← All projectsGitHub
Robot societies among people

Can a few queue-enforcing robots hold a queueing norm against line-cutting robots?

research/society-enforcers

How many enforcer robots hold a queue against line-cutting robots? About 5% of the crowd holds the norm against 30% cutters (memory 9, 10 people in view of each cut, stop chance 0.5), simulation agrees. Stylised: view size and stop chance are swept, not sourced.

6 tests

What it found

society-lineup found that committed line-cutting robots break a queueing norm at 12.7% of the crowd because bystander objection fades as cutting spreads. Here a share e of enforcer robots queue and object to cuts they see: each of L people in view is an enforcer with probability e, and each stops the cut with probability q, and unlike human objection this does not fade. With L = 10, q = 0.5, memory 9 and lone-cut human objection 0.9, the cutter share that breaks the norm rises from 0.127 with no enforcers to 0.201 at 3% and 0.277 at 5% enforcers. Holding the norm against 30% cutters takes 5.4% enforcers (9.7% with 5 people in view, 2.9% with 20); against 50% cutters, 7.7%. Only the product q·L matters to first order (q = 0.25 gives the same numbers as 0.5 with half the view). Simulation (200 people, 12 seeds) holds 0 of 12 crowds at 0.044 enforcers, 4 of 12 at the mean-field value 0.054 and 12 of 12 from 0.064. There is no hysteresis: a crowd that already cuts recovers at the same enforcer share. Stylised: L and q have no source and are swept; binary one-counter model.

A figure from its own output

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

Paper, code and results

More in Robot societies among people

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.