from __future__ import annotations
import csv
import html
import os
import sys
import tempfile
import traceback
from pathlib import Path
ROOT = Path(__file__).resolve().parent
SRC = ROOT / "src"
if str(SRC) not in sys.path:
sys.path.insert(0, str(SRC))
import gradio as gr
import numpy as np
import plotly.graph_objects as go
try:
import spaces
@spaces.GPU(duration=1)
def _zerogpu_startup_probe():
return "ready"
except ImportError:
def _zerogpu_startup_probe():
return "ready"
from socialdynamics.experiments import aggregate, beta_sweep, run_ensemble, seeds_from, summarise_sweep
from socialdynamics.metrics import NullTest
from socialdynamics.presets import PRESETS
from socialdynamics.simulation import OpinionSimulation, SimulationConfig
from socialdynamics.visualization import (
BLUE,
GREEN,
INK,
INK_2,
MUTED,
OCHRE,
PAPER,
PAPER_2,
PLUM,
VERMILION,
base_layout,
distribution_figure,
evolution_figure,
influence_figure,
metric_figure,
network_figure,
style_axes,
sweep_figure,
)
VERSION = "1.2.0"
PRESET_META = {
"Healthy Discussion": ("01", "Baseline", "Low confirmation bias, no external signal. Social influence alone pulls the population together."),
"Filtered, Not Segregated": ("02", "Filter only", "Two camps and a strong confirmation-bias filter on fixed ties. Polarized, but the camps stay interleaved."),
"Echo Chamber": ("03", "Filter + rewiring", "Same as 02, but agents may drop discordant ties for like-minded ones. The network itself sorts."),
"Misinformation Cascade": ("04", "External signal", "A neutral population on a scale-free graph receives a persistent directional signal."),
"Stubborn Minority": ("05", "Committed agents", "One agent in ten holds +1 permanently while everyone else keeps adapting."),
}
# Thresholds for calling opinion clustering "significant" in the interface.
Z_SIGNIFICANT = 3.0
R_MEANINGFUL = 0.20
# ── Configuration helpers ───────────────────────────────────────────────────
def _direction(label: str) -> float:
return 1.0 if str(label).startswith("Positive") else -1.0
def _config(
n_agents,
network,
initial_opinion,
stubborn_fraction,
stubborn_mode,
misinformation_exposure,
misinformation_strength,
misinformation_direction,
confirmation_bias,
social_influence,
noise,
steps,
seed,
rewiring_rate=0.0,
rewiring_tolerance=0.5,
):
return SimulationConfig(
n_agents=int(n_agents),
network=network,
initial_opinion=initial_opinion,
stubborn_fraction=float(stubborn_fraction),
stubborn_mode=stubborn_mode,
misinformation_exposure=float(misinformation_exposure),
misinformation_strength=float(misinformation_strength),
misinformation_direction=_direction(misinformation_direction),
confirmation_bias=float(confirmation_bias),
social_influence=float(social_influence),
noise=float(noise),
steps=int(steps),
seed=int(seed if seed is not None else 42),
rewiring_rate=float(rewiring_rate),
rewiring_tolerance=float(rewiring_tolerance),
)
def _composition(opinions: np.ndarray) -> tuple[float, float, float]:
negative = float(np.mean(opinions < -0.2))
neutral = float(np.mean(np.abs(opinions) <= 0.2))
positive = max(0.0, 1.0 - negative - neutral)
return negative, neutral, positive
def _delta(value: float, threshold: float = 0.005) -> str:
if value > threshold:
return f"▲ {value:+.3f}"
if value < -threshold:
return f"▼ {value:+.3f}"
return "— ≈ 0.000"
def _spark(values: list[float], lo: float, hi: float, color: str) -> str:
"""Auto-scaled sparkline with a soft area fill and an end marker."""
w, h = 140, 34
vals = [float(v) for v in values] if len(values) > 1 else [float(values[0])] * 2
vmin, vmax = min(vals), max(vals)
if vmax - vmin < 1e-6:
pad = max(abs(vmax) * 0.05, 0.01)
vmin, vmax = vmin - pad, vmax + pad
span = vmax - vmin
n = len(vals)
pts = [(i / (n - 1) * w, h - 5 - (v - vmin) / span * (h - 10)) for i, v in enumerate(vals)]
line = " ".join(f"{x:.1f},{y:.1f}" for x, y in pts)
gid = "sg" + color.lstrip("#")
ex, ey = pts[-1]
return (
f''
f' '
)
# ── HTML fragments ──────────────────────────────────────────────────────────
# key, label, colour, range, format, note (shown under the sparkline)
READOUTS = (
("mean_opinion", "Mean opinion", BLUE, (-1.0, 1.0), "{:+.3f}", "distribution"),
("polarization", "Polarization", VERMILION, (0.0, 1.0), "{:.3f}", "Var(x) · network-blind"),
("bimodality", "Bimodality", OCHRE, (0.0, 1.0), "{:.3f}", "> 0.555 hints at two modes · skew-sensitive"),
("assortativity", "Neighbour similarity r", GREEN, (-1.0, 1.0), "{:+.3f}", "who sits next to whom · 0 = chance"),
("selective_exposure", "Selective exposure", PLUM, (0.0, 1.0), "{:.3f}", "filter set by β · not clustering"),
)
def _cards(sim: OpinionSimulation, step: int, running: bool = True, null: NullTest | None = None) -> str:
first = sim.metric_history[0]
current = sim.metric_history[-1]
exposed = int(np.sum(sim.misinformation_mask))
stubborn = int(np.sum(sim.stubborn_mask))
progress = 100.0 * step / max(1, sim.config.steps)
negative, neutral, positive = _composition(sim.opinions)
state = "Running" if running else "Complete"
segments = "".join(
f'
'
f'
{fmt.format(current[key])}
'
f'
{_delta(current[key] - first[key])} since t=0
'
f'
{html.escape(note)}
'
f'{_spark(series, lo, hi, color)}
'
f'
{stubborn} stubborn
'
f'
{exposed} exposed to signal
'
f'
{stubborn / sim.config.n_agents:.0%} fixed · {exposed / sim.config.n_agents:.0%} exposed{rewired}
{sim.config.n_agents:,} agents · {html.escape(sim.config.network)}{' (adaptive)' if sim.config.rewiring_rate > 0 else ''} · {html.escape(sim.config.initial_opinion)} start · seed {sim.config.seed}
t = {step:03d} / {sim.config.steps:03d}
{''.join(cells)}
Population composition thresholds ±0.2
{segments}
{negative:.0%} {neutral:.0%} {positive:.0%}
"""
def _clustered(r: float, null: NullTest | None) -> bool:
return null is not None and null.z >= Z_SIGNIFICANT and r >= R_MEANINGFUL
def _regime(sim: OpinionSimulation, null: NullTest | None) -> tuple[str, str]:
o = sim.opinions
m = sim.metric_history[-1]
mean = float(m["mean_opinion"])
strong_neg = float(np.mean(o < -0.5))
strong_pos = float(np.mean(o > 0.5))
if m["consensus"] >= 0.8:
return "Consensus", f"The population converges to a shared position near {mean:+.2f} ."
