Strategic software agents in continuous double auction under dynamic environments

Abstract

Internet auctions in open dynamic environments have been attracting increasing attention. We analyze with a bottom-up approach the competition between artificial intelligent agents in Continuous Double Auction markets. In almost all previous works agents have a fixed bidding strategy during the auction, usually under static symmetric environments. In our simulations we allow the soft-agents to learn not only about how much they should bid or ask, but also about possible switching between the alternative strategies. We examine the behaviour of strategic traders under dynamic asymmetric environments thus extending previous results. This analysis is important in the design and performance of auctions in the real world (stock exchanges, commodity markets, emission permits, and B2B exchanges) and in the applications of auction theory to many problems in management and production, far beyond market design (market oriented programming). © Springer-Verlag Berlin Heidelberg 2006.