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We study repeated two-player games where one of the players, the learner, employs a no-regret learning strategy, while the other, the optimizer, is a rational utility maximizer. We consider general Bayesian games, where the payoffs of both…

Machine Learning · Computer Science 2022-05-19 Yishay Mansour , Mehryar Mohri , Jon Schneider , Balasubramanian Sivan

We study games with incomplete information and characterize when a feasible outcome is Pareto efficient. Outcomes with excessive randomization are inefficient: generically, the total number of action profiles across states must be strictly…

Theoretical Economics · Economics 2025-12-09 Itai Arieli , Yakov Babichenko , Atulya Jain , Rann Smorodinsky

The commitment power of senders distinguishes Bayesian persuasion problems from other games with (strategic) communication. Persuasion games with multiple senders have largely studied simultaneous commitment and signalling settings.…

Theoretical Economics · Economics 2022-02-15 Shih-Tang Su , Vijay G. Subramanian

I study dynamic contracting where Sender privately observes a Markovian state and seeks to motivate Receiver, who acts. Sender provides incentives in two ways: payments, which alter payoffs ex-post, and (Bayesian) persuasion, which shapes…

Theoretical Economics · Economics 2026-05-22 Daniel Luo

Albrecht and Stone (2018) state that modeling of changing behaviors remains an open problem "due to the essentially unconstrained nature of what other agents may do". In this work we evaluate the adaptability of neural artificial agents…

Computation and Language · Computer Science 2024-02-08 Philipp Sadler , Sherzod Hakimov , David Schlangen

We study the problem of online learning in a two-player decentralized cooperative Stackelberg game. In each round, the leader first takes an action, followed by the follower who takes their action after observing the leader's move. The goal…

Machine Learning · Computer Science 2023-04-13 Geng Zhao , Banghua Zhu , Jiantao Jiao , Michael I. Jordan

In this paper, we study the problem of deceptive reinforcement learning to preserve the privacy of a reward function. Reinforcement learning is the problem of finding a behaviour policy based on rewards received from exploratory behaviour.…

Machine Learning · Computer Science 2021-02-08 Zhengshang Liu , Yue Yang , Tim Miller , Peta Masters

Herding, where investors imitate others' decisions rather than relying on their own analysis, is a prevalent phenomenon in financial markets. Excessive herding distorts rational decisions, amplifies volatility, and can be exploited by…

Mathematical Finance · Quantitative Finance 2026-04-14 Huisheng Wang , H. Vicky Zhao

Game theory has been increasingly applied in settings where the game is not known outright, but has to be estimated by sampling. For example, meta-games that arise in multi-agent evaluation can only be accessed by running a succession of…

Multiagent Systems · Computer Science 2021-01-25 Tabish Rashid , Cheng Zhang , Kamil Ciosek

This paper studies the optimal mechanism to motivate effort in a dynamic principal-agent model without transfers. An agent is engaged in a task with uncertain future rewards and can quit at any time. The principal knows the reward and…

Theoretical Economics · Economics 2026-01-16 Chang Liu

According to the standard imitation protocol, a less successful player adopts the strategy of the more successful one faithfully for future success. This is the cornerstone of evolutionary game theory that explores the vitality of competing…

Physics and Society · Physics 2019-09-27 Attila Szolnoki , Xiaojie Chen

Peer prediction mechanisms incentivize agents to truthfully report their signals even in the absence of verification by comparing agents' reports with those of their peers. In the detail-free multi-task setting, agents respond to multiple…

Computer Science and Game Theory · Computer Science 2021-08-27 Grant Schoenebeck , Fang-Yi Yu

We analyze an extended model of the Iterated Prisoner's Dilemma where agents decide to play based on the data from their limited memory or recommendations. The cooperators can decide whether to play with the matched opponent or not. The…

Computer Science and Game Theory · Computer Science 2022-09-15 Orhun Gorkem , Haluk O. Bingol

In some agent designs like inverse reinforcement learning an agent needs to learn its own reward function. Learning the reward function and optimising for it are typically two different processes, usually performed at different stages. We…

Artificial Intelligence · Computer Science 2020-04-29 Stuart Armstrong , Jan Leike , Laurent Orseau , Shane Legg

Although the data-driven analysis of football players' performance has been developed for years, most research only focuses on the on-ball event including shots and passes, while the off-ball movement remains a little-explored area in this…

Machine Learning · Computer Science 2023-09-06 Yisheng Pei , Varuna De Silva , Mike Caine

In this paper, we present a novel, game-theoretic model of deception in two-player, zero-sum games. Our framework leverages an information asymmetry: one player (the deceiver) has access to accurate payoff information, while the other (the…

Computer Science and Game Theory · Computer Science 2025-09-25 Brendan Gould , Kyriakos Vamvoudakis

Dynamic Stackelberg games are a broad class of two-player games in which the leader acts first, and the follower chooses a response strategy to the leader's strategy. Unfortunately, only stylized Stackelberg games are explicitly solvable…

Optimization and Control · Mathematics 2024-11-15 Guillermo Alvarez , Ibrahim Ekren , Anastasis Kratsios , Xuwei Yang

In settings with incomplete information, players can find it difficult to coordinate to find states with good social welfare. For example, in financial settings, if a collection of financial firms have limited information about each other's…

Computer Science and Game Theory · Computer Science 2014-09-01 Avrim Blum , Jamie Morgenstern , Ankit Sharma , Adam Smith

Evolutionary game theory has been an important tool for describing economic and social behaviour for decades. Approximate mean value equations describing the time evolution of strategy concentrations can be derived from the players'…

Populations and Evolution · Quantitative Biology 2011-02-10 Mathis Antony , Degang Wu , K Y Szeto

A network model based on players' aspirations is proposed and analyzed theoretically and numerically within the framework of evolutionary game theory. In this model, players decide whether to cooperate or defect by comparing their payoffs…

Physics and Society · Physics 2025-02-06 M. Aguilar-Janita , N. Khalil , I. Leyva , I. Sendiña-Nadal