Can a local mean-field predict which robot blocks tip a ring crowd's passing-side convention?

What it found

A fixed-point map for the ring walking-side game (one number per person, memory slots as independent draws, N = 200, M = 9, ε = 0.02) is compared with the society-allies simulation over 160 cells (robot share 0.04–0.40, blocks 1/2/4/8, neighbourhood k = 1/2/3/5, 10 seeds). It matches the simulated tip/no-tip outcome in 145 cells; in 14 of the other 15 the simulation tips and the map does not. It reproduces the rise of the threshold with block size and its fall with k, within one 0.04 grid step for blocks of 4 and 8 in all 6 such cells at k ≥ 2, but puts the spread-robot threshold at 0.20 against 0.12 simulated. Limits: stylised 1D binary game with chosen M and ε; the cause of the gap (finite-size noise or the memory-independence step) is not tested; thresholds are coarse (±0.04).

Paper, code and results

• README and method
• White paper
• Experiments and results
• Code
• Tests

More in Robot societies among people

• Is the mean-field's overshoot on the ring crowd caused by finite-size noise or by memory correlation?Neither alone: a noisy memoryless map tips at 0.06 robot share for spread robots, the simulation at 0.10 and the deterministic mean-field at 0.18 (k = 2).
• Does a measured walking-side preference pin down how many robots tip a crowd's passing side?Fitting the stubborn-bloc model to the side preference Moussaïd et al. (2009) measured for two head-on walkers (about 17.5% left, read from their Fig. 4) puts the mean-field tipping robot share at 0.075 to 0.096 for memory 9 across error rates 0.02 to 0.30, against 0.217 for an unbiased crowd; memory matters more (0.033 to 0.053 at 5, 0.133 to 0.149 at 17) and a stubborn-right bloc raises it.
• Should a robot yield only to people in a hurry, or to everyone?Should a robot yield only to people in a hurry, or to everyone? With 30% robots and 20% urgent people, a fleet that yields only to the urgent cuts their cost by 40% (1.130 to 0.676) at a 3% cost to everyone else, is individually stable, and costs the robots 29%.
• How many robots does it take to change how a crowd walks?How many robots does it take to change which side a crowd walks on? Robots that hold a programmed side tip a crowd once they are 16–32% of it, more when people remember more past partners (simulation matches mean field within 0.02); just short of that they make people collide up to 14× as often as adaptive robots, and placement matters more than numbers when people meet only neighbours. Stylised.
• Do robots that keep to one side make people walk on it, or jam the corridor?Do robots that keep to one side make people walk on it, or jam the corridor? In a social-force corridor, robots that adapt change nothing, but robots that hold the left cut people's speed from 0.91 to 0.39 of desired by 30% of the crowd and halve lane separation, while people move to their side only gradually (no sharp tipping, unlike the 1D model). Stylised.
• Can a robot fleet's reputation survive one bad incident?Can a robot fleet's reputation survive one bad incident? In a model where trust spreads by experience and gossip, a dent always recovers because people keep meeting robots; reputation is lost only through avoidance (at a 0.30 avoid-below threshold, 20/20, 18/20 and 0/20 crowds recover under neutral, 2× and 4× negativity in gossip), and robots that explain failures recovered every time. Stylised.

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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.