How many line-cutting robots does it take to break a queueing norm?

What it found

People remember the outcomes of the last 9 cut attempts they saw and cut when most succeeded; an attempt is objected to with probability p0(1−z)^k, so objection fades as cutting spreads. Committed cutting robots break the norm at a mean-field share of 0.127 (p0 = 0.9, k = 1): below it 0.2–3.4% of people cut, above it all do. Simulation (200 people, 12 seeds) tips 0/12 crowds at 0.107, 10/12 at 0.127 and 12/12 from 0.147. The threshold ranges 0–0.214 over p0 0.7–1.0 (0.214 at p0 = 1 against 0.217 in society-robotnorms) and 0.395–0.007 over k 0.5–4. Stylised: p0 and k have no source I could find and are swept; binary one-counter model.

A figure from its own output

Paper, code and results

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

More in Robot societies among people

• Does the observed rate of objection to line-cutting keep a queueing norm in a robot crowd?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.
• Does a signal-reading door fleet help people once robots are not counted, and for whom?Does a signal-reading door fleet help people once robots are not counted? Only if the urgent cue costs them about a third of what it costs others, or a planner weights urgent waiting; an equal-cost cue hurts people at every weight tried.
• Can a local mean-field predict which robot blocks tip a ring crowd's passing-side convention?A deterministic local mean-field agrees with the society-allies simulation on tip/no-tip in 145 of 160 cells and is almost never too optimistic, but it overstates the share of evenly spread robots needed (0.20 against 0.12 at k = 2).
• 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%.

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.