Repeated Auctions in Incomplete Information Settings with Learning Bidders

最近，电子市场的数量有了巨大的增长，这些市场迎合了不同的参与者，如广告商、零售商、出版商、贸易商和自由职业者。这类市场的一个统一特征是，参与者并不掌握与收益相关的市场特征的完整信息，他们必须在与市场的多次互动中学习这些信息。本研究的目的是研究重复拍卖市场中，投标人对拍卖物品的信息不完全，并分析由此产生的动态投标行为。这种拍卖出现在在线广告市场，在维持互联网上提供的各种服务方面发挥着重要作用。从这项研究中获得的见解将用于评估和改善这些市场的设计和运作。PI致力于让来自代表性不足群体的学生参与这项研究计划，并将通过康奈尔大学的各种外展计划参与外展工作。重复拍卖市场固有的学习激励要求每个投标人评估他或她的行为及其竞争对手对市场状态未来演变的影响。为了了解如何投标人的行为，在这样的设置，本研究将首先使用动态规划的工具，以获得有关投标人的最优策略的结构，她的竞争对手的固定策略的见解。利用这些结构性的见解，研究人员将描述由此产生的均衡在市场上，每个投标人的最佳反应，以他人。我们将开发基于迭代方案的算法来近似计算这样的均衡，并数值分析市场的设计如何影响其均衡特性。我们将使用的数值研究，以确定实际的指导方针，以改善重复拍卖市场的设计和运作，在最大限度地提高其收入和整体效率。

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.