if min(strong_neg, strong_pos) >= 0.2 and m["polarization"] >= 0.25:
split = f"{strong_neg:.0%} of agents hold strongly negative and {strong_pos:.0%} strongly positive views"
if _clustered(m["assortativity"], null):
return "Echo chamber", f"{split}, and the camps are segregated on the network: neighbours agree far more than chance."
return "Polarized, mixed", f"{split}, but the camps remain interleaved on the network: neighbours agree no more than chance."
if abs(mean) >= 0.3:
side = "positive" if mean > 0 else "negative"
return "Directional drift", f"The population leans {side} overall ({mean:+.2f} ) without reaching full agreement."
return "Plural / dispersed", "Opinions stay spread across the scale, with no dominant pole and no shared position."
def _structure_sentence(sim: OpinionSimulation, null: NullTest | None) -> str:
m0, m = sim.metric_history[0], sim.metric_history[-1]
r, r0 = m["assortativity"], m0["assortativity"]
zs = f", z = {null.z:+.1f} against shuffled opinions" if null is not None and null.n_permutations else ""
if _clustered(r, null):
where = "built by the dynamics" if r - r0 > 0.1 else "already present at t = 0"
sd = float(np.std(sim.opinions))
tail = (" Opinion spread is small, so this reflects local smoothing rather than opposed camps."
if sd < 0.2 else "")
return f"Neighbours hold similar opinions (r = {r:+.2f} {zs}), {where}.{tail}"
return f"Who is connected to whom is unrelated to opinion (r = {r:+.2f} {zs}): no network echo chamber."
def _filter_sentence(sim: OpinionSimulation) -> str:
se = sim.metric_history[-1]["selective_exposure"]
if sim.config.confirmation_bias <= 0:
return "Confirmation bias is off, so agents weight all neighbours equally."
return (f"Separately, the confirmation-bias filter removes {se:.0%} of neighbour disagreement from what agents hear; "
"this follows from the β setting and the state, not from network position.")
def _summary(sim: OpinionSimulation, null: NullTest | None) -> str:
first = sim.metric_history[0]
last = sim.metric_history[-1]
dmean = last["mean_opinion"] - first["mean_opinion"]
dpol = last["polarization"] - first["polarization"]
dr = last["assortativity"] - first["assortativity"]
dse = last["selective_exposure"] - first["selective_exposure"]
regime, regime_sentence = _regime(sim, null)
if abs(dmean) <= 0.03:
mean_sentence = "Net directional movement is negligible."
else:
mean_sentence = f"The mean shifts {'positive' if dmean > 0 else 'negative'} by {dmean:+.3f} ."
if dpol > 0.03:
pol_sentence = "Dispersion increases."
elif dpol < -0.03:
pol_sentence = "Dispersion decreases."
else:
pol_sentence = "Dispersion is broadly stable."
c = sim.config
record = (
f"β {c.confirmation_bias:.2f} · α {c.social_influence:.2f} · stubborn {c.stubborn_fraction:.0%} ({html.escape(c.stubborn_mode)}) · "
f"signal {c.misinformation_exposure:.0%} × {c.misinformation_strength:.2f} ({'+' if c.misinformation_direction > 0 else '−'}1) · "
f"rewiring {c.rewiring_rate:.2f} @ τ {c.rewiring_tolerance:.2f} · σ {c.noise:.3f} · T {c.steps} · seed {c.seed}"
)
null_line = ""
if null is not None and null.n_permutations:
null_line = (f"Null model: {null.n_permutations} node-label permutations on the final graph · "
f"shuffled r = {null.null_mean:+.3f} ± {null.null_sd:.3f}")
return f"""
Outcome · t = {len(sim.history) - 1} · single seed
{regime}
{regime_sentence}
{mean_sentence} {pol_sentence} {_structure_sentence(sim, null)} {_filter_sentence(sim)}
{dmean:+.3f} {dpol:+.3f} {dr:+.3f} {dse:+.3f}
Run record {record}
{null_line}{' · ' if null_line else ''}One seed is one sample; use the multi-seed comparison below before drawing conclusions. Diagnostics describe this synthetic run only.
"""
def _waiting(title: str, sub: str) -> str:
return f'{title} {sub}
'
def _error_panel(exc: Exception) -> str:
return f"""
Simulation interrupted
{html.escape(type(exc).__name__)}: {html.escape(str(exc))}
The full traceback was written to the Space logs.
See the error below.
{html.escape(str(r['Network']))} {float(r['mean_opinion_mean']):+.3f} "]
for key, _, color, fmt in COMPARE_METRICS:
v = float(r[f"{key}_mean"])
ci = float(r[f"{key}_ci95"])
pm = f" ± {ci:.3f} " if n_seeds > 1 else ""
cells.append(f"{float(r['assortativity_z_mean']):+.1f} · {sig:.0%} ")
body.append("" + "".join(cells) + " ")
return (
"Network Mean Polarization Bimodality "
"Neighbour r Selective exp. z · runs z≥3 {''.join(body)}
"
f"± is the 95% CI of the mean over {n_seeds} seed{'s' if n_seeds > 1 else ''}. "
"z compares neighbour similarity with 200 opinion shuffles on the same graph; the last figure is the share of seeds with z ≥ 3.
