Evolutionary dynamics and equitable core selection in assignment games
We study evolutionary dynamics in assignment games where many agents interact anonymously at virtually no cost.� The process is decentralized, very little information is available and trade takes place at many different prices simultaneously.� We propose a completely uncoupled learning process that selects a subset of the core of the game with a natural equity interpretation.� This happens even though agents have no knowledge of other agents' strategies, payoffs, or the structure of the game, and there is no central authority with such knowledge either.� In our model, agents randomly encounter other agents, make bids and offers for potential partnerships and match if the partnerships are profitable.� Equity is favored by our dynamics beause it is more stable, not because of any ex ante fairness criterion.
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