Sequential Mechanisms with ex-post Participation Guarantees
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Abstract (arXiv)
We provide a characterization of revenue-optimal dynamic mechanisms in settings where a monopolist sells k items over k periods to a buyer who realizes his value for item i in the beginning of period i. We require that the mechanism satisfies a strong individual rationality constraint, requiring that the stage utility of each agent be positive during each period. We show that the optimum mechanism can be computed by solving a nested sequence of static (single-period) mechanisms that optimize a tradeoff between the surplus of the allocation and the buyer's utility. We also provide a simple dynamic mechanism that obtains at least half of the optimal revenue. The mechanism either ignores history and posts the optimal monopoly price in each period, or allocates with a probability that is independent of the current report of the agent and is based only on previous reports. Our characterization extends to multi-agent auctions. We also formulate a discounted infinite horizon version of the problem, where we study the performance of "Markov mechanisms."
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Mar 2016.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.
4 papers.
- 2023Leveraging Reviews: Learning to Price with Buyer and Seller Uncertaintycited by 1
- 2024Deep Reinforcement Learning for Sequential Combinatorial Auctionscited by 1
- 2024Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learningcited by 0
- 2026Pathwise Individual Rationality in Federated Learning: A Mechanism-Architecture Co-Designcited by 0
- 2026Pathwise Individual Rationality in Federated Learning: A Mechanism-Architecture Co-Design13 Sep 2026
- 2024Deep Reinforcement Learning for Sequential Combinatorial Auctions10 Jul 2024
- 2024Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning6 Jul 2024
- 2023Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty20 Feb 2023