Zero Intelligence Plus and Gjerstad-Dickhaut Agents for Sealed Bid Auctions

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.