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Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals
Junyan Liu, Arnab Maiti, Artin Tajdini, Kevin Jamieson, Lillian J. Ratliff
https://arxiv.org/abs/2505.23124

Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals
We initiate the study of a repeated principal-agent problem over a finite horizon $T$, where a principal sequentially interacts with $K\geq 2$ types of agents arriving in an adversarial order. At each round, the principal strategically chooses one of the $N$ arms to incentivize for an arriving agent of unknown type. The agent then chooses an arm based on its own utility and the provided incentive, and the principal receives a corresponding reward. The objective is to minimize regret against the…

Flexible Moral Hazard Problems with Adverse Selection
Siwen Liu
https://arxiv.org/abs/2506.23954 https://arxiv.org/pdf/2506.23954

Flexible Moral Hazard Problems with Adverse Selection
We study a moral hazard problem with adverse selection: a risk-neutral agent can directly control the output distribution and possess private information about the production environment. The principal designs a menu of contracts satisfying limited liability. Deviating from classical models, not only can the principal motivate the agent to exert certain levels of aggregate efforts by designing the "power" of the contracts, but she can also regulate the support of the chosen output distributions…

An Algorithm-to-Contract Framework without Demand Queries
Ilan Doron-Arad, Hadas Shachnai, Gilad Shmerler, Inbal Talgam-Cohen
https://arxiv.org/abs/2507.20038 https://

An Algorithm-to-Contract Framework without Demand Queries
Consider costly tasks that add up to the success of a project, and must be fitted by an agent into a given time-frame. This is an instance of the classic budgeted maximization problem, which admits an approximation scheme (FPTAS). Now assume the agent is performing these tasks on behalf of a principal, who is the one to reap the rewards if the project succeeds. The principal must design a contract to incentivize the agent. Is there still an approximation scheme? In this work, our ultimate goal …

Ultra-Efficient Contracts: Breaking the Substitutes Barrier in Combinatorial Contracts
Michal Feldman, Liat Yashin
https://arxiv.org/abs/2506.18008 https:/…

Ultra-Efficient Contracts: Breaking the Substitutes Barrier in Combinatorial Contracts
We study the optimal contract problem in the framework of combinatorial contracts, introduced by Duetting et al. [FOCS'21], where a principal delegates the execution of a project to an agent, and the agent can choose any subset from a given set of costly actions. At the core of the model is a reward function - a monotone set function that maps each set of actions taken by the agent into an expected reward to the principal. To incentivize the agent, the principal offers a contract specifying the…

Broad Validity of the First-Order Approach in Moral Hazard
Eduardo Azevedo, Ilan Wolff
https://arxiv.org/abs/2506.18873 https://arxiv…

Broad Validity of the First-Order Approach in Moral Hazard
The first-order approach (FOA) is the main tool for the moral hazard principal-agent problem. Although many existing results rely on the FOA, its validity has been established only under relatively restrictive assumptions. We demonstrate in examples that the FOA frequently fails when the agent's reservation utility is low (such as in principal-optimal contracts). However, the FOA broadly holds when the agent's reservation utility is at least moderately high (such as in competitive settings wher…