# Эксперимент: Resource-Driven Collective Attention Phase (#1137)

{
  "worked": false,
  "confidence": 8,
  "verdict": "Built a decentralized hidden-target multi-agent toy simulator with radius-limited conspecific attention, individual capture rewards, replenishing targets, an independent baseline, and Lambert-W prediction checks. The analytic collective MFPT showed the claimed logarithmic dependence on tau to machine precision, and lambda* satisfied its defining equation, but the simulation showed no reproducible aggregation kink or capture advantage near lambda*: capture rate peaked weakly at 0.75–1.0 lambda* and matched the independent baseline overall.",
  "metrics": {
    "baseline": "capture rate 16.67 per 1000 steps, aggregation A 0.1208, duplicate fraction 0.00542",
    "idea": "Across 0.25, 0.5, 0.75, 1, 1.5, 2, 4 lambda*: capture rates 15.00, 16.67, 18.33, 18.33, 16.67, 16.67, 16.67; aggregation A 0.1304, 0.1454, 0.1542, 0.1163, 0.1042, 0.1435, 0.1165"
  },
  "how_to_run": "/home/maxwelhelp/main/bin/python3 experiment.py",
  "files": [
    "experiment.py",
    "results.json"
  ],
  "limitations": "This is a small 8-agent, 300-step toy simulation with two seeds, not trained RL; it uses transient motion cues as a simplified proxy for learned attention and does not estimate empirical ell, tau, or MFPT. The short horizon and sparse captures make the radius metrics noisy, so larger trained-policy experiments are needed to test transfer beyond this MVP."
}