2.0 | Weight of prevalence differences in adoption probability | Opinion change weakly tied to representational depth; more random diffusion | Deeply rooted agents dominate transmission; strong inertia | | adoption-floor | 0.0 – 0.10 | Minimum probability of adoption regardless of distance | Near-impossible cross-camp adoption | Persistent low-level noise and occasional inversions | | polarization-factor | 0.0 – 1.0 | Penalty applied to large opinion gaps | Distance barely matters; smooth convergence | Strong ideological barriers; entrenched camps | | group-impact-weight | 0.0 – 1.0 | Strength of group alignment effect | Individual interactions dominate | Local majorities strongly condition adoption | | group-impact-alpha | 0.2 – 3.0 | Non-linearity of group effect | Small minorities exert strong influence | Only large aligned groups matter | | meme-max | 50 – 200 | Maximum stock of memes per agent (prevalence scale) | Shallow belief systems; fast saturation | Deep ideological accumulation; slow dynamics | | meme-gain | 0.5 – 2.0 | Meme increment per successful transmission | Slow learning; weak reinforcement | Rapid ideological buildup | | meme-anti-leak | 0.0 – 0.5 | Cross-erosion of opposite meme stock | Memes accumulate independently | Strong competition; winner-takes-more dynamics | | meme-decay | 0.0 – 0.05 | Forgetting rate of memes per tick | Stable long-term memory | Rapid erosion; volatile belief systems | | event-prob-max | 0.0 – 1.0 | Proportion of agents affected by an event | Micro-perturbations, local shocks | System-wide shocks |

Usage notes

Low–mid ranges are recommended for exploratory runs and sensitivity analysis.
Extreme values are useful to study boundary cases (lock-in, collapse, polarization).
Parameters interact strongly: e.g., high meme-gain combined with high meme-anti-leak accelerates polarization.

This table is intended as a cheat sheet; empirical calibration should rely on systematic parameter sweeps.

Meme Injection — Slider Ranges, Effects, and Interactions

This table documents the nine sliders governing meme injection.
Together, they define when, how often, to whom, and with what strength new memes are introduced into the population, allowing controlled simulations of campaigns, rumors, or polarizing shocks.

Core Injection Parameters

| Slider | Typical range | Role in the model | Low values → effects | High values → effects | |------|---------------|-------------------|----------------------|----------------------| | inject-tick | 1 – max_iter | First tick at which meme injection can occur | Early priming of agents; long-run structural impact | Late shock; short-term perturbation with limited propagation | | inject-pace | 1 – 200 | Interval (ticks) between injections | Continuous or quasi-continuous pressure | Rare, punctuated shocks | | inject-prob-max | 0.0 – 1.0 | Maximum probability that an eligible agent receives a meme at an injection tick | Injection remains marginal or localized | Broad exposure; near-systemic dissemination | | inject-amount | 0 – meme-max | Quantity of meme stock added per injection | Subtle informational nudges | Strong narrative saturation or propaganda burst | | inject-weight | 0.0 – 2.0 (or higher) | Relative impact of injected memes on opinion vs. prevalence | Injected memes mainly increase prevalence (attention/salience) | Injected memes strongly bias opinion formation |

Targeting and Selectivity Parameters

| Slider | Typical range | Role in the model | Low values → effects | High values → effects | |------|---------------|-------------------|----------------------|----------------------| | inject-low_meme | −1.0 – 1.0 | Lower bound of opinion eligible for injection | Broad targeting across ideological spectrum | Injection limited to one side or a narrow ideological niche | | inject-high_meme | −1.0 – 1.0 | Upper bound of opinion eligible for injection | Narrow ideological window | Broad or opposing-side reach (depending on bounds) | | inject-low-prev | 0 – 99 | Minimum prevalence required to receive injected memes | Inclusion of low-salience or weakly engaged agents | Targeting already attentive or mobilized agents | | inject-high-prev | 0 – 99 | Maximum prevalence eligible for injection | Focus on low-to-mid engagement agents | Restriction to highly engaged elites |

Interaction Effects (Key Dynamics)

inject-tick × inject-pace
- Early + short pace → persistent campaign dynamics.
- Late + long pace → isolated shock events.
inject-prob-max × inject-amount
- Low prob + high amount → elite seeding (few agents, strong impact).
- High prob + low amount → mass diffusion (many agents, weak signal).
inject-weight × inject-amount
- High weight + high amount → rapid opinion polarization.
- Low weight + high amount → agenda-setting without strong persuasion.
inject-lowmeme / inject-highmeme × inject-low-prev / inject-high-prev
- Narrow opinion + high prevalence → echo-chamber reinforcement.
- Broad opinion + low prevalence → grassroots diffusion potential.

Conceptual Interpretation

Low values generally model background noise, rumors, or weak informational exposure.
High values approximate organized campaigns, disinformation bursts, or polarizing media events.
Intermediate combinations allow exploration of threshold effects, diffusion delays, and nonlinear amplification.

These sliders are designed to be orthogonal but non-independent: meaningful experiments emerge from their joint configuration, not isolated tuning.

Preset — Campaign / Rumor / Polarizing shock (recommended default profile)

This preset is a ready-to-run parameter profile designed to reproduce a common empirical pattern in opinion dynamics:

1) a stable baseline (moderate homophily, limited cross-camp contact),
2) a rumor/campaign phase (repeated external shocks reaching only part of the population),
3) a polarizing outcome (network segmentation, fewer inversions, stronger within-camp reinforcement).

It is intended as a starting point for university-level experimentation (reproducible and interpretable), not as a calibrated model for a specific case.

A. Core idea

Use repeat-event + event-pace to create a campaign/rumeur that reappears periodically.
Use event-prob-max < 1 so the shock reaches only a subset of eligible agents, generating diffusion rather than an immediate global collapse.
Keep bridge-prob low but non-zero so rare cross-camp bridges exist (possible inversions), while still allowing polarization to emerge.
Activate group impact and a mild reward system to reproduce realistic “social proof” and reinforcement mechanisms.

B. Default values (Preset)

(Values assume your current sliders/switches; ranges remain adjustable by the operator.)

1) Events: “campaign / rumor” mechanics

auto_event: ON
repeat-event: ON
event-init: 50 (first shock after an initial stabilization period)
event-pace: 25 (shock repeats every 25 iterations)
event-prob-max: 0.10 (≈10% of eligible agents receive the shock each cycle)

Bounds & targeting - meme_set: OFF (use bounds rather than structural Left/Right set)
- lowmeme / highmeme: -0.30 / +0.30 (targets the “convertible middle”)
- low-prev / high-prev: 10 / 60 (targets moderate-prevalence agents—reachable but not rigid)

Shock magnitude - event_size: 0.25 (clear movement without saturating ±1 too fast)
- prev_change: +8 (moderate strengthening of prevalence in targeted agents; set 0 if you want “pure opinion shock”)

Interpretation - This reproduces a campaign/rumeur that repeatedly perturbs the middle rather than only extremes, and spreads indirectly through the network.

