Learning to bid: The design of auctions under uncertainty and adaptation

Thomas Noe (), Michael Rebello and Jun Wang

Games and Economic Behavior, 2012, vol. 74, issue 2, 620-636

Abstract: We examine auction design in a context where symmetrically informed adaptive agents with common valuations learn to bid for a good. Despite the absence of private valuations, asymmetric information, or risk aversion, bidder strategies do not converge to the Bertrand–Nash equilibrium strategies even in the long run. Deviations from equilibrium strategies depend on uncertainty regarding the value of the good, auction structure, the agentsʼ learning model, and the number of bidders. Although individual agents learn Nash bidding strategies in isolation, the learning of each agent, by flattening the best-reply correspondence of other agents, blocks common learning. These negative externalities are more severe in second-price auctions, auctions with many bidders, and auctions where the good has an uncertain value ex post.

Keywords: Auction design; Adaptive learning; Genetic algorithm (search for similar items in EconPapers)
JEL-codes: C63 D44 D83 (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:gamebe:v:74:y:2012:i:2:p:620-636

DOI: 10.1016/j.geb.2011.08.005

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