Repeated Auctions in Incomplete Information Settings with Learning Bidders

Recently, there has been a tremendous growth in the number of electronics markets that cater to a diverse set of participants such as advertisers, retailers, publishers, traders, and freelance workers. A unifying feature of such markets is that the participants do not possess complete information about payoff-relevant market features, which they must learn during their multiple interactions with the market. The goal of this research activity is to study repeated auction markets where the bidders have incomplete information about the items being auctioned, and analyze the resulting dynamic bidding behavior. Such auctions arise in online advertising markets and play a significant role in sustaining a wide range of services available on the Internet. The insights obtained from this research will be used to evaluate and improve the design and operation of such markets. The PI is committed to involving students from underrepresented groups into this research program, and will engage in outreach efforts through various outreach programs at Cornell University.

The learning incentives inherent in repeated auction markets require each bidder to evaluate the effects of his or her actions and those of his or her competitors on the future evolution of the market state. In order to understand how a bidder behaves in such settings, this research will first use the tools of dynamic programming to obtain insights about the structure of the bidder's optimal strategy for given fixed strategies of her competitors. Using these structural insights, the researcher will then characterize the resulting equilibrium in the market where each bidder reacts optimally to others. We will develop algorithms based on iterative schemes to approximately compute such equilibria and numerically analyze how the design of the market affects its equilibrium properties. We will use the numerical investigations to identify practical guidelines for improving the design and operation of repeated auction markets, in terms of maximizing its revenue and overall efficiency.