2) Network: controlled homophily + rare bridges

network: ON
link-formation-threshold: 0.20
link-removal-threshold: 0.40
prob: 0.30
linksup / linksdown: 10 / 10 (balanced churn)
bridge-prob: 0.05 (rare but non-zero cross-camp bridges)
show-links?: optional (OFF for performance; ON for demonstrations)

Expected effect - The network remains mostly homophilous, but with occasional “bridges” allowing limited cross-camp exposure.

3) Opinion adoption: prevalence-driven, polarization-aware

prevalence-weight: 1.40
polarization-factor: 0.60
adoption-floor: 0.03
noise: 0.01 (small background drift)

Expected effect - Adoption is mostly driven by prevalence differences, while strong polarization reduces cross-camp adoption without making it impossible.

4) Group impact: social proof (moderate strength)

group-impact-mode: k-nearest
group-k: 8
group-impact-weight: 0.60
group-impact-alpha: 1.20

Expected effect - Local neighbourhood alignment matters; majorities have slightly more weight than minorities (alpha > 1).

5) Reward: modest reinforcement (avoid runaway)

reward-step: 0.03
reward-cap: 0.30
reward-decay: 0.005
reward-scope: both
reward-prev-delta: 0 (keep prevalence effects attributable to memes/events; set 1–3 if you want “success breeds conviction”)

Expected effect - Successful influencers become moderately more effective over time, but decay prevents permanent dominance.

6) Memes: ON (recommended for this preset)

use-memes?: ON
meme-max: 120
meme-gain: 1.0
meme-anti-leak: 0.20
meme-decay: 0.01

Expected effect - Agents gradually accumulate representations; anti-leak creates a mild competitive relation between pro/anti stocks, supporting polarization over repeated shocks.

7) Meta-influencers: optional, controlled

meta-ok: ON (optional; ON reproduces real-world asymmetric reach)
meta-influencers-selection: All
meta-influencers: 5%
prev-low / prev-high: 20 / 80
meta-min / meta-max: 8 / 20
metablock: ON (prevents sign switching for metas)
vary-influence: OFF (recommended OFF to avoid meta inflation; turn ON only if studying endogenous influencer emergence)

Expected effect - A small set of highly connected agents accelerates diffusion while the metablock prevents metas from oscillating across camps.

C. What you should observe (typical outcomes)

Early phase (before event-init): moderate clustering, limited movement.
During repeated events: a growing asymmetry in meme stocks and prevalence among the targeted “middle”.
Over time: stronger within-camp reinforcement, more gray links disappearing, fewer inversions.
If you increase event-prob-max toward 0.30–0.50: faster and more global shifts.
If you increase bridge-prob toward 0.10–0.20: more cross-camp exposure and more inversions (polarization weakens).

D. Minimal variant (if you want “shock-only”, no campaign)

Set repeat-event = OFF and keep a single event-init (or press event once).
Keep all other values unchanged to compare one-shot shock vs campaign repetition.

Meme Dynamics — Integrated Monitoring Indicators

This simulator includes six dedicated monitors designed to track how memes (internal representations) shape opinion formation, prevalence, and polarization over time.
Each monitor is updated at every tick and provides a complementary analytical perspective on the meme–opinion coupling.

1. Mean Meme Stock

What it measures
The average total number of memes held by agents:

Mean Meme Stock = mean(meme-plus + meme-minus)

Why it matters
This indicator captures the global cognitive density of the population.

Low values indicate weakly structured opinions (few internal representations).
High values reflect ideologically “loaded” agents with many arguments.

Interpretation
- Rising values → accumulation of representations (learning, persuasion, repeated events).
- Falling values → forgetting or erosion (via meme-decay).

Implementation (monitor expression)

2. Meme Polarity Index

What it measures
The net ideological balance of all memes in the system.

It compares the total stock of positive memes to negative memes.

Interpretation
- Values close to +1 → dominance of pro (+) memes.
- Values close to -1 → dominance of contra (−) memes.
- Values near 0 → balanced or plural meme ecology.

Implementation (monitor expression)

3. Opinion–Meme Gap

What it measures
The average absolute difference between: - the agent’s expressed opinion, and
- the opinion implied by its internal meme balance.

Why it matters
This indicator captures latent cognitive inconsistency: agents may express an opinion that is not fully supported by their internal representations.

Interpretation
- Low gap → opinions are well grounded in memes.
- High gap → cognitive tension, instability, or transitional states.

Implementation (monitor expression)

4. Ideologization Index

What it measures
The degree to which strong opinions are backed by high prevalence.

It combines opinion extremity and representational depth.

Interpretation
- High values → polarized and ideologically entrenched population.
- Low values → pragmatic or weakly structured opinion landscape.

Implementation (monitor expression)

5. Right Meme Polarization

What it measures
The internal ideological polarization of meme stocks among agents holding a positive opinion (opinion >= 0).

It compares reinforcing memes (meme-plus) to counter-memes (meme-minus) within the right-leaning subgroup only, independently of the left side.

Interpretation
- Values close to +1 → strong dominance of reinforcing (pro-right) memes.
- Values near 0 → balanced or internally plural meme structure.
- Values below 0 → erosion or counter-meme dominance inside the right camp.

This indicator reveals whether right-side opinions are: - deeply ideologized, - stable but weakly supported, - or vulnerable to counter-narratives.

Implementation (plot and monitor expression)

6. Left Meme Polarization

What it measures
The internal ideological polarization of meme stocks among agents holding a negative opinion (opinion < 0).

It measures the balance between reinforcing and counter-memes inside the left-leaning subgroup, independently from right-side dynamics.

Interpretation
- Values close to -1 → strong dominance of reinforcing (pro-left) memes.
- Values near 0 → plural or contested internal meme structure.
- Values above 0 → counter-meme pressure within the left camp.

This indicator is essential to detect: - asymmetric polarization, - minority radicalization, - or differential resistance to meme diffusion.

Implementation (plot and monitor expression)

7. Mean Meme-derived Opinion

What it measures
The average opinion value reconstructed from meme stocks, independently of the agents’ current explicit opinions.

This measure computes, for each agent, the opinion implied by the balance of its internal memes
(meme-plus vs meme-minus), then averages this value across the whole population.

