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We consider a team of autonomous agents that navigate in an adversarial environment and aim to achieve a task by allocating their resources over a set of target locations. An adversary in the environment observes the autonomous team's…

Optimization and Control · Mathematics 2023-10-09 Shenghui Chen , Yagiz Savas , Mustafa O. Karabag , Brian M. Sadler , Ufuk Topcu

In recent years online social networks have become increasingly prominent in political campaigns and, concurrently, several countries have experienced shock election outcomes. This paper proposes a model that links these two phenomena. In…

Theoretical Economics · Economics 2020-11-03 Edoardo Gallo , Alastair Langtry

This effort is focused on examining the behavior of reinforcement learning systems in personalization environments and detailing the differences in policy entropy associated with the type of learning algorithm utilized. We demonstrate that…

Machine Learning · Computer Science 2024-04-30 Anton Dereventsov , Andrew Starnes , Clayton G. Webster

We study the limiting behavior of the mixed strategies that result from optimal no-regret learning strategies in a repeated game setting where the stage game is any 2 by 2 competitive game. We consider optimal no-regret algorithms that are…

Computer Science and Game Theory · Computer Science 2022-03-03 Vidya Muthukumar , Soham Phade , Anant Sahai

Motivated by the scarcity of accurate payoff feedback in practical applications of game theory, we examine a class of learning dynamics where players adjust their choices based on past payoff observations that are subject to noise and…

Optimization and Control · Mathematics 2016-06-03 Mario Bravo , Panayotis Mertikopoulos

We propose a game-theoretic framework that incorporates both incomplete information and general ambiguity attitudes on factors external to all players. Our starting point is players' preferences on payoff-distribution vectors, essentially…

Economics · Quantitative Finance 2017-04-04 Jian Yang

We study uncoordinated matching markets with additional local constraints that capture, e.g., restricted information, visibility, or externalities in markets. Each agent is a node in a fixed matching network and strives to be matched to…

Computer Science and Game Theory · Computer Science 2014-09-16 Martin Hoefer , Lisa Wagner

We study the problem of resilient strategies in the presence of uncertainty. Resilient strategies enable an agent to make decisions that are robust against disturbances. In particular, we are interested in those disturbances that are able…

Computer Science and Game Theory · Computer Science 2026-03-02 Kush Grover , Markel Zubia , Debraj Chakraborty , Muqsit Azeem , Nils Jansen , Jan Kretinsky

We study the plausibility of sub-optimal Nash equilibria of the direct revelation mechanism associated with a strategy-proof social choice function. By using the recently introduced empirical equilibrium analysis (Velez and Brown, 2019,…

Theoretical Economics · Economics 2020-07-08 Rodrigo A. Velez , Alexander L. Brown

Evolutionary game dynamics in structured populations are strongly affected by updating rules. Previous studies usually focus on imitation-based rules, which rely on payoff information of social peers. Recent behavioral experiments suggest…

Populations and Evolution · Quantitative Biology 2018-10-02 Lei Zhou , Bin Wu , Jinming Du , Long Wang

Students' decisions on whether to take a class are strongly affected by whether their friends plan to take the class with them. A student may prefer to be assigned to a course they likes less, just to be with their friends, rather than…

Artificial Intelligence · Computer Science 2023-09-19 Ilya Khakhiashvili , Lihi Dery , Tal Grinshpoun

How do incentive levels affect strategic behaviour? We address this with an experiment that separately identifies own- and opponent-incentive effects in two dominance-solvable games that differ in strategic complexity. Higher own incentives…

Theoretical Economics · Economics 2026-03-30 Teresa Esteban-Casanelles , Duarte Gonçalves

Fully cooperative multiagent systems - those in which agents share a joint utility model- is of special interest in AI. A key problem is that of ensuring that the actions of individual agents are coordinated, especially in settings where…

Computer Science and Game Theory · Computer Science 2013-02-18 Craig Boutilier

We propose a pseudo-market solution to resource allocation problems subject to constraints. Our treatment of constraints is general: including bihierarchical constraints due to considerations of diversity in school choice, or scheduling in…

Theoretical Economics · Economics 2020-11-09 Federico Echenique , Antonio Miralles , Jun Zhang

For AI systems to be useful to humans, they must understand and act in accordance with our values and preferences. Since specifying preferences is a hard task, inverse reinforcement learning (IRL) aims to develop methods that allow for…

Artificial Intelligence · Computer Science 2026-05-12 Karim Abdel Sadek , Mark Bedaywi , Rhys Gould , Stuart Russell

The timing of strategic exit is one of the most important but difficult business decisions, especially under competition and uncertainty. Motivated by this problem, we examine a stochastic game of exit in which players are uncertain about…

Optimization and Control · Mathematics 2023-10-09 H. Dharma Kwon , Jan Palczewski

We investigate in this paper the theory and econometrics of optimal matchings with competing criteria. The surplus from a marriage match, for instance, may depend both on the incomes and on the educations of the partners, as well as on…

General Economics · Economics 2021-02-26 Alfred Galichon , Bernard Salanié

I study robust comparative statics for risk-averse subjective expected utility (SEU) maximizers. Starting with a finite menu of actions totally ordered by sensitivity to risk, I identify the transformations of her menu that lead a…

Theoretical Economics · Economics 2025-05-27 Mark Whitmeyer

We address the following dynamic version of the school choice question: a city, named City, admits students in two temporally-separated rounds, denoted $\mathcal{R}_1$ and $\mathcal{R}_2$. In round $\mathcal{R}_1$, the capacity of each…

Computer Science and Game Theory · Computer Science 2020-07-24 Karthik Gajulapalli , James Liu , Tung Mai , Vijay V. Vazirani

This paper studies mechanism design environments in which the designer does not know the distribution of agents' private information a priori and instead learns from agents' behavior induced by the mechanism itself. We formalize a notion of…

Theoretical Economics · Economics 2026-03-16 Zhiming Feng , Qingmin Liu