"
)
def compare_networks(n_seeds, *values):
try:
base = _config(*values)
n_seeds = int(n_seeds)
seeds = seeds_from(base.seed, n_seeds)
rows: list[dict[str, float | str]] = []
keys = ("mean_opinion", *(k for k, *_ in COMPARE_METRICS), "assortativity_z")
for network in ("Random", "Small World", "Scale Free"):
cfg = SimulationConfig(**{**base.__dict__, "network": network})
runs = run_ensemble(cfg, seeds)
agg = aggregate(runs, keys)
agg["significant_share"] = float(np.mean([r["assortativity_z"] >= Z_SIGNIFICANT for r in runs]))
rows.append({"Network": network, **agg})
status = f'Complete · seeds {seeds[0]}–{seeds[-1]} · non-topology parameters held fixed
'
return _compare_table(rows, n_seeds), _compare_figure(rows, n_seeds), status
except Exception as exc:
traceback.print_exc()
return _error_panel(exc), gr.skip(), ""
SWEEP_BETAS = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0)
SWEEP_KEYS = ("selective_exposure", "assortativity", "assortativity_z", "polarization", "bimodality")
def _sweep_csv(rows: list[dict[str, float]], base: SimulationConfig) -> str:
fd, path = tempfile.mkstemp(prefix="socialdynamics_filter_vs_chamber_", suffix=".csv")
cols = ["rewiring_rate", "confirmation_bias", "seed", "selective_exposure", "assortativity", "assortativity_initial",
"assortativity_z", "assortativity_p", "neighbour_corr", "polarization", "bimodality", "components", "rewired_ties"]
with os.fdopen(fd, "w", newline="", encoding="utf-8") as fh:
for line in _provenance(base):
fh.write(line + "\n")
fh.write("# confirmation_bias and rewiring_rate columns override the config line\n")
writer = csv.writer(fh)
writer.writerow(cols)
for r in rows:
writer.writerow([f"{r[c]:.6f}" if isinstance(r[c], float) else r[c] for c in cols])
return path
def _sweep_verdict(summary: list[dict[str, float]]) -> str:
fixed = [r for r in summary if r["rewiring_rate"] == 0.0]
if len(fixed) < 2:
return ""
se = np.array([r["selective_exposure_mean"] for r in fixed])
rr = np.array([r["assortativity_mean"] for r in fixed])
corr = float(np.corrcoef(se, rr)[0, 1]) if np.std(se) > 1e-9 and np.std(rr) > 1e-9 else float("nan")
corr_s = "undefined" if np.isnan(corr) else f"{corr:+.2f}"
parts = [f"On fixed ties, across β the two quantities correlate at {corr_s} : "
f"selective exposure spans {se.min():.2f}–{se.max():.2f} while neighbour similarity spans {rr.min():+.2f}–{rr.max():+.2f}."]
rew = [r for r in summary if r["rewiring_rate"] > 0.0]
if rew:
best = max(rew, key=lambda r: r["assortativity_mean"])
parts.append(f"With rewiring, neighbour similarity peaks at {best['assortativity_mean']:+.2f} "
f"(β = {best['confirmation_bias']:.1f}), where selective exposure is {best['selective_exposure_mean']:.2f}.")
return f'{" ".join(parts)}
'
def run_sweep(n_seeds, *values):
try:
base = _config(*values)
# The sweep always uses 100 agents to keep the hosted Space responsive.
base = SimulationConfig(**{**base.__dict__, "n_agents": 100})
rate = base.rewiring_rate if base.rewiring_rate > 0 else 0.10
seeds = seeds_from(base.seed, int(n_seeds))
rows = beta_sweep(base, SWEEP_BETAS, (0.0, rate), seeds, n_permutations=100)
summary = summarise_sweep(rows, SWEEP_KEYS)
status = (f'Complete · {len(rows)} runs · 100 agents · seeds {seeds[0]}–{seeds[-1]} · '
f'rewiring rate {rate:.2f}, τ {base.rewiring_tolerance:.2f}
')
return sweep_figure(summary), _sweep_verdict(summary) + status, gr.DownloadButton(value=_sweep_csv(rows, base), interactive=True)
except Exception as exc:
traceback.print_exc()
return gr.skip(), _error_panel(exc), gr.skip()
def _preset_params(p: dict) -> str:
extra = f" · rewire {p['rewiring_rate']:.2f}" if p.get("rewiring_rate", 0) > 0 else ""
return f"{p['network']} · {p['initial_opinion']} · β {p['confirmation_bias']:.2f} · α {p['social_influence']:.2f}{extra}"
def _banner(name: str | None) -> str:
if name is None:
return 'Custom Your own configuration Adjust parameters, then run.
'
num, tag, _ = PRESET_META[name]
return f'{num} {name} {_preset_params(PRESETS[name])}
'
def preset_values(name: str):
p = PRESETS[name]
return (
p["network"], p["initial_opinion"], p["stubborn_fraction"], p["stubborn_mode"],
p["misinformation_exposure"], p["misinformation_strength"], p["misinformation_direction"],
p["confirmation_bias"], p["social_influence"], p["noise"], p["steps"],
p.get("rewiring_rate", 0.0), p.get("rewiring_tolerance", 0.5), _banner(name),
)
# ── Theme ───────────────────────────────────────────────────────────────────
BG = PAPER
SURFACE = "#111316"
LINE = "rgba(255,255,255,0.10)"
def _theme() -> gr.themes.Base:
theme = gr.themes.Base(
primary_hue="neutral",
neutral_hue="zinc",
radius_size="md",
font=[gr.themes.GoogleFont("Geist"), "ui-sans-serif", "system-ui", "sans-serif"],
font_mono=[gr.themes.GoogleFont("Geist Mono"), "ui-monospace", "monospace"],
)
settings = {
"body_background_fill": BG, "body_text_color": INK, "body_text_color_subdued": MUTED,
"background_fill_primary": BG, "background_fill_secondary": SURFACE,
"border_color_primary": LINE, "border_color_accent": INK, "border_color_accent_subdued": LINE,
"color_accent": INK, "color_accent_soft": "rgba(255,255,255,0.08)",
"link_text_color": INK, "link_text_color_hover": VERMILION,
"block_background_fill": "transparent", "block_border_width": "0px", "block_border_color": "transparent",
"block_shadow": "none", "block_radius": "8px", "container_radius": "8px", "embed_radius": "8px",
"block_label_background_fill": "transparent", "block_label_text_color": MUTED, "block_label_border_width": "0px",
"block_title_text_color": "#D6D3CD", "block_title_text_weight": "400", "block_info_text_color": MUTED,
"panel_background_fill": "transparent", "panel_border_width": "0px", "panel_border_color": "transparent",
"input_background_fill": SURFACE, "input_background_fill_focus": PAPER_2,
"input_border_color": LINE, "input_border_color_focus": "rgba(255,255,255,0.28)", "input_border_width": "1px",
"input_radius": "6px", "input_shadow": "none", "input_shadow_focus": "none",
"slider_color": INK,
"checkbox_label_background_fill": SURFACE, "checkbox_label_background_fill_hover": PAPER_2,
"checkbox_label_background_fill_selected": INK, "checkbox_label_text_color": INK,
"checkbox_label_text_color_selected": BG, "checkbox_label_border_color": LINE,
"checkbox_label_border_color_selected": INK, "checkbox_background_color": SURFACE,
"checkbox_background_color_selected": INK, "checkbox_border_color_selected": INK, "checkbox_label_shadow": "none",
"button_primary_background_fill": INK, "button_primary_background_fill_hover": "#FFFFFF",
"button_primary_text_color": BG, "button_primary_text_color_hover": BG, "button_primary_border_color": INK,
"button_secondary_background_fill": "transparent", "button_secondary_background_fill_hover": "rgba(255,255,255,0.05)",
"button_secondary_text_color": INK, "button_secondary_border_color": "rgba(255,255,255,0.14)",
"button_large_radius": "6px", "button_small_radius": "6px", "button_shadow": "none", "button_shadow_hover": "none",
"shadow_drop": "none", "shadow_drop_lg": "none", "shadow_spread": "0px",
"table_border_color": LINE, "table_even_background_fill": BG, "table_odd_background_fill": SURFACE,
