Optimal Auction Design with Deferred Inspection and Reward

Saeed Alaei (), Alexandre Belloni (), Ali Makhdoumi () and Azarakhsh Malekian ()
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Saeed Alaei: Google Research, Mountain View, California 94043
Alexandre Belloni: Fuqua School of Business, Duke University, Durham, North Carolina 27708; Amazon, WW FBA, North Carolina
Ali Makhdoumi: Fuqua School of Business, Duke University, Durham, North Carolina 27708
Azarakhsh Malekian: Rotman School of Management, University of Toronto, Toronto, Ontario M5S 3E6, Canada

Operations Research, 2024, vol. 72, issue 6, 2413-2429

Abstract: 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.

Keywords: Market Analytics and Revenue Management; optimal auction design; Bayesian mechanism design; deferred inspection; calculus of variations; Border’s theorem (search for similar items in EconPapers)
Date: 2024
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