Interpretation
- Values close to +1 → the meme ecology strongly supports the positive (right/pro) polarity.
- Values close to -1 → dominance of negative (left/contra) meme representations.
- Values near 0 → balanced or internally conflicting meme structures.

Comparing this indicator with the mean explicit opinion reveals latent ideological tensions: - If meme-derived opinion is stronger than explicit opinion → opinions may soon shift.
- If explicit opinion is stronger than meme-derived opinion → opinions are weakly grounded and fragile.

Why it matters
This indicator captures the deep cognitive orientation of the population, beyond surface-level expressed opinions.
It helps distinguish: - superficial opinion alignment, - structurally grounded ideological states, - and phases where internal representations lag behind observable behavior.

Implementation (monitor expression)

8. Meme Saturation (%)

What it measures
The overall saturation level of meme stocks in the population, expressed as a percentage of the maximum possible meme capacity.

It compares the total number of memes currently held by all agents to the theoretical maximum (meme-max × population).

Interpretation
- Values close to 0% → agents hold very few internal representations; opinions are shallow and weakly grounded.
- Intermediate values (30–60%) → active circulation of arguments with moderate cognitive load.
- Values close to 100% → saturated cognitive environment; agents are highly entrenched and resistant to change.

This indicator captures the cognitive density of the system and helps distinguish: - early diffusion phases, - mature ideological ecosystems, - and saturation-driven stabilization or lock-in effects.

Implementation (monitor expression)

(These indicators are computed only when use-memes? is ON.)

EXOGENOUS EVENTS (bounded & probabilistic)

Targeting

meme_set + to_left: if ON, structural targeting by camp (Left side / Right side).
Otherwise, use bounds: low_meme / high_meme (opinion window) and low-prev / high-prev (prevalence window).

Effects on targeted agents

event_size: opinion shift (toward the intended camp).
prev_change: prevalence change (clamped to [0,99]).
event-prob-max (0–1): maximum share of targeted agents that actually receive the shock (each agent draws U(0,1)).

Triggering

event button: one-shot shock (manual).
auto_event + tick-event: scheduled automatic shock at iteration tick-event.

Repeated events (per your code)

event-init: initial offset of the first event (on setup and at each new trial, tick-event ← event-init).
repeat-event (switch): if ON, re-schedules the next event after each occurrence.
event-pace (≥ 1 tick recommended): spacing between repeated events.
Scheduling logic:
- If auto_event = ON and iter = tick-event → run event.
- If repeat-event = ON → tick-event ← tick-event + event-pace.
- Else (OFF) → no automatic re-scheduling (you may adjust tick-event manually).
- If auto_event = OFF → each tick, tick-event ← iter + event-pace (the next time you switch ON, the event fires ≈ event-pace ticks later).

Quick examples

Single calibration shock: auto_event=ON, repeat-event=OFF, event-init=2, event-prob-max=1.0.
Periodic pulses: auto_event=ON, repeat-event=ON, event-init=50, event-pace=50, event-prob-max=0.30.
Diffuse perturbations: repeat-event=ON, event-pace=100, event-prob-max=0.05.

CSV EXPORT (NEW FEATURE)

The simulator supports buffered CSV export, allowing results to be written efficiently to disk at the end of a run or trial.

Two export modes are available:

Statistics mode

Exports aggregate indicators per tick, including:

mean opinion
median opinion (left / right)
median prevalence (left / right)
median influence (left / right)
population shares (left / right)
number of links created and removed
inversion rate
interaction counters
fractal indicators

Values mode

Exports individual agent states at selected iterations. One row is written per agent per exported iteration, including:

opinion
prevalence
influence
meme-plus
meme-minus
meme-plus weight
meme-minus weight

CSV Controls

csv-export
Enables or disables CSV export.
csv-mode
Selects "Statistics" or "Values" export mode.
csv-basename
Base name used for generated CSV files.
csv-values-start
First tick at which agent values are exported (Values mode).
csv-values-step
Interval (in ticks) between value exports.

CSV Buttons

csv open
Initializes the CSV buffer and writes the file header.
csv close
Flushes the buffer and writes the CSV file to disk.

CSV filenames are automatically constructed from the base name, trial number, export mode, and locale.

IMPORTING AGENT STATES (NEW FEATURE)

The simulator supports two distinct formats for importing agent states.

in_file (Simple format)

The in_file button loads a file containing a single snapshot of agents, typically corresponding to iteration 0.

Each line in the file must contain exactly:

tick
prevalence
opinion
influence

This format is intended for:

handcrafted initial populations
externally generated datasets
controlled experimental starting points

When loaded:

agents are created once
meme stocks are initialized from opinion and prevalence
the social network is rebuilt normally

infilevalues (CSV Values re-import)

The infilevalues button loads agents from a CSV file previously exported in Values mode.

Usage:

select the iteration to load using choice_iter
click infilevalues

Behavior:

agents are reconstructed exactly as they were at the selected iteration
opinion, prevalence, influence, meme stocks, and meme weights are restored
agents with influence = 1 are automatically recolored as meta-agents

This enables:

replaying simulations from an intermediate state
branching alternative scenarios
counterfactual experiments
reproducible analysis from archived runs

OUTPUTS AND MONITORS

The interface provides monitors for:

population shares (left / right)
opinion, prevalence, and influence statistics
meme-based indicators
polarization and saturation measures
inversion rates
network dynamics (links created / removed)

Plots display time trajectories of key variables, supporting both exploratory and analytical use of the simulator.

OUTPUTS / MONITORS / CSV

Monitors: % left/right, medians (opinion/prevalence/influence), inversions, interactions, fractal dimension, links created/removed.
Graph: time trajectories of key variables.
CSV: if csv-export = ON, per-trial export with a standard header (basename-try.csv).