"loader_color": INK,
}
# Apply one variable at a time (and mirror into *_dark) so an unknown name in a
# future Gradio release is skipped instead of breaking start-up. The lab is
# a single dark theme regardless of OS preference.
for key, value in settings.items():
for name in (key, f"{key}_dark"):
try:
theme.set(**{name: value})
except (TypeError, AttributeError, ValueError):
pass
return theme
CSS = r"""
:root{--bg:#0A0B0E;--surface:#121419;--surface-2:#171A20;--text:#ECEAE5;--text-2:#A8A59E;--muted:#7F7C75;--line:rgba(255,255,255,.07);--line-2:rgba(255,255,255,.12);--blue:#6C8EF5;--verm:#F0694A;--amber:#D9A441;--green:#5FB88A;--plum:#B39AE8;--neutral:#5A5D66;--sans:"Geist",ui-sans-serif,system-ui,sans-serif;--mono:"Geist Mono",ui-monospace,SFMono-Regular,Menlo,monospace;
--card-bg:linear-gradient(180deg,rgba(255,255,255,.045),rgba(255,255,255,.018));--card-shadow:inset 0 1px 0 rgba(255,255,255,.06),0 1px 2px rgba(0,0,0,.5),0 24px 48px -28px rgba(0,0,0,.9);--radius:14px}
html,body,gradio-app{background:var(--bg)!important;color-scheme:dark}
body{-webkit-font-smoothing:antialiased;background:radial-gradient(1100px 620px at 12% -8%,rgba(108,142,245,.13),transparent 62%),radial-gradient(900px 520px at 96% -4%,rgba(240,105,74,.09),transparent 60%),radial-gradient(1200px 800px at 50% 120%,rgba(95,184,138,.05),transparent 60%),var(--bg)!important;background-attachment:fixed!important}
gradio-app{background:transparent!important}
.gradio-container{max-width:1560px!important;width:100%!important;margin:0 auto!important;padding:0 32px 32px!important;background:transparent!important;color:var(--text)!important;font-family:var(--sans)!important}
.gradio-container .main,.gradio-container .wrap,.gradio-container .contain{background:transparent!important}
.gradio-container a{color:var(--text);text-decoration:none}.gradio-container a:hover{color:var(--verm)}
footer{display:none!important}
.gradio-container .prose{color:var(--text)!important}
.gradio-container .prose p{margin:0}
.gradio-container .form{background:transparent!important;border:0!important;gap:14px!important;box-shadow:none!important}
.gradio-container .block{background:transparent!important;box-shadow:none!important}
*::-webkit-scrollbar{width:8px;height:8px}*::-webkit-scrollbar-thumb{background:rgba(255,255,255,.1);border-radius:8px}*::-webkit-scrollbar-track{background:transparent}
/* card primitive */
.preset-card,.control-panel,.ro,.comp,.viz-tabs,.findings,.ctable-wrap,.m-col,.compare-plot{background:var(--card-bg)!important;border:1px solid var(--line)!important;border-radius:var(--radius)!important;box-shadow:var(--card-shadow)!important}
/* dropdown lists */
.gradio-container ul.options{background:#16181C!important;border:1px solid var(--line-2)!important;border-radius:10px!important;padding:4px!important;box-shadow:0 18px 40px -12px rgba(0,0,0,.85)!important;z-index:1000!important}
.gradio-container ul.options li{border-radius:6px!important;padding:8px 10px!important;font-size:13px!important;color:var(--text-2)!important;background:transparent!important}
.gradio-container ul.options li:hover,.gradio-container ul.options li.active{background:rgba(255,255,255,.07)!important;color:var(--text)!important}
.gradio-container ul.options li.selected{color:var(--text)!important}
/* top bar + intro */
.topbar{display:flex;align-items:center;gap:24px;height:66px;border-bottom:1px solid var(--line)}
.brand{display:flex;align-items:center;gap:11px;font-size:16.5px;font-weight:600;letter-spacing:-.01em;color:var(--text)}
.brand .mark{width:25px;height:25px;border-radius:50%;background:conic-gradient(from 200deg,#3D6BE0,#5A5D66,#E5532F,#5A5D66,#3D6BE0);box-shadow:0 0 0 3px rgba(255,255,255,.04),0 0 18px rgba(240,105,74,.25)}
.brand .ver{font:400 11px var(--mono);color:var(--muted);padding:3px 7px;border:1px solid var(--line);border-radius:999px}
.topbar nav{display:flex;gap:6px;margin-left:auto;font-size:13px}
.topbar nav a{color:var(--text-2)!important;padding:6px 12px;border-radius:8px}
.topbar nav a:hover{background:rgba(255,255,255,.05);color:var(--text)!important}
.intro{padding:48px 0 34px;max-width:52rem}
.eyebrow{display:inline-flex;align-items:center;gap:10px;font:500 11.5px/1 var(--mono);letter-spacing:.08em;text-transform:uppercase;color:var(--text-2);padding:6px 12px 6px 10px;border:1px solid var(--line);border-radius:999px;background:rgba(255,255,255,.03)}
.eyebrow:before{content:"";width:6px;height:6px;border-radius:50%;background:var(--verm);box-shadow:0 0 10px var(--verm)}
.gradio-container .intro h1{margin:18px 0 0!important;font:500 clamp(34px,4.4vw,56px)/1.04 var(--sans)!important;text-wrap:balance;letter-spacing:-.025em!important;color:var(--text)!important}
.gradio-container .intro h1 em{font-style:normal;background:linear-gradient(90deg,#6C8EF5,#F0694A);-webkit-background-clip:text;background-clip:text;color:transparent}
.gradio-container .intro p{margin-top:16px!important;max-width:40rem;font-size:15px;line-height:1.6;color:var(--text-2);text-wrap:pretty}
/* section heads */
.panel{position:relative;padding:20px!important;gap:16px!important;border:1px solid var(--line)!important;border-radius:18px!important;background:linear-gradient(180deg,rgba(255,255,255,.03),rgba(255,255,255,.008))!important;box-shadow:inset 0 1px 0 rgba(255,255,255,.05),0 30px 60px -36px rgba(0,0,0,.9)!important}
.sec-head{display:flex;align-items:center;gap:14px;margin:0}
.sec-ico{width:34px;height:34px;flex:none;border-radius:10px;background:conic-gradient(from 200deg,#6C8EF5,#B39AE8,#F0694A,#D9A441,#6C8EF5);opacity:.9;box-shadow:inset 0 0 0 9px #14161B,0 0 18px rgba(179,154,232,.25)}
.compare-sec .sec-ico{background:conic-gradient(from 90deg,#6C8EF5,#B39AE8,#D9A441,#6C8EF5)}
.sec-title{font:600 17px/1.2 var(--sans);letter-spacing:-.01em;color:var(--text)}
.sec-sub{margin-top:4px;font-size:12.5px;color:var(--muted);text-wrap:pretty}
/* scenario presets */
.pc-1{--acc:108,142,245}.pc-2{--acc:179,154,232}.pc-3{--acc:217,164,65}.pc-4{--acc:95,184,138}
.preset-row{gap:14px!important;background:transparent!important;border:0!important;align-items:stretch!important}
