Structural leverage and fictitious play in sequential auctions

We model sequential, sealed-bid auctions as a sequential game with imperfect and incomplete information. We de-velop an agent that, through fictitious play, constructs a policy for the auctions that takes advantage of information learned in the early stages of the game, and is flexible with respect to assumptions about the other bidders ’ valuations. Because the straightforward expansion of the incomplete information game is intractable, we develop more concise representa-tions that take advantage of the sequential auctions ’ natural structure. We examine the performance of our agent versus agents that play perfectly, agents that also create policies us-ing Monte-Carlo, and agents that play myopically. The tech-nique performs quite well in these empirical studies, though the tractable problem size is still quite small