STATISTICS EXPORT — VARIABLES GUIDE (Automatically exported when csv-export + statistics is ON)

| Variable | Meaning | Analytical role | |---------|---------|-----------------| | try | Run index | Multi-run / Monte Carlo analysis | | tick | Simulation time | Temporal dynamics | | pop | Population size | Normalization | | interactionsperiter | Total interactions per tick | Social activity level | | change | Opinion changes | Instantaneous volatility | | inversions | Sign changes (+↔−) | Ideological switching | | interactionsperinversion | Interactions per inversion | Cognitive inertia | | linkscreated | New links | Network plasticity | | linksremoved | Deleted links | Network fragmentation | | bridgelinks | Cross-camp links | Ideological permeability | | meanopinion | Average opinion | Global orientation | | medianopinion | Median opinion | Robust central tendency | | meanabsopinion | Mean |opinion| | Degree of radicalization | | majoritypct | Majority share | Dominance vs pluralism | | meanprevalence | Average prevalence | Depth of conviction | | medianprevalence | Median prevalence | Distribution of conviction | | meanmemestock | Avg. meme quantity | Cognitive density | | meanmemederivedopinion | Opinion from memes | Latent ideological orientation | | meanpolarityindex | Meme balance index | System-level polarity | | memesaturationpct | Meme capacity usage (%) | Cognitive saturation | | rightmemepolarization | Meme asymmetry (Right) | Intra-camp radicalization | | leftmemepolarization | Meme asymmetry (Left) | Intra-camp radicalization | | opinionmemegap | Opinion–meme distance | Cognitive dissonance | | ideologizationindex | Mean |meme+ − meme−| | Ideological intensity | | metaagents | Number of meta-agents | Influence concentration | | metalinks | Links of meta-agents | Structural reach |

Notes: • Meme-based indicators are meaningful only when use-memes? = ON • ideologization_index measures ideological asymmetry, not direction • inversions capture true allegiance shifts (sign changes)

• opinionmemegap signals latent instability

THINGS TO WATCH

Polarization, convergence, fragmentation.
Roles of meta-influencers (and metablock), group effect, and reward.
Impact of memes (memory, cross-leak, decay).
How repeated events and event-prob-max shape the global dynamics.

QUICK CHEAT SHEET — TYPICAL VALUES

| Parameter | Useful range | Tendency | | ----------------------- | ------------ | ----------------------------------------------------------- | | prevalence-weight | 0–2 | ↑ makes prevalence gaps dominate adoption | | adoption-floor | 0–0.1 | ↑ allows more “noisy” cross-camp adoptions | | bridge-prob | 0–0.3 | ↑ creates more cross-camp bridges & inversions | | group-impact-weight | 0–1 | ↑ strengthens neighborhood alignment effect | | group-impact-alpha | 0.2–3 | <1 favors small aligned clusters; >1 needs large majorities | | reward-step | 0.01–0.1 | ↑ faster reinforcement of persuasive agents | | reward-decay | 0–0.05 | ↑ bonus fades faster | | meme-anti-leak | 0–0.5 | ↑ growth erodes the opposite stock more | | event-prob-max | 0–1 | ↑ more “massive” shocks per occurrence | | event-pace | ≥1 | ↓ means more frequent events (if repetition ON) | | mod-prev & Rate-mod | — | adapt prevalence to opinion changes |

CREDITS

Original concept: Public Opinion Research Group (GROP.CA)
NetLogo implementation & enhancements: Pierre-Alain Cotnoir (2015–2026)
AI-assisted design: GPT-4 & GPT-5 (2024–2026)
Contact: pacotnoir@gmail.com

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extensions [sound nw]

globals [
  ;; core
  min-prevalence
  max-prevalence
  memes-per-change
  meta-influencers-droit
  meta-influencers-gauche
  inject-tick
  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

  ;; LOCALE
 ;; locale-format          ;; "EN" | "FR"

  ;; CSV (simplifié, sans BOTH)
  ;;csv-export             ;; switch
  ;;csv-basename           ;; string
  ;;csv-mode               ;; "Statistics" | "Values"
  csv-file-stats
  csv-file-values
  csv-buffer             ;; liste de lignes (strings) à écrire en fin de run/try
  csv-open?              ;; buffer initialisé ?
  ;;csv-values-step        ;; pas d’export Values (ticks mod step = 0)
  ;;csv-values-start       ;; tick de départ pour exporter Values

  ;; NEW: import Values CSV
  list_values_data
  values_file_in
  values_sep

  ;; compat: si vous n’avez pas le switch, le code compile quand même
  ;;inject-metas-only
]

turtles-own [
  opinion
  prevalence
  agent-type
  influence
  opinion-previous
  influence-previous
  x3d y3d z3d

  meme-plus
  meme-minus
  meme-plus-w
  meme-minus-w

  old-opinion
  proposed-opinion

  tx-bonus
]

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; LOCALE / FORMAT HELPERS
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to-report col-sep
  ifelse locale-format = "FR" [ report ";" ] [ report "," ]
end 

to-report replace-all [s old new]
  let out s
  while [ position old out != false ] [
    let i position old out
    set out (word (substring out 0 i) new (substring out (i + length old) (length out)))
  ]
  report out
end 

to-report fmt [x]
  ;; formatte nombres selon locale-format (décimale "," en FR)
  if x = nobody [ report "" ]
  if not is-number? x [ report (word x) ]
  let s (word "" x)
  if locale-format = "FR" [
    set s replace-all s "." ","
  ]
  report s
end 

to-report join-cols [lst]
  let sep col-sep
  let out ""
  foreach lst [ val ->
    let s (word "" val)
    set out ifelse-value (out = "")
      [ s ]
      [ (word out sep s) ]
  ]
  report out
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; VALUES CSV IMPORT HELPERS
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to-report split-by [s sep]
  ;; retourne une liste de segments (strings)
  let parts []
  let rest s
  while [ position sep rest != false ] [
    let i position sep rest
    set parts lput (substring rest 0 i) parts
    set rest substring rest (i + length sep) (length rest)
  ]
  set parts lput rest parts
  report parts
end 

to-report to-number-locale [s sep]
  ;; s: string ; sep: ";" (FR) ou "," (EN)
  if s = "" [ report 0 ]
  let x s
  ;; si colonnes séparées par ";" on suppose décimale ","
  if sep = ";" [
    set x replace-all x "," "."
  ]
  set x replace-all x " " ""
  report read-from-string x
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; 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 inject-tick inject-base
  set major 0

  ;; Locale defaults
  if not is-string? locale-format [ set locale-format "FR" ]
  if (locale-format != "FR" and locale-format != "EN") [ set locale-format "FR" ]

  ;; tick-event initialisé par event-init
  if not is-number? event-init [ set event-init 50 ]
  set tick-event event-init

  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 ]

  ;; CSV defaults
  if not is-boolean? csv-export [ set csv-export false ]
  if (not is-string? csv-basename) or (csv-basename = "") [ set csv-basename "run" ]
  if not is-string? csv-mode [ set csv-mode "Statistics" ]
  if (csv-mode != "Statistics" and csv-mode != "Values") [
    set csv-mode "Statistics"
  ]

  if not is-number? csv-values-step  [ set csv-values-step 10 ]
  if csv-values-step < 1 [ set csv-values-step 1 ]

  if not is-number? csv-values-start [ set csv-values-start 0 ]
  if csv-values-start < 0 [ set csv-values-start 0 ]

  set csv-file-stats ""
  set csv-file-values ""
  set csv-buffer []
  set csv-open? false

