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Civilizations & group psychology

Simulated societies at scale: how opinions, norms and cultures form, split and spread, and which interventions change the path of a large group of people, tested across the modelling assumptions nobody has measured.

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Which interventions bring a polarized society back together, and does contact backfire?

research/civ-levers · 35 tests

Which interventions bring a polarized society back together? In an opinion-dynamics model with backfire swept from none to strong, bridging ties across camps never merges them and at a rejection threshold of 0.7 rewiring 10% of ties drives everyone to the extremes, while a ladder of committed voices within reach merges them in 24 of 25 settings (moderate broadcast 19, nudge 14, tolerance 8, bridging 2); interactive simulator included (tolerance and backfire are swept, not measured).

Abstract

1,000 simulated people in two camps (opinions near 0.2 and 0.8, 95% of ties within camp) follow bounded confidence (Deffuant et al. 2000) with optional repulsion from distant views (Jager & Amblard 2005); the model reproduces Deffuant’s floor(1/(2ε)) cluster count and the Hegselmann–Krause consensus threshold. Six levers are swept over dose, tolerance ε 0.2–0.4 and rejection threshold τ (none, 0.9–0.6), 10 societies each. Rewiring ties across camps never merges them below ε = 0.4 and is the only lever that backfires: at τ = 0.7, rewiring 10% of ties sends everyone to the extremes (10% with no lever). Over 25 settings at top dose, a ladder of committed voices merges the camps in 24, a moderate broadcast or committed moderates in 19, a one-off nudge in 14, raising tolerance in 8 and bridging in 2 (more people at the extremes in 10). A ladder must be spaced for the least tolerant: built for ε = 0.3 when the real one is 0.2, it merges none of 10. Tolerance, rejection threshold and the network are not measured for any real population and are swept, not sourced; opinions are one-dimensional and contact here changes positions only, not prejudice. A follow-up seeds a threshold (complex) contagion on Watts–Strogatz graphs: on clustered networks a patch of 2–3 neighbours starts a cascade that spread-out or random seeds need 12–20 to match, and with threshold 3 nothing past 40 seeds works once ties are rewired (stylised: threshold, degree and graph family chosen, not measured). A third follow-up runs Axelrod’s culture model with a common broadcast: at F = 5, q = 10 on a 20×20 lattice a broadcast heard 30% of the time fragments a society that is one culture without it (largest culture 99% → 26% of sites), because sites sharing no feature with the media never interact with it; the F = 10 order–disorder drop appears at q ≈ 30–50 on small lattices, not reproducing Castellano et al.’s qc ≈ 300 (lattices to L = 150, not run here). A fifth follow-up builds a toy structural-demographic civilization (Turchin 2003): across 864 parameter cells (none measured) it never produces secular cycles, with or without a doubled tax, an elite cap or a welfare transfer, so it cannot yet say which levers damp them. Includes an interactive browser simulator.

Other research topics

Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1

Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.