Optimal Auction Design with Deferred Inspection and Reward

• 成果类型: Article
• 署名作者: Alaei, Saeed; Belloni, Alexandre; Makhdoumi, Ali; Malekian, Azarakhsh
• 署名单位: Alphabet Inc.; Google Incorporated; Duke University; Amazon.com; University of Toronto
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2020.0651
• 发表日期: 2024
• 页码: 2413-2429
• 关键词: optimal allocation; INFORMATION; CONTRACTS

摘要

Consider a mechanism run by an auctioneer who can use both payment and inspection instruments to incentivize agents. The timeline of the events is as follows. Based on a prespecified allocation rule and the reported values of agents, the auctioneer allocates the item and secures the reported values as deposits. The auctioneer then inspects the values of agents and, using a prespecified reward rule, rewards the ones who have reported truthfully. Using techniques from convex analysis and calculus of variations, for any distribution of values, we fully characterize the optimal mechanism for a single agent. Using Border's theorem and duality, we find conditions under which our characterization extends to multiple agents. Interestingly, the optimal allocation function, unlike the classic settings without inspection, is not a threshold strategy and instead is an increasing and continuous function of the types. We also present an implementation of our optimal auction and show that it achieves a higher revenue than auctions in classic settings without inspection. This is because the inspection enables the auctioneer to charge payments closer to the agents' true values without creating incentives for them to deviate to lower types.