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Principal-agent problems arise when one party acts on behalf of another, leading to conflicts of interest. The economic literature has extensively studied principal-agent problems, and recent work has extended this to more complex scenarios…

Artificial Intelligence · Computer Science 2024-01-02 Omer Ben-Porat , Yishay Mansour , Michal Moshkovitz , Boaz Taitler

We study principal-agent problems in which a principal commits to an outcome-dependent payment scheme (a.k.a. contract) so as to induce an agent to take a costly, unobservable action. We relax the assumption that the principal perfectly…

Computer Science and Game Theory · Computer Science 2021-06-02 Matteo Castiglioni , Alberto Marchesi , Nicola Gatti

We propose a solution and a mechanism for two-agent social choice problems with large (infinite) policy spaces. Our solution is an efficient compromise rule between the two agents, built on a common cardinalization of their preferences. Our…

Theoretical Economics · Economics 2026-02-03 Federico Echenique , Matías Núñez

In this paper we propose a numerical method to obtain an approximation of Nash equilibria for multi-player non-cooperative games with a special structure. We consider the infinite horizon problem in a case which leads to a system of…

Numerical Analysis · Mathematics 2016-02-19 Simone Cacace , Emiliano Cristiani , Maurizio Falcone

A prominent theme in behavioural contract theory is the study of present-biased agents represented through quasi-hyperbolic discounting. In a model of competitive credit provision, we study an alternative to this framework in which the…

Theoretical Economics · Economics 2026-02-11 Siddharth Chatterjee , Daniel F. Garrett

We consider the robust contract design problem when the principal only has limited information about the actions the agent can take. The principal evaluates a contract according to its worst-case performance caused by the uncertain action…

Theoretical Economics · Economics 2024-06-18 Bo Peng , Zhihao Gavin Tang

We consider the sequential decision-making problem of making proactive request assignment and rejection decisions for a profit-maximizing operator of an autonomous mobility on demand system. We formalize this problem as a Markov decision…

Machine Learning · Computer Science 2023-05-11 Tobias Enders , James Harrison , Marco Pavone , Maximilian Schiffer

We study a single-agent contracting environment where the agent has misspecified beliefs about the outcome distributions for each chosen action. First, we show that for a myopic Bayesian learning agent with only two possible actions, the…

Computer Science and Game Theory · Computer Science 2024-06-03 Yingkai Li , Argyris Oikonomou

We study principal-agent problems in which a principal commits to an outcome-dependent payment scheme -- called contract -- in order to induce an agent to take a costly, unobservable action leading to favorable outcomes. We consider a…

Computer Science and Game Theory · Computer Science 2024-06-10 Francesco Bacchiocchi , Matteo Castiglioni , Alberto Marchesi , Nicola Gatti

Fairness is desirable yet challenging to achieve within multi-agent systems, especially when agents differ in latent traits that affect their abilities. This hidden heterogeneity often leads to unequal distributions of wealth, even when…

Computer Science and Game Theory · Computer Science 2025-06-23 Jakub Tłuczek , Victor Villin , Christos Dimitrakakis

We consider quadratic, nonmonotone generalized Nash equilibrium problems with symmetric interactions among the agents. Albeit this class of games is known to admit a potential function, its formal expression can be unavailable in several…

Optimization and Control · Mathematics 2022-03-31 Filippo Fabiani , Andrea Simonetto , Paul J. Goulart

We provide a generic method to find full dynamical solutions to binary decision models with interactions. In these models, agents follow a stochastic evolution where they must choose between two possible choices by taking into account the…

Statistical Mechanics · Physics 2022-03-01 James Holehouse , José Moran

Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings. We present an actor-critic algorithm that trains decentralized policies in…

Machine Learning · Computer Science 2019-05-29 Shariq Iqbal , Fei Sha

We study a two-period moral hazard problem; there are two agents, with action sets that are unknown to the principal. The principal contracts with each agent sequentially, and seeks to maximize the worst-case discounted sum of payoffs,…

Theoretical Economics · Economics 2024-02-15 Chang Liu

In this paper, a new on-line scheme is presented to design the optimal coordination control for the consensus problem of multi-agent differential games by fuzzy adaptive dynamic programming (FADP), which brings together game theory,…

Optimization and Control · Mathematics 2017-12-01 Huaguang Zhang , Jilie Zhang , Guang-Hong Yang , Yanhong Luo

We propose an optimal solution to a deterministic dynamic assignment problem by leveraging connections to the theory of discrete optimal transport to convert the combinatorial assignment problem into a tractable linear program. We seek to…

Multiagent Systems · Computer Science 2019-10-25 Koray G. Kachar , Alex A. Gorodetsky

In this paper, we study multi-agent network games subject to affine time-varying coupling constraints and a time-varying communication network. We focus on the class of games adopting proximal dynamics and study their convergence to a…

Computer Science and Game Theory · Computer Science 2019-11-20 Carlo Cenedese , Giuseppe Belgioioso , Sergio Grammatico , Ming Cao

In this paper, we show the equivalence between a constrained, multi-agent control problem, modeled within the port-Hamiltonian framework, and an exact potential game. Specifically, critical distance-based constraints determine a network of…

Optimization and Control · Mathematics 2018-11-20 Filippo Fabiani , Andrea Caiti

Synthesis of finite-state controllers from high-level specifications in multi-agent systems can be reduced to solving multi-player concurrent games over finite graphs. The complexity of solving such games with qualitative objectives for…

Computer Science and Game Theory · Computer Science 2018-09-28 Shaull Almagor , Rajeev Alur , Suguman Bansal

The main challenge of multiagent reinforcement learning is the difficulty of learning useful policies in the presence of other simultaneously learning agents whose changing behaviors jointly affect the environment's transition and reward…