AUCTIONS WITH ADAPTIVE ARTIFICIALLY INTELLIGENT AGENTS

Abstract

While there is an extremely active theoretical literature on auctions, little is known about the actual behavior of bidders in real auctions. This is partly due to the scientist's inability to observe the values or strategies of bidders. When values are induced using experiments, subjects are often unable to formulate Nash equilibrium bids. Rather than using human subjects, this paper examines the ability of agents to learn bidding strategies by using artificially intelligent agents who learn adaptively. We examine first-price common-value auctions, and first-and second-price auctions with both affiliated and independent private-values. We find that there are many striking parallels between the artificial agents and humans. Since we can observe both the values and the strategies of the artificial agents, we are able to conjecture about .the behavior of humans in the different auction environments. With this model of adaptive learning, we are able to understand many of the "bidder errors" 9bserved in experiments. We suggest several new hypotheses about bidding behavior, and about the various auction institutions.