.preset-card{position:relative;overflow:hidden;padding:18px!important;gap:16px!important;display:flex!important;flex-direction:column!important;transition:border-color .2s ease,box-shadow .2s ease;background:radial-gradient(360px 160px at 0% 0%,rgba(var(--acc),.16),transparent 70%),var(--card-bg)!important;border-color:rgba(var(--acc),.18)!important}
.preset-card:before{content:"";position:absolute;left:18px;right:18px;top:0;height:1px;background:linear-gradient(90deg,transparent,rgba(var(--acc),.85),transparent)}
.preset-card:hover{border-color:rgba(var(--acc),.4)!important;box-shadow:var(--card-shadow),0 0 0 1px rgba(var(--acc),.12),0 18px 40px -20px rgba(var(--acc),.35)!important}
.preset-card>.block:first-child,.preset-card>div:first-child{flex:1 1 auto}
.pc-top{display:flex;align-items:center;gap:10px}
.pc-num{font:500 10.5px var(--mono);color:rgb(var(--acc));width:28px;height:24px;display:inline-flex;align-items:center;justify-content:center;border-radius:7px;background:rgba(var(--acc),.14);border:1px solid rgba(var(--acc),.3)}
.pc-title{font:500 15px/1.3 var(--sans);color:var(--text)}
.pc-copy{margin-top:10px;font-size:12.5px;line-height:1.55;color:var(--text-2);text-wrap:pretty}
.pc-params{margin-top:12px;display:inline-block;padding:6px 9px;border-radius:7px;background:rgba(0,0,0,.25);border:1px solid rgba(var(--acc),.14);font:400 11px/1.5 var(--mono);color:var(--text-2)}
button.preset-btn,.preset-btn button{margin-top:auto!important;width:100%!important;max-width:none!important;background:rgba(255,255,255,.05)!important;border:1px solid var(--line-2)!important;color:var(--text)!important;border-radius:8px!important;font:500 12.5px/1 var(--sans)!important;min-height:34px!important;box-shadow:inset 0 1px 0 rgba(255,255,255,.05)!important}
.preset-card button.preset-btn,.preset-card .preset-btn button{background:rgba(var(--acc),.12)!important;border-color:rgba(var(--acc),.32)!important;color:#fff!important}
button.preset-btn:hover,.preset-btn button:hover,.preset-card button.preset-btn:hover{background:rgb(var(--acc))!important;color:#0A0B0E!important;border-color:rgb(var(--acc))!important}
.active-scn{display:flex;flex-wrap:wrap;align-items:center;gap:6px 12px;padding:14px 16px;border-radius:10px;background:rgba(0,0,0,.22);border:1px solid var(--line);font-size:13px;color:var(--text-2)}
.active-scn .tag{font:500 10.5px var(--mono);color:var(--muted);padding:3px 8px;border:1px solid var(--line);border-radius:999px}
.active-scn .tag.on{color:var(--bg);background:var(--text);border-color:var(--text)}
.active-scn b{font-weight:500;color:var(--text)}
.active-scn .hint{font:400 11.5px var(--mono);color:var(--muted)}
/* layout */
.lab-row{gap:24px!important;margin-top:24px!important;align-items:flex-start!important}
.control-panel{position:sticky!important;top:16px;align-self:flex-start!important;overflow:visible!important;padding:18px 18px 16px!important;gap:6px!important;flex-wrap:nowrap!important}
.ctl-head{padding-bottom:8px}
.ctl-head strong{display:block;font:500 15px/1.2 var(--sans);color:var(--text)}
.ctl-head p{margin-top:4px!important;font-size:12px;color:var(--muted)}
.pg-pop{--acc:108,142,245}.pg-dyn{--acc:179,154,232}.pg-sig{--acc:217,164,65}.pg-run{--acc:95,184,138}
.param-group{position:relative;overflow:hidden;border:1px solid rgba(var(--acc),.18)!important;border-radius:12px!important;background:linear-gradient(180deg,rgba(var(--acc),.07),rgba(var(--acc),.015) 60%)!important;padding:4px 12px 10px!important;margin:0 0 10px!important;box-shadow:inset 0 1px 0 rgba(255,255,255,.04)!important}
.param-group:before{content:"";position:absolute;left:12px;right:12px;top:0;height:1px;background:linear-gradient(90deg,transparent,rgba(var(--acc),.7),transparent)}
.param-group>div,.param-group .form,.param-group .block{background:transparent!important;border:0!important}
.param-group>button,.param-group>.label-wrap,.param-group button.label-wrap{display:flex!important;align-items:center!important;gap:10px!important;width:100%!important;font:500 13px/1.2 var(--sans)!important;letter-spacing:0;color:var(--text)!important;background:transparent!important;border:0!important;padding:10px 0 6px!important}
.param-group>button:before,.param-group>.label-wrap:before,.param-group button.label-wrap:before{content:"";width:8px;height:8px;border-radius:50%;flex:none;background:rgb(var(--acc));box-shadow:0 0 10px rgba(var(--acc),.8)}
.param-group .label-wrap .icon,.param-group>button span.icon{color:rgb(var(--acc))!important;margin-left:auto}
.param-group input[type="range"]{--slider-color:rgb(var(--acc))!important;accent-color:rgb(var(--acc))}
.param-group input[type="range"]::-webkit-slider-thumb{background:rgb(var(--acc))!important;box-shadow:0 0 0 4px rgba(var(--acc),.18),0 2px 6px rgba(0,0,0,.6)!important}
.param-group input[type="range"]::-moz-range-thumb{background:rgb(var(--acc))!important}
.param-group input[type="range"]::-moz-range-progress{background:rgb(var(--acc))!important}
.param-group label:has(>input[type="radio"]:checked),.param-group label.selected:has(>input[type="radio"]){background:rgba(var(--acc),.22)!important;color:#fff!important}
.param-group input[type="number"]:focus,.param-group input[type="text"]:focus{border-color:rgba(var(--acc),.6)!important}
.control-panel [data-testid="block-info"],.control-panel label>span{font-size:12.5px!important;color:#D8D5CF!important}
.control-panel [data-testid="block-info"]+div,.control-panel .info{color:var(--muted)!important}
.control-panel input[type="number"]{font:400 12px var(--mono)!important;background:rgba(255,255,255,.04)!important;border:1px solid var(--line)!important;border-radius:6px!important;color:var(--text)!important}
.control-panel select,.control-panel input[type="text"],.control-panel .secondary-wrap{background:rgba(255,255,255,.04)!important;border-radius:8px!important}
.control-panel button.reset-button{color:var(--muted)!important;background:transparent!important;border:0!important}
/* sliders */
.control-panel input[type="range"]{--slider-color:#ECEAE5;--neutral-200:rgba(255,255,255,.1);--neutral-700:rgba(255,255,255,.1);-webkit-appearance:none;appearance:none;height:4px!important;border-radius:4px!important;accent-color:#ECEAE5;outline:none;cursor:pointer}
.control-panel input[type="range"]::-webkit-slider-thumb{-webkit-appearance:none;width:14px;height:14px;border-radius:50%;background:#ECEAE5;border:0;box-shadow:0 0 0 4px rgba(236,234,229,.12),0 2px 6px rgba(0,0,0,.6);cursor:grab}