  ;; import values defaults
  set list_values_data []
  set values_file_in false
  set values_sep ";"

  ;; compat
  if not is-boolean? inject-metas-only [ set inject-metas-only false ]

  ;; defaults group-impact
  if (not is-string? group-impact-mode) [ set group-impact-mode "all" ]
  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 ]

  ;; defaults memes pondérés
  if not is-number? meme-weight-mean [ set meme-weight-mean 1.0 ]
  if not is-number? meme-weight-sd   [ set meme-weight-sd 0.0 ]
  if not is-number? meme-weight-min  [ set meme-weight-min 0.05 ]
  if not is-number? meme-weight-max  [ set meme-weight-max 5.0 ]

  ;; defaults injection
  if not is-boolean? auto_inject?   [ set auto_inject? false ]
  if not is-boolean? repeat-inject? [ set repeat-inject? false ]
  if not is-number? inject-tick     [ set inject-tick 50 ]
  if not is-number? inject-pace     [ set inject-pace 50 ]

  if not is-string? inject-sign     [ set inject-sign "plus" ]
  if not is-number? inject-amount   [ set inject-amount 1 ]
  if not is-number? inject-weight   [ set inject-weight 1.0 ]
  if not is-number? inject-prob-max [ set inject-prob-max 1.0 ]

  if not is-number? inject-low_meme  [ set inject-low_meme -1.0 ]
  if not is-number? inject-high_meme [ set inject-high_meme  1.0 ]
  if not is-number? inject-low-prev  [ set inject-low-prev  0.0 ]
  if not is-number? inject-high-prev [ set inject-high-prev 99.0 ]

  set-background-black

  create
  rapport
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; CREATE
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to create
  create-turtles pop / 2 [
    set agent-type "Right side"
    set opinion random-float 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-memes-from-state
    update-3d self
  ]

  create-turtles pop / 2 [
    set agent-type "Left side"
    set opinion (random-float 1 - 1)
    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-memes-from-state
    update-3d self
  ]

  influenceurs
  reset-ticks

  set total 0
  set change 0
  set Interactions 0
  set %Major 0

  update-networks
  recolor-links
  apply-link-visibility
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; OUTPUT HEADERS
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to rapport
  if output = "Statistics" [
    output-print join-cols (list
      "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" [
    ;; memed RETIRÉ des Values
    output-print join-cols (list
      "Try" "Ticks" "Agents"
      "Prevalence" "Opinion" "Influence"
      "meme plus" "meme minus"
      "meme plus w" "meme minus w"
    )
  ]
end 

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

to influenceurs
  if meta-mode = "Pourcent" [
  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
        ]
      ]
    ]
  ]

  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
        ]
      ]
    ]
  ]

  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
        ]
      ]
    ]
  ]
]
  
  if meta-mode = "Nombre" [
  if meta-influencers-selection = "All" [
    let k meta-influencers
    let candidates turtles with [(prevalence > prev-low and prevalence <= prev-high)]
    if k > 0 [
      ask up-to-n-of k turtles [
          set influence 1
          set color yellow
          set meta-agents meta-agents + 1
        ]
      ]     
    ]
  
       

  if meta-influencers-selection = "Right side" [
    set meta-influencers-droit meta-influencers 
    let candidates turtles with [opinion > 0 and (prevalence > prev-low and prevalence <= prev-high)]
    let k min list meta-influencers-droit count candidates
    if k > 0 [
      ask up-to-n-of k candidates [
      
          set influence 1
          set color yellow
          set meta-agents meta-agents + 1
        ]
      ]
    ]

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

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

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 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; CSV (bufferisé) — sans BOTH
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to-report csv-filename [mode-tag]
  report (word csv-basename "-" try "-" mode-tag "-" locale-format ".csv")
end 

to csv-begin
  if not csv-export [ stop ]

  set csv-buffer []
  set csv-open? true

  if csv-mode = "Statistics" [
    set csv-file-stats csv-filename "stats"
    set csv-buffer lput (join-cols (list
    "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" 
    "bridge_links"
    "inversion_pct" "change" "total" "fractale" "major"
    "interactions_per_iter" "majority_pct" "interactions_per_inversion"
    "meta_links" "meta_agents"
    "mean_prevalence" "median_prevalence" "median_opinion"
    "mean_abs_opinion"
    "mean_meme_stock" "mean_meme_derived_opinion"
    "mean_polarity_index" "meme_saturation_pct"
    "right_meme_polarization" "left_meme_polarization"
    "opinion_meme_gap" "ideologization_index")) csv-buffer
  ]

  if csv-mode = "Values" [
    set csv-file-values csv-filename "values"
    ;; memed RETIRÉ des Values CSV
    set csv-buffer lput (join-cols (list
      "try" "tick" "agent"
      "prevalence" "opinion" "influence"
      "meme_plus" "meme_minus"
      "meme_plus_w" "meme_minus_w"
    )) csv-buffer
  ]
end 

to ensure-csv-ready
  if not csv-export [ stop ]
  if not csv-open? [ csv-begin ]
end 

to csv-row-statistics
  ;; appelé seulement si csv-mode = "Statistics"
  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)
  ;; bridge links = ties connecting opposite opinion signs
let bridge_links count links with [ (sign [opinion] of end1) != (sign [opinion] of end2) ]


  ;; --- UI monitor-aligned extras ---
  let interactions-per-iter ifelse-value (iter > 0) [ total / iter ] [ 0 ]
  let majority-pct %Major
  let interactions-per-inversion ifelse-value (change > 0) [ total / change ] [ 0 ]

  let mean-prevalence ifelse-value (any? turtles) [ mean [prevalence] of turtles ] [ 0 ]
  let median-prevalence safe-median turtles "prevalence"
  let median-opinion safe-median turtles "opinion"
  let mean-abs-opinion ifelse-value (any? turtles) [ mean [abs opinion] of turtles ] [ 0 ]

