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Learning to bid in sequential Dutch Auctions

By Eric Guerci, Alan Kirman and Sonia Moulet

Abstract

International audienceWe propose an agent-based computational model to investigate sequential Dutch auctions with particular emphasis on markets for perishable goods and we take as an example wholesale fish markets. Buyers in these markets sell the fish they purchase on a retail market. The paper provides an original model of boundedly rational behavior for wholesale buyers' behavior incorporating learning to improve profits, conjectures as to the bids that will be made and fictitious learning. We analyse the dynamics of the aggregate price under different market conditions in order to explain the emergence of market price patterns such as the well-known declining price paradox and the empirically observed fact that the very last transactions in the day may be at a higher price. The proposed behavioral model provides alternative explanations for market price dynamics to those which depend on standard hypotheses such as diminishing marginal profits. Furthermore, agents learn the option value of having the possibility of bidding in later rounds. When confronted with random buyers, such as occasional participants or new entrants, they learn to bid in the optimal way without being conscious of the strategies of the other buyers. When faced with other buyers who are also learning their behavior still displays some of the characteristics learned in the simpler case even though the problem is not analytically tractable

Topics: Multi-agent learning, Auction markets, Agent-based computational economics, [SHS.ECO]Humanities and Social Sciences/Economics and Finance
Publisher: 'Elsevier BV'
Year: 2014
DOI identifier: 10.1016/j.jedc.2014.09.029
OAI identifier: oai:HAL:halshs-01069634v1
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