A Courteous Learning Rule for Ad-hoc Anti-coordination
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In this paper, we investigate the problem of anti-coordination under rationality constraints in ad-hoc resource allocation settings. Inspired by human behavior, we propose a framework (CA3NONY) that enables fast convergence to efficient and fair allocations based on a simple convention of courtesy. We prove that following such convention induces a strategy which constitutes an approximate subgame-perfect equilibrium of the repeated resource allocation game with discounting. Simulation results highlight the effectiveness of CA3NONY as compared to state-of-the-art bandit algorithms, since it achieves more than two orders of magnitude faster convergence, higher efficiency, fairness, and average payoff for the agents.
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