  ;; meme aggregates (only meaningful when use-memes? = true, but exported regardless)
  let mean-meme-stock ifelse-value (any? turtles) [ mean [meme-plus + meme-minus] of turtles ] [ 0 ]

  let mean-meme-derived-opinion ifelse-value (any? turtles) [
    mean [
      ifelse-value ((meme-plus-w + meme-minus-w) > 0)
        [ (meme-plus-w - meme-minus-w) / (meme-plus-w + meme-minus-w) ]
        [ 0 ]
    ] of turtles
  ] [ 0 ]

  let mean-pol-index mean-polarity-index
  let meme-sat meme-saturation-pct

  ;; Right/Left meme polarization within camps (quantity-based, for comparability with earlier monitors)
  let right-den sum [meme-plus + meme-minus] of turtles with [opinion >= 0]
  let right-num (sum [meme-plus] of turtles with [opinion >= 0]) - (sum [meme-minus] of turtles with [opinion >= 0])
  let right-meme-pol ifelse-value (right-den > 0) [ right-num / right-den ] [ 0 ]

  let left-den sum [meme-plus + meme-minus] of turtles with [opinion < 0]
  let left-num (sum [meme-plus] of turtles with [opinion < 0]) - (sum [meme-minus] of turtles with [opinion < 0])
  let left-meme-pol ifelse-value (left-den > 0) [ left-num / left-den ] [ 0 ]

  ;; Opinion–Meme Gap: absolute mismatch between expressed opinion and meme-derived opinion (weighted)
  let opinion-meme-gap ifelse-value (any? turtles) [
    mean [
      abs (opinion - (ifelse-value ((meme-plus-w + meme-minus-w) > 0)
        [ (meme-plus-w - meme-minus-w) / (meme-plus-w + meme-minus-w) ]
        [ 0 ]))
    ] of turtles
  ] [ 0 ]

  ;; Ideologization Index: extremity × conviction depth proxy (0..1)
  let ideologization-index ifelse-value (any? turtles) [
    mean [abs (meme-plus - meme-minus)] of turtles
  ] [ 0 ]

  set csv-buffer lput (join-cols (list
    fmt try
    fmt iter
    fmt ticks
    fmt leftpct
    fmt rightpct
    fmt avg-opinion
    fmt opR
    fmt opL
    fmt prevR
    fmt prevL
    fmt inflR
    fmt inflL
    fmt links-dead
    fmt links-create
    fmt bridge_links
    fmt inversion
    fmt change
    fmt total
    fmt fractale
    fmt major

    fmt interactions-per-iter
    fmt majority-pct
    fmt interactions-per-inversion

    fmt meta-links
    fmt meta-agents

    fmt mean-prevalence
    fmt median-prevalence
    fmt median-opinion
    fmt mean-abs-opinion

    fmt mean-meme-stock
    fmt mean-meme-derived-opinion
    fmt mean-pol-index
    fmt meme-sat
    fmt right-meme-pol
    fmt left-meme-pol
    fmt opinion-meme-gap
    fmt ideologization-index
  )) csv-buffer
end 

to csv-row-values
  ;; appelé seulement si csv-mode = "Values"
  if ticks < csv-values-start [ stop ]
  if (ticks mod csv-values-step) != 0 [ stop ]

  foreach sort turtles [ t ->
    set csv-buffer lput (join-cols (list
      fmt try
      fmt ticks
      fmt [who] of t
      fmt [prevalence] of t
      fmt [opinion] of t
      fmt [influence] of t
      fmt [meme-plus] of t
      fmt [meme-minus] of t
      fmt [meme-plus-w] of t
      fmt [meme-minus-w] of t
    )) csv-buffer
  ]
end 

to csv-flush
  if empty? csv-buffer [ stop ]
  file-close-all

  if csv-mode = "Statistics" [
    if file-exists? csv-file-stats [ file-delete csv-file-stats ]
    file-open csv-file-stats
  ]
  if csv-mode = "Values" [
    if file-exists? csv-file-values [ file-delete csv-file-values ]
    file-open csv-file-values
  ]

  foreach csv-buffer [ line -> file-print line ]
  file-close
end 

to csv-end
  ;; écriture finale du buffer
  if csv-open? [ csv-flush ]
  file-close-all
  set csv-buffer []
  set csv-open? false
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; GO
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to go
  ifelse (iter < max_iter) [

    if csv-export [ ensure-csv-ready ]

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

    ;; EVENEMENTS (corrigé)
if auto_event [
  if (iter = tick-event) [
    event
    if repeat_event [
      set tick-event (tick-event + event-pace)
    ]
  ]
]


    ;; INJECTION AUTO
    if auto_inject? [ 
      if ticks = inject-tick [
        inject-memes
        if repeat-inject? [ set inject-tick (inject-tick + inject-pace) ]
      ]
    ]

    if meta-ok = true [ meta ]

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

    ;; OUTPUT Statistics
    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 join-cols (list
        fmt try
        fmt ti
        fmt avg-opinion
        fmt positive-opinion
        fmt negative-opinion
        fmt positive-prevalence
        fmt negative-prevalence
        fmt positive-influence
        fmt negative-influence
        fmt Left%
        fmt Right%
        fmt links-dead
        fmt links-create
        fmt inversion
        fmt change
        fmt total
        fmt fractale
      )
    ]

    tick

    ifelse use-memes? [
      if (change > 1 and iter > 1) [ set fractale (ln total / ln change) ]
    ] [
      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
    ]

    ;; CSV buffer write selon mode
    if csv-export [
      if csv-mode = "Statistics" [ csv-row-statistics ]
      if csv-mode = "Values"     [ csv-row-values ]
    ]

  ] [

    ;; fin d'un try
    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 event-init
      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 inject-tick inject-base
      ]
    ] [
      if csv-export [ csv-end ]
      sound:play-note "Tubular Bells" 60 64 1
      stop
    ]
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; UPDATE OPINIONS (mèmes pondérés)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to update-opinions
  ask turtles [
    set opinion-previous opinion
    let target one-of link-neighbors

    if target != nobody [
      let raw-dprev ([prevalence] of target) - prevalence
      if raw-dprev < 1 [ set raw-dprev 0 ]
      let dprev raw-dprev / max-prevalence

      if dprev > 0 [
        let dmem abs(abs(opinion) - abs([opinion] of target))

        let base-prob dprev * prevalence-weight
        let pol-penalty max list adoption-floor (1 - polarization-factor * dmem)
        let p-adopt base-prob * pol-penalty * [influence] of target * (1 + [tx-bonus] of target)

        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)))

        if p-adopt < 0 [ set p-adopt 0 ]
        if p-adopt > 1 [ set p-adopt 1 ]

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

          ifelse use-memes? [
            transmit-memes target
            recompute-from-memes
          ] [
            maybe-set-opinion proposed-opinion
          ]

          if opinion = old-opinion [ stop ]
          set total total + 1

          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))
            ]
          ]

          if reward-prev-delta > 0 [
            set prevalence min (list max-prevalence (prevalence + reward-prev-delta))
          ]

          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 ]
                ]
              ]
            ]
          ]

          if (sign old-opinion) != (sign opinion) [
            set change change + 1
          ]

          if change > 0 [
            set memes-per-change (((sum [meme-plus + meme-minus] of turtles) / change) / pop)
          ]
        ]
      ]
    ]