.control-panel input[type="range"]::-moz-range-thumb{width:14px;height:14px;border-radius:50%;background:#ECEAE5;border:0;box-shadow:0 0 0 4px rgba(236,234,229,.12),0 2px 6px rgba(0,0,0,.6)}
.control-panel input[type="range"]::-moz-range-track{height:4px;border-radius:4px;background:rgba(255,255,255,.1)}
.control-panel input[type="range"]::-moz-range-progress{height:4px;border-radius:4px;background:#ECEAE5}
.control-panel .min_value,.control-panel .max_value{font:400 10.5px var(--mono)!important;color:var(--muted)!important}
/* radios as segmented control */
.control-panel .wrap:has(>label>input[type="radio"]){display:flex!important;flex-wrap:nowrap!important;gap:3px!important;padding:3px!important;border-radius:9px!important;background:rgba(0,0,0,.25)!important;border:1px solid var(--line)!important}
.control-panel label:has(>input[type="radio"]){flex:1 1 0;justify-content:center!important;min-height:30px!important;padding:0 8px!important;border:0!important;border-radius:6px!important;background:transparent!important;box-shadow:none!important;color:var(--text-2)!important;font-size:12.5px!important;cursor:pointer}
.control-panel label:has(>input[type="radio"]:checked),.control-panel label.selected:has(>input[type="radio"]){background:rgba(255,255,255,.1)!important;color:var(--text)!important;box-shadow:inset 0 1px 0 rgba(255,255,255,.08),0 1px 3px rgba(0,0,0,.4)!important}
.control-panel label>input[type="radio"]{display:none!important}
button.run-button,.run-button button{background:linear-gradient(180deg,#FFFFFF,#DDDAD3)!important;color:#0A0B0E!important;border:0!important;border-radius:9px!important;font:600 13.5px/1 var(--sans)!important;min-height:44px!important;box-shadow:inset 0 1px 0 #fff,0 8px 20px -8px rgba(255,255,255,.25)!important;margin-top:14px!important;flex:none!important}
button.run-button:hover,.run-button button:hover{filter:brightness(1.05)}
.ctl-foot{font:400 11px/1.5 var(--mono);color:var(--muted);padding-top:8px;text-align:center}
/* status + readouts */
.status{display:flex;align-items:center;flex-wrap:wrap;gap:8px 18px;font:400 12px/1.3 var(--mono);color:var(--text-2);padding:2px 2px 12px}
.status-state{display:flex;align-items:center;gap:8px;color:var(--text);padding:4px 10px;border-radius:999px;background:rgba(255,255,255,.05);border:1px solid var(--line)}
.status-meta{color:var(--muted)}
.status-step{margin-left:auto;font-variant-numeric:tabular-nums}
.status.err{color:var(--verm)}
.dot{display:inline-block;width:6px;height:6px;border-radius:50%;flex:none}
.dot.live{background:var(--amber);box-shadow:0 0 8px var(--amber);animation:sdpulse 1.2s ease-in-out infinite}
.dot.done{background:var(--green);box-shadow:0 0 8px rgba(95,184,138,.7)}
.dot.err{background:var(--verm)}
@keyframes sdpulse{50%{opacity:.25}}
.progress{height:2px;border-radius:2px;background:var(--line);margin-bottom:16px;overflow:hidden}
.progress-fill{height:100%;background:linear-gradient(90deg,#6C8EF5,#ECEAE5 50%,#F0694A);transition:width .4s ease}
.readouts{display:grid;grid-template-columns:repeat(5,minmax(0,1fr));gap:12px}
.ro{padding:16px 16px 12px;min-width:0;display:flex;flex-direction:column}
.ro-label{font-size:12px;color:var(--text-2);white-space:nowrap;overflow:hidden;text-overflow:ellipsis}
.ro-label i{display:none}
.ro-val{margin:10px 0 6px;font:400 26px/1 var(--mono);letter-spacing:-.03em;color:var(--text);font-variant-numeric:tabular-nums}
.ro-val small{font:400 12px var(--sans);color:var(--muted);letter-spacing:0}
.ro-delta{font:400 11px/1.3 var(--mono);color:var(--text-2);white-space:nowrap;overflow:hidden;text-overflow:ellipsis}
.ro-delta span{color:var(--muted)}
.ro-note{margin-top:auto;padding-top:12px;font:400 11px/1.3 var(--mono);color:var(--muted)}
.spark{display:block;width:100%;height:34px;margin-top:auto;padding-top:10px;box-sizing:content-box}
.comp{margin-top:12px;padding:16px 18px!important}
.comp-head{display:flex;justify-content:space-between;font-size:12px;color:var(--text-2);margin-bottom:12px}
.comp-note{font:400 11px var(--mono);color:var(--muted)}
.comp-track{display:flex;gap:3px;height:10px;padding:2px;border-radius:999px;background:rgba(0,0,0,.35);box-shadow:inset 0 1px 2px rgba(0,0,0,.6)}
.comp-track span{height:100%;border-radius:999px;min-width:6px;transition:flex-grow .4s ease;box-shadow:inset 0 1px 0 rgba(255,255,255,.25)}
.comp-legend{display:flex;flex-wrap:wrap;gap:6px 22px;margin-top:12px;font-size:12px;color:var(--muted)}
.comp-legend span{display:flex;align-items:center;gap:7px}
.comp-legend i{width:8px;height:8px;border-radius:50%}
.comp-legend b{font:400 12px var(--mono);color:var(--text)}
.legend{display:flex;flex-wrap:wrap;gap:6px 18px;padding:18px 2px 12px;font-size:12px;color:var(--text-2)}
.legend span{display:flex;align-items:center;gap:7px}
.lg-dot{width:8px;height:8px;border-radius:50%}
.lg-dia{width:7px;height:7px;transform:rotate(45deg);background:var(--neutral);border:1px solid var(--text)}
.lg-ring{width:9px;height:9px;border-radius:50%;border:1.5px solid var(--amber);background:var(--neutral);box-sizing:border-box}
/* chart tabs */
.viz-tabs{padding:10px!important;gap:8px!important}
.viz-tabs .tab-wrapper{display:flex!important;width:100%!important;padding:4px!important;border:0!important;border-radius:10px!important;background:rgba(0,0,0,.28)!important;box-shadow:inset 0 1px 2px rgba(0,0,0,.5)!important;height:auto!important;margin:0 0 6px!important}
.viz-tabs .tab-container,.viz-tabs [role="tablist"]{display:flex!important;flex:1 1 auto!important;width:100%!important;gap:3px!important;padding:0!important;border:0!important;height:auto!important;overflow:visible!important;background:transparent!important}
.viz-tabs .tab-container::after,.viz-tabs [role="tablist"]::after{display:none!important}
.viz-tabs button[role="tab"]{flex:1 1 auto!important;justify-content:center!important;font:500 12.5px/1 var(--sans)!important;color:var(--muted)!important;border-radius:7px!important;padding:9px 14px!important;background:transparent!important;border:0!important;height:auto!important;white-space:nowrap}
.viz-tabs button[role="tab"]::after{display:none!important}
