Does the observed rate of objection to line-cutting keep a queueing norm in a robot crowd?
research/society-milgramfit
Do real objection rates keep a queue norm? People alone need to stop a lone cut at least 0.786 of the time; Milgram's field reactions (about 0.5, secondary source) imply 7.5% enforcer robots even with no cutters. Inputs not read from the paper; p0 is swept.
What it found
society-enforcers assumed people stop a lone line-cut with probability p0 = 0.9. In the same memory-9 model, people alone keep a queueing norm only if p0 ≥ 0.786 (0.901 at memory 3, 0.698 at 25). Milgram et al. (1986) report, in a popular summary I have not checked against the paper, that field crowds reacted to one intruder about half the time and ejected the intruder 10% of the time, which bounds p0 between 0.1 and 0.5. Across that range the norm needs enforcers even with no cutter robots: 7.5% at p0 = 0.5 and 12.3% at 0.1 (10 in view, stop chance 0.5), and 9.2% and 12.6% against 30% cutters, versus 5.4% at 0.9. Two intruders drew 91% objection against 75% if viewers were independent. Limitations: inputs from a secondary source, a reaction is not a stopped cut, L and q unsourced, binary memory model.
A figure from its own output
Paper, code and results
More in Robot societies among people
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All 31 Robot societies among people projects →
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.