    ;; modulation prevalence (widget: mode_prev)
    if mode_prev = true [
      if prevalence > abs opinion * 100 [
        set prevalence prevalence - abs(opinion - opinion-previous) * influence * rate-mod
      ]
      if prevalence < abs opinion * 100 [
        set prevalence prevalence + abs(opinion - opinion-previous) * influence * rate-mod
      ]
      if prevalence < min-prevalence [ set prevalence min-prevalence ]
      if prevalence > max-prevalence [ set prevalence max-prevalence ]
    ]

    if random-float 1 < noise [
      let delta (random-float 0.4 - 0.2)
      maybe-set-opinion (opinion + delta)
    ]

    if use-memes? [ decay-memes ]
    update-3d self

    if (output = "Values") [ compute-statistics ]
  ]

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

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

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; VALUES OUTPUT (memed RETIRÉ)
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to compute-statistics
  if output = "Values" [
    output-print join-cols (list
      fmt try
      fmt ticks
      fmt who
      fmt prevalence
      fmt opinion
      fmt influence
      fmt meme-plus
      fmt meme-minus
      fmt meme-plus-w
      fmt meme-minus-w
    )
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; IMPORT SIMPLE (format existant) + WRAPPER
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to in_file
  in_file_simple
end 

to in_file_simple
  carefully [
    set file-in user-file
    if (file-in != false) [
      set list_data []
      file-open file-in
      while [not file-at-end?] [
        ;; format attendu : tick prevalence opinion influence
        set list_data sentence list_data (list (list file-read file-read file-read file-read))
      ]
      file-close
      user-message "File uploaded (simple format)!"
      set in_data true
    ]
  ] [
    user-message "File read error"
  ]
  set choice_iter 0
  data
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; IMPORT VALUES CSV (nouveau) — charge un CSV Values multi-ticks
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to in_file_values
  carefully [
    set values_file_in user-file
    if values_file_in = false [ stop ]

    set list_values_data []
    file-open values_file_in

    if file-at-end? [ user-message "Empty file" stop ]
    let header file-read-line

    ;; détecter séparateur colonnes
    ifelse (position ";" header != false)
      [ set values_sep ";" ]
      [ set values_sep "," ]

    while [not file-at-end?] [
      let line file-read-line
      if line != "" [
        let cols split-by line values_sep
        set list_values_data lput cols list_values_data
      ]
    ]
    file-close

    user-message "File uploaded (values CSV)!"
  ] [
    user-message "Values file read error"
    stop
  ]

  data-values
end 

to data-values
  clear-turtles
  clear-links

  let tick_to_load choice_iter

  ;; colonnes attendues (Values sans memed):
  ;; 0 try, 1 tick, 2 agent, 3 prevalence, 4 opinion, 5 influence,
  ;; 6 meme_plus, 7 meme_minus, 8 meme_plus_w, 9 meme_minus_w
  let rows filter [cols ->
      (length cols >= 10) and
      (to-number-locale (item 1 cols) values_sep = tick_to_load)
    ] list_values_data

  if empty? rows [
    user-message (word "No rows found for tick " tick_to_load)
    stop
  ]

  let n min list pop length rows
  let selected sublist rows 0 n

  set meta-agents 0

  create-turtles n [
    let cols item who selected

    set prevalence to-number-locale (item 3 cols) values_sep
    set opinion    to-number-locale (item 4 cols) values_sep
    set influence  to-number-locale (item 5 cols) values_sep

    set meme-plus    to-number-locale (item 6 cols) values_sep
    set meme-minus   to-number-locale (item 7 cols) values_sep
    set meme-plus-w  to-number-locale (item 8 cols) values_sep
    set meme-minus-w to-number-locale (item 9 cols) values_sep

    set opinion-previous opinion
    set influence-previous influence
    set tx-bonus 0

    ;; bornes prudentes
    if prevalence < 0 [ set prevalence 0 ]
    if prevalence > 99 [ set prevalence 99 ]
    if opinion < -1 [ set opinion -1 ]
    if opinion > 1 [ set opinion 1 ]
    if influence < 0 [ set influence 0 ]
    if influence > 1 [ set influence 1 ]

    ;; type & couleur
    ifelse opinion < 0 [
      set agent-type "Left side"
      set color red
    ] [
      set agent-type "Right side"
      set color blue
    ]

    if influence = 1 [
      set color yellow
      set meta-agents meta-agents + 1
    ]

    update-3d self
  ]

  update-networks
  apply-link-visibility
  recolor-links

  set repet_data true
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; I/O : DATA (simple) — utilisé par in_file_simple
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to data
  clear-turtles
  clear-links
  set meta-agents 0
  let tick_to_load choice_iter

  ifelse (is-list? list_data) [
    let filtered_data filter [ row -> first row = tick_to_load ] list_data
    
    if empty? filtered_data [
  let available remove-duplicates map [row -> first row] 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-memes-from-state
      update-3d self
    ]
  ] [
    set in_data false
    user-message "Read error"
  ]

  update-networks
  apply-link-visibility
  recolor-links

  influenceurs
  update-opinions
  set repet_data true
end 

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

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

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; NETWORK
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to update-networks
  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
  ]

  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 

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 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; EVENT
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to event
  ask turtles [
    let event-prob random-float 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)) ]
          ]
        ]
      ]
      if use-memes? [ init-memes-from-state ]
    ]
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; UTILITAIRES DIVERS
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

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 

;; Meme Saturation (%)

to-report meme-saturation-pct
  if not use-memes? [ report 0 ]
  if not any? turtles [ report 0 ]
  let total-memes sum [meme-plus + meme-minus] of turtles
  let capacity meme-max * count turtles
  if capacity <= 0 [ report 0 ]
  report 100 * total-memes / capacity
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; IMPACT DE GROUPE
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

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 

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 

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 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; MEMES : quantité + poids
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