.viz-tabs button[role="tab"]:hover{color:var(--text)!important}
.viz-tabs button[role="tab"][aria-selected="true"],.viz-tabs button[role="tab"].selected{color:var(--text)!important;background:rgba(255,255,255,.09)!important;box-shadow:inset 0 1px 0 rgba(255,255,255,.08),0 1px 3px rgba(0,0,0,.5)!important}
.viz-tabs .overflow-menu{display:none!important}
.viz-tabs .js-plotly-plot,.viz-tabs .plot-container,.viz-tabs .svg-container{width:100%!important;border-radius:10px}
.viz-tabs .gradio-plot,.viz-tabs [data-testid="plot"]{width:100%!important}
/* findings */
.waiting{display:flex;flex-wrap:wrap;align-items:center;gap:10px 14px;padding:22px 4px;font:400 12px/1.4 var(--mono);color:var(--muted)}
.waiting b{font:500 15px/1.2 var(--sans);color:var(--text)}
.findings{display:grid;grid-template-columns:minmax(0,1fr) minmax(0,1.3fr);gap:24px 40px;padding:22px 24px!important;margin-top:16px}
.kicker{font-size:12px;color:var(--muted)}
.kicker.err{margin-bottom:10px;color:var(--verm)}
.f-regime{margin-top:8px;font:500 24px/1.15 var(--sans);letter-spacing:-.02em;color:var(--text)}
.gradio-container .f-lead{margin-top:8px!important;font-size:14px;line-height:1.6;color:var(--text-2);text-wrap:pretty}
.gradio-container .f-copy{font-size:14px;line-height:1.6;color:var(--text-2);text-wrap:pretty}
.f-lead b,.f-copy b{color:var(--text);font-weight:500}
.f-deltas{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:6px;margin-top:14px}
.f-deltas span{display:flex;align-items:center;gap:8px;padding:8px 10px;border:1px solid var(--line);border-radius:8px;background:rgba(0,0,0,.2);font:400 11.5px/1 var(--mono);color:var(--muted)}
.f-deltas i{display:inline-block;width:6px;height:6px;border-radius:50%}
.f-deltas b{margin-left:auto;color:var(--text);font-weight:400}
.f-record{margin-top:10px;padding:10px 12px;border-radius:8px;background:rgba(0,0,0,.25);border:1px solid var(--line);font:400 11.5px/1.6 var(--mono);color:var(--text-2)}
.f-record span{display:block;font:400 11px var(--sans);color:var(--muted);margin-bottom:2px}
.f-note{margin-top:10px;font-size:11.5px;line-height:1.5;color:var(--muted)}
button.dl-btn,.dl-btn button{align-self:flex-start;margin-top:12px!important;background:rgba(255,255,255,.05)!important;border:1px solid var(--line-2)!important;border-radius:8px!important;color:var(--text)!important;font:500 12.5px/1 var(--sans)!important;box-shadow:inset 0 1px 0 rgba(255,255,255,.05)!important;min-height:36px!important;max-width:200px}
button.dl-btn:hover:not(:disabled),.dl-btn button:hover:not(:disabled){background:rgba(255,255,255,.1)!important}
button.dl-btn:disabled{opacity:.35}
.error-card{border:1px solid rgba(240,105,74,.4);border-radius:12px;background:rgba(240,105,74,.06);padding:16px 18px;margin-top:18px}
.error-card code{font:400 12.5px/1.5 var(--mono);color:var(--text)}
/* compare */
.compare-sec{margin-top:40px!important}
.cmp-head{align-items:center!important;gap:16px!important;flex-wrap:wrap!important}
.cmp-head>div:first-child{flex:1 1 auto!important}
.cmp-head .compare-button,.cmp-head button.compare-button{flex:none!important;width:auto!important;min-width:180px!important}
button.compare-button,.compare-button button{align-self:center;background:linear-gradient(180deg,#FFFFFF,#DDDAD3)!important;color:#0A0B0E!important;border:0!important;border-radius:9px!important;font:600 13px/1 var(--sans)!important;min-height:40px!important;max-width:220px;box-shadow:inset 0 1px 0 #fff,0 8px 20px -8px rgba(255,255,255,.25)!important}
button.compare-button:hover,.compare-button button:hover{filter:brightness(1.05)}
.compare-ok{font:400 11.5px/1.4 var(--mono);color:var(--green)}
.ctable-wrap{padding:6px 20px 14px;background:radial-gradient(500px 220px at 0% 0%,rgba(108,142,245,.10),transparent 70%),var(--card-bg)!important}
.compare-plot{padding:6px!important;min-height:200px;background:radial-gradient(500px 220px at 100% 0%,rgba(179,154,232,.10),transparent 70%),var(--card-bg)!important}
.compare-plot .empty,.compare-plot [data-testid="empty"]{opacity:.35}
.ctable{width:100%;border-collapse:collapse;font-size:13px;color:var(--text-2)}
.ctable th{font-weight:400;color:var(--muted);text-align:right;padding:12px 10px;border-bottom:1px solid var(--line)}
.ctable th:first-child,.ctable td:first-child{text-align:left;padding-left:0}
.ctable td{padding:14px 10px;border-bottom:1px solid rgba(255,255,255,.05)}
.ctable tr:last-child td{border-bottom:0}
.ctable td.net{color:var(--text);font-weight:500}
.ctable td.num{text-align:right;font-family:var(--mono);font-variant-numeric:tabular-nums;white-space:nowrap;color:var(--muted)}
.ctable td.top{color:var(--text)}
.cbar{display:inline-block;height:5px;border-radius:3px;vertical-align:middle;margin-right:10px}
.empty-note{display:flex;flex-direction:column;gap:6px;padding:22px 4px;font-size:12.5px;color:var(--muted)}
.empty-note b{font:500 14px var(--sans);color:var(--text)}
.en-nets{display:flex;flex-wrap:wrap;gap:6px;margin-bottom:8px}
.en-nets span{display:inline-flex;align-items:center;gap:7px;padding:5px 10px;border-radius:999px;font:500 11px var(--mono);color:rgb(var(--c));background:rgba(var(--c),.1);border:1px solid rgba(var(--c),.25)}
.en-nets span:before{content:"";width:5px;height:5px;border-radius:50%;background:rgb(var(--c))}
/* methods */
.methods{display:grid;grid-template-columns:repeat(4,minmax(0,1fr));gap:14px;margin-top:48px}
.m-col:nth-child(1){--acc:108,142,245}.m-col:nth-child(2){--acc:95,184,138}.m-col:nth-child(3){--acc:179,154,232}.m-col:nth-child(4){--acc:217,164,65}
.m-col{position:relative;overflow:hidden;min-width:0;padding:20px;background:radial-gradient(420px 180px at 0% 0%,rgba(var(--acc),.14),transparent 70%),var(--card-bg)!important;border-color:rgba(var(--acc),.16)!important}
.m-col:before{content:"";position:absolute;left:20px;right:20px;top:0;height:1px;background:linear-gradient(90deg,transparent,rgba(var(--acc),.8),transparent)}
.m-num{display:inline-flex;align-items:center;gap:7px;font:500 10.5px var(--mono);color:rgb(var(--acc));padding:4px 9px;border-radius:999px;background:rgba(var(--acc),.12);border:1px solid rgba(var(--acc),.25)}
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"""
HEAD = """
A laboratory for collective opinion
How opinions move through a network.
