# Sequential Mechanisms with ex-post Participation Guarantees

arXiv: https://arxiv.org/abs/1603.07229 · HTML: https://mlsanity.com/paper/1603.07229/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2016.

## Built on by (most cited first, 4 of 4)

- [Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty](https://mlsanity.com/paper/2302.09700/) (2023)
- [Deep Reinforcement Learning for Sequential Combinatorial Auctions](https://mlsanity.com/paper/2407.08022/) (2024)
- [Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning](https://mlsanity.com/paper/2407.04898/) (2024)
- [Pathwise Individual Rationality in Federated Learning: A Mechanism-Architecture Co-Design](https://mlsanity.com/paper/2609.14591/) (2026)

## 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."