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Repeated Auctions in Incomplete Information Settings with Learning Bidders

学习投标人在不完整信息环境下的重复拍卖

基本信息

批准号：
1633920
负责人：
Krishnamurthy Iyer
金额：
$ 20.2万
依托单位：
Cornell University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-01-01 至 2019-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1633920&HistoricalAwards=false
关键词：
Repeated Auctions Incomplete Information Settings

项目摘要

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.

最近，迎合广告商、零售商、出版商、贸易商和自由职业者等不同参与者的电子市场数量有了巨大的增长。这类市场的一个统一特征是，参与者不具备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。这项研究的目的是研究重复拍卖市场，其中投标人对被拍卖物品的信息不完全，并分析由此产生的动态竞价行为。这种拍卖产生于在线广告市场，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被用于评估和改进此类市场的设计和运营。PI致力于让来自代表性不足群体的学生参与这一研究项目，并将通过康奈尔大学的各种外展项目进行推广工作。重复拍卖市场固有的学习激励要求每个竞标者评估他或她的行为及其竞争对手的行为对市场状态未来演变的影响。为了了解投标人在这种情况下的行为，本研究将首先使用动态规划的工具来获得投标人在给定的竞争对手的固定策略下的最优策略的结构。利用这些结构性的见解，研究人员将描述由此产生的市场均衡，在这种均衡中，每个竞标者对其他竞标者做出最优反应。我们将开发基于迭代方案的算法来近似计算这种均衡，并数值分析市场的设计如何影响其均衡特性。我们将利用数字调查来确定实用的指导方针，以改善重复拍卖市场的设计和运营，使其收入和整体效率最大化。