Set up a synthetic population, choose who talks to whom, add confirmation bias, committed minorities, a persistent external signal or adaptive ties, then watch local rules produce consensus, drift or division. The lab reports what agents hear (the filter you set) separately from who sits next to whom (the structure the dynamics build).
"""
def _preset_card_html(name: str) -> str:
num, tag, copy = PRESET_META[name]
return (
f'{num} {name}
'
f'{copy}
'
f'{_preset_params(PRESETS[name])}
'
)
METHODS = """
Model
Distance-weighted influence
Each agent holds a bounded opinion in [−1, +1]. Neighbour influence decays with opinion distance:
wij = exp(− β |xi − xj |), β = 6 × slider
Updates are synchronous. A smooth model inspired by DeGroot and bounded-confidence traditions, not a verbatim implementation of either.
Adaptive ties
Homophilic rewiring
After each opinion update, every adaptive agent with probability ρ inspects one random neighbour. If they differ by more than τ, the tie is dropped and replaced by a tie to a random non-neighbour within τ (after Holme & Newman, 2006). Edge count is conserved. With ρ = 0 the graph is fixed and results match v1.1 exactly.
Distribution
Network-blind readouts
Polarization Var(x) ∈ [0, 1]. Consensus = 1 − √Var is its monotone transform and is exported but not shown twice.
Bimodality Sarle's coefficient; values above 5/9 ≈ 0.555 suggest two modes (Pfister et al., 2013). Strong skew also inflates it, so read it with the Distribution tab.
Structure
Is there a network echo chamber?
Neighbour r Pearson correlation of opinions across edge endpoints, both orientations: Newman's (2003) assortativity. Takes no model parameter as input.
Null model Opinions shuffled across nodes 200× on the same graph; z = (r − mean) / sd. Readout text calls clustering only when z ≥ 3 and r ≥ 0.2.
Nbr-mean corr. corr(xi , mean of neighbours), as in Cinelli et al. (2021).
Filter
Selective exposure
1 − Deff /Draw : the share of edge disagreement removed by the β-weights. It is 0 whenever β = 0 and positive for any spread-out state once β > 0, including i.i.d. opinions on an unstructured graph. It describes the filter, not clustering. v1.1 labelled this quantity "echo-chamber index".
References
Scientific context
DeGroot (1974). Reaching a consensus. JASA 69.
Hegselmann & Krause (2002). Opinion dynamics and bounded confidence. JASSS 5(3).
Newman (2003). Mixing patterns in networks. Phys. Rev. E 67.
Holme & Newman (2006). Coevolution of networks and opinions. Phys. Rev. E 74.
Cinelli et al. (2021). The echo chamber effect on social media. PNAS 118(9).
Watts & Strogatz (1998); Barabási & Albert (1999); Erdős & Rényi (1959).
SocialDynamics Lab · Anouar ImelMIT License · v""" + VERSION + """
"""
# ── Interface ───────────────────────────────────────────────────────────────
with gr.Blocks(title="SocialDynamics Lab — opinion dynamics simulator") as demo:
gr.HTML(MASTHEAD)
probe_btn = gr.Button("probe", visible=False)
probe_out = gr.Textbox(visible=False)
probe_btn.click(_zerogpu_startup_probe, outputs=probe_out, api_visibility="private", show_progress="hidden")
preset_buttons = {}
with gr.Column(elem_classes=["panel", "scn-sec"]):
gr.HTML('Scenarios
Each preset isolates one mechanism and runs immediately. Compare 02 with 03: same filter, different structure.
Parameters Deterministic for a fixed seed.
'
f'
'
)
with gr.Tabs(elem_classes=["viz-tabs"]):
with gr.Tab("Network"):
network_plot = gr.Plot(show_label=False)
with gr.Tab("Evolution"):
evolution_plot = gr.Plot(show_label=False)
with gr.Tab("Diagnostics"):
metric_plot = gr.Plot(show_label=False)
with gr.Tab("Distribution"):
distribution_plot = gr.Plot(show_label=False)
with gr.Tab("Influence map"):
influence_plot = gr.Plot(show_label=False)
summary = gr.HTML(_waiting("Ready", "Configure an experiment or load a scenario, then run it."))
download = gr.DownloadButton("Download CSV", value=None, interactive=False, size="sm", elem_classes=["dl-btn"])
with gr.Column(elem_classes=["panel", "compare-sec"], elem_id="compare"):
with gr.Row(equal_height=True, elem_classes=["cmp-head"]):
gr.HTML('Topology comparison
Rerun the current configuration on all three network families over several seeds.
Random Small world Scale free
No comparison yet Run “Compare networks” to score all three topologies side by side. Filter vs chamber
Sweep confirmation bias from 0 to 1 with the current settings (100 agents), with and without rewiring. If the filter and the structure were the same thing, the two curves would move together.