Can a robot that yields to people in a hurry survive people faking a hurry?

What it found

The urgency-reading door of society-urgentdoor, but robots see only a costly signal that can be faked and misread. With 30% robots, 20% urgent people (waiting cost 3x), collision cost 3 and dithering cost 1, no signal cost on a 0 to 1.2 grid lets the signal-reading fleet beat yield-all for the crowd (best case 1.201 against 1.047); the most it saves urgent people is 0.025 per encounter (1.105 against 1.130), about 6% of the 0.45 gained from perfect reading. A free signal is sent by 95% of normal people; a signal dear enough to stop fakers is dearer than the yielding is worth to the urgent, who stop sending it; a 10% misreading rate removes most of what is left. Mean field, checked by simulation within 0.012 with signalling held fixed. Assumed, not sourced: round-figure costs and logit sharpness (swept), a fixed signal cost, no robot learning, random pairing, no geometry.

A figure from its own output

Paper, code and results

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

More in Robot societies among people

• Does the rule that robot blocks must be narrower than the neighbourhood carry over from a ring to a 2D grid?On a 16x16 torus (memory 9), robot patches up to about 2k+1 sites wide tip every crowd at robot share 0.25 and wider ones tip few or none; one 8x8 patch of all the robots tips none at any neighbourhood radius up to 5.
• Can robots that take over from fading allies keep a crowd tipped, and is that cheaper than fielding them upfront?Is a hand-over cheaper than buying robots first? With 20% allies fading at rate c and 10% robots at the start, replacing half of fading allies with robots tips 10 of 12 crowds at c = 0.03 but ends with 0.19 robots, against 0.15 robots upfront in society-allyfade; below 5% initial robots only full replacement tips anything. Stylised
• Do door robots that learn from their own costs keep honouring an urgency cue?Do door robots that learn from their own costs keep honouring an urgency cue? They keep yielding to cue senders but stop going against everyone else, which erases most of the cue's gain to urgent people.
• Do robots that learn at a door drift to going first when collisions are cheap?Do robots that learn at a door drift to going first when collisions are cheap? No: with 30% robots they settle on a mix whose go share falls smoothly with collision cost (0.51 at k = 1.5, 0.03 at k = 2.8), whatever they start from, at a higher crowd cost than a fixed yielding fleet for k of 1 or more.
• How many line-cutting robots does it take to break a queueing norm?How many line-cutting robots break a queueing norm? Mean-field critical share 12.7% (memory 9, lone-cut objection 0.9), simulation agrees; depends mostly on how fast objection fades. Stylised: objection strength is swept, not sourced.
• 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.

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.