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Zero Intelligence Plus and Gjerstad Dickhaut Agents for Sealed Bid Auctions A J Bagnall and I E Toft School of Computing Sciences University of East Anglia Norwich England NR4 7TJ ajb it cmp uea ac uk Abstract The increasing prevalence of auctions as a method of conducting a variety of transactions has promoted interest in modelling bidding behaviours with simulated agent models The majority of popular research has focused on double auctions i e auctions with multiple buyers and sellers In this paper we investigate agent models of sealed bid auctions i e single seller auctions where each buyer submits a single bid We propose an adaptation of two learning mechanisms used in double auctions Zero Intelligence Plus ZIP and Gjerstad Dickhaut GD for sealed bid auctions The experimental results determine if a single agent adopting ZIP GD bidding mechanisms is able to learn the known optimal strategy through experience We experiment with two types of sealed bid auctions first price sealed bid and second price sealed bid Quantitive analysis shows that whilst ZIP agents learn a good strategy they do not learn the optimal strategy whereas GD agents learn an optimal strategy in first price auctions 1 Introduction The increase in the level of Internet connectivity has allowed the WWW to become a hub for electronic trading places Buyers and sellers are now able to trade in previously inaccessible markets Some of the important questions facing market overseers and traders are what are the optimal strategies for a given auction structure how do agents learn the optimal strategy and how does restriction of information prevent agents from learning a strategy These questions have been addressed through auction theory 7 12 field studies 8 experimental lab studies 9 and agent simulations 2 4 Recently there has been particular interest in the study of agents for continuous double auctions CDA 2 3 5 6 10 We adapt learning mechanisms developed for CDA to investigate agent architectures for



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