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Robot societies among people

How many robots does it take to change a crowd's walking side when some people never adapt?

research/society-stubborn

How many robots does it take to change a crowd's walking side when some people never adapt? With 0, 20, 40 and 50% stubborn walkers the mean-field tipping share (memory 9) rises 0.217, 0.240, 0.283, 0.329, simulation agrees within 0.02, and committed-robot conflict costs barely change. Stylised.

9 tests

What it found

The society-robotnorms committed-robot model with a share s of stubborn people who always walk right. The mean-field tipping share for memory 9 rises from 0.217 (s = 0) to 0.240, 0.283 and 0.329 at s = 0.2, 0.4 and 0.5, and memory 5 and 9 coincide from s = 0.4. Simulation (200 people, 12 seeds) agrees within 0.02 at every s when “tipped” means a majority of adaptive people on the robots' side. Conflict costs of committed and adaptive robots are unchanged by stubborn people. Stylised; simulated walkers only.

A figure from its own output

Figure produced by research/society-stubborn: tipping_by_stubborn
society-stubborn/experiments/tipping_by_stubborn.svg, written by the project's own experiment code.

Paper, code and results

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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.