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

to init-memes-from-state
  let totq initial-prevalence-to-memes prevalence

  ifelse opinion >= 0 [
    set meme-plus  totq * (0.5 + 0.5 * abs opinion)
    set meme-minus totq - meme-plus
  ] [
    set meme-minus totq * (0.5 + 0.5 * abs opinion)
    set meme-plus  totq - meme-minus
  ]

  set meme-plus-w  meme-plus  * meme-weight-mean
  set meme-minus-w meme-minus * meme-weight-mean

  if meme-plus < 0 [ set meme-plus 0 ]
  if meme-minus < 0 [ set meme-minus 0 ]
  if meme-plus-w < 0 [ set meme-plus-w 0 ]
  if meme-minus-w < 0 [ set meme-minus-w 0 ]
end 

to-report draw-meme-weight
  let w meme-weight-mean
  if meme-weight-sd > 0 [
    set w (meme-weight-mean + (random-float (2 * meme-weight-sd) - meme-weight-sd))
  ]
  if w < meme-weight-min [ set w meme-weight-min ]
  if w > meme-weight-max [ set w meme-weight-max ]
  report w
end 

to recompute-from-memes
  let totw meme-plus-w + meme-minus-w
  if totw < 1e-6 [ set totw 1e-6 ]
  set proposed-opinion ((meme-plus-w - meme-minus-w) / totw)
  maybe-set-opinion proposed-opinion

  let totq meme-plus + meme-minus
  let scaled (totq / meme-max) * 99
  if scaled < 0 [ set scaled 0 ]
  if scaled > 99 [ set scaled 99 ]
  set prevalence scaled
end 

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

to transmit-memes [emitter]
  let sgn sign [opinion] of emitter
  let w draw-meme-weight
  let leak (meme-anti-leak * meme-gain)

  ifelse sgn >= 0 [
    set meme-plus   meme-plus + meme-gain
    set meme-plus-w meme-plus-w + (w * meme-gain)

    set meme-minus   max list 0 (meme-minus - leak)
    set meme-minus-w max list 0 (meme-minus-w - (w * leak))
  ] [
    set meme-minus   meme-minus + meme-gain
    set meme-minus-w meme-minus-w + (w * meme-gain)

    set meme-plus   max list 0 (meme-plus - leak)
    set meme-plus-w max list 0 (meme-plus-w - (w * leak))
  ]

  let totq meme-plus + meme-minus
  if totq > meme-max [
    let factor meme-max / totq
    set meme-plus    meme-plus    * factor
    set meme-minus   meme-minus   * factor
    set meme-plus-w  meme-plus-w  * factor
    set meme-minus-w meme-minus-w * factor
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; INJECTION
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to inject-memes
  let base-pool turtles with [
    opinion >= inject-low_meme and opinion <= inject-high_meme and
    prevalence >= inject-low-prev and prevalence <= inject-high-prev
  ]

  let pool base-pool
  if inject-metas-only [
    set pool base-pool with [ color = yellow ]
  ]

  ask pool [
    if random-float 1 <= inject-prob-max [
      let w inject-weight
      if w < meme-weight-min [ set w meme-weight-min ]
      if w > meme-weight-max [ set w meme-weight-max ]

      if inject-amount < 0 [ stop ]

      if inject-sign = "plus" [
        set meme-plus   meme-plus + inject-amount
        set meme-plus-w meme-plus-w + (w * inject-amount)
      ]
      if inject-sign = "minus" [
        set meme-minus   meme-minus + inject-amount
        set meme-minus-w meme-minus-w + (w * inject-amount)
      ]

      let totq meme-plus + meme-minus
      if totq > meme-max [
        let factor meme-max / totq
        set meme-plus    meme-plus    * factor
        set meme-minus   meme-minus   * factor
        set meme-plus-w  meme-plus-w  * factor
        set meme-minus-w meme-minus-w * factor
      ]

      if use-memes? [ recompute-from-memes ]
    ]
  ]
end 

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; Additional reporter
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

to-report mean-polarity-index
  let total-plus  sum [meme-plus] of turtles
  let total-minus sum [meme-minus] of turtles
  ifelse (total-plus + total-minus > 0)
    [ report (total-plus - total-minus) / (total-plus + total-minus) ]
    [ report 0 ]
end

There is only one version of this model, created about 20 hours ago by Pierre-Alain Cotnoir.

Attached files

File	Type	Description	Last updated
Architecture_Opinion_Dynamics_Simulator_EN_FINAL.pdf	word	Architecture of the Simulator	about 20 hours ago, by Pierre-Alain Cotnoir	Download
Sondage 02-2022.csv	data	Survey_model_02-2022	about 20 hours ago, by Pierre-Alain Cotnoir	Download

Parent: Version_20260115-2_Simulator v6_EN

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Child of Version_20260115-2_Simulator v6_EN

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WHAT IS IT?

3D Representation

HOW TO USE

GENERAL CONTROLS

POPULATION & ITERATIONS

SOCIAL NETWORK (link dynamics)

META-INFLUENCERS

OPINION & PREVALENCE DYNAMICS

Prevalence modulation (renamed in code)

GROUP EFFECT

REWARD MECHANISM

Meme-Based Representation (Weighted Memes + Targeted Injection)

1) Two levels: Quantity vs Weight

Meme quantity → Prevalence

Meme weight → Opinion

2) Weighted transmission during interactions

Weight distribution parameters

3) Meme anti-leak and decay (optional)

Meme Injection (Targeted diffusion of new representations)

1) Targeting bounds (preferred controls)

2) Injection strength and reach

3) Scheduling injection

Example scenarios

Parameter Ranges — Key Sliders (Overview)

Usage notes

Meme Injection — Slider Ranges, Effects, and Interactions

Core Injection Parameters

Targeting and Selectivity Parameters

Interaction Effects (Key Dynamics)

Conceptual Interpretation

Preset — Campaign / Rumor / Polarizing shock (recommended default profile)

A. Core idea

B. Default values (Preset)

1) Events: “campaign / rumor” mechanics

2) Network: controlled homophily + rare bridges

3) Opinion adoption: prevalence-driven, polarization-aware

4) Group impact: social proof (moderate strength)

5) Reward: modest reinforcement (avoid runaway)

6) Memes: ON (recommended for this preset)

7) Meta-influencers: optional, controlled

C. What you should observe (typical outcomes)

D. Minimal variant (if you want “shock-only”, no campaign)

Meme Dynamics — Integrated Monitoring Indicators

1. Mean Meme Stock

2. Meme Polarity Index

3. Opinion–Meme Gap

4. Ideologization Index

5. Right Meme Polarization

6. Left Meme Polarization

7. Mean Meme-derived Opinion

8. Meme Saturation (%)

EXOGENOUS EVENTS (bounded & probabilistic)

Repeated events (per your code)

CSV EXPORT (NEW FEATURE)

Statistics mode

Values mode

CSV Controls

CSV Buttons

IMPORTING AGENT STATES (NEW FEATURE)

in_file (Simple format)

infilevalues (CSV Values re-import)

OUTPUTS AND MONITORS

OUTPUTS / MONITORS / CSV

• opinionmemegap signals latent instability

THINGS TO WATCH

QUICK CHEAT SHEET — TYPICAL VALUES

CREDITS

Comments and Questions

Attached files