Can robots that take over from fading allies keep a crowd tipped, and is that cheaper than fielding them upfront?

What it found

The society-allyfade model (20% allies, memory 9, 200 people, 12 seeds, 2000 rounds) where a fading ally becomes a committed robot with probability h. With 10% robots at the start, h = 0.5 tips 10 of 12 crowds at fade rate c = 0.03 but ends with 0.19 robots, while 0.15 robots bought upfront do the same, so hand-over is dominated by upfront robots; it helps only on the margin (slow fading, h = 0.1 lifts 11 to 12 of 12). At 0 to 2% initial robots only h = 1 tips, ending near the no-ally threshold 0.2165. Stylised: c and h are swept, not measured; instant replacement; simulated walkers only.

A figure from its own output

Paper, code and results

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

More in Robot societies among people

• 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.
• 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).

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.