Do stubborn walkers who convert after meeting robots let a smaller robot minority tip a crowd's walking side?

What it found

The society-converts model with 40% stubborn walkers who each convert to an ordinary memory-rule walker after meeting a committed robot k times, instead of at a constant rate. With the block fixed, 28% robots tip 5 of 12 simulated crowds; with exposure conversion at k = 30 every crowd at 24% robots tips (median round 171, 12 of 12) and every stubborn walker has converted by round 1000 at every robot share tried, yet 16% and 20% robots still never tip: the threshold falls to the no-block value, not below it. Tipping time follows the conversion time (about k/ρ rounds). An exposure-blind rate with the same mean time to convert (ρ/k) tips at the same robot shares but up to 1.8 times sooner at larger k (but later at k = 1 and 3) (median round 62 against 110 at 28% robots, k = 30), because it converts a few people from round one. Stylised: well-mixed meetings, no geometry, 200 people, 12 seeds; robot share is the only exposure source.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
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More in Robot societies among people

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• 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
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• 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.

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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.