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arXiv Machine Learning By Yefei Zhang, Yuxuan Zhao

Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas

Read the original on arXiv Machine Learning →

The study investigates how spatial patterns arise when agents in public goods dilemmas learn movement strategies via tabular Q‑learning. Cooperators tend to cluster around resource peaks, while co‑adaptation with defectors can alter cluster strength and motion, sometimes producing traveling bands. Higher cooperator learning rates combined with lower defector rates lead to the greatest welfare losses, and crowding penalties can mitigate these losses, linking learning rates to spatial organization and collective welfare.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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