Cooperative Game Theory

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Cooperative game theory models what groups can achieve together and how to allocate their gains among members. Researchers use Shapley values and related contribution measures for fair attribution, and core-based concepts to test whether any group would prefer to act alone; hedonic games and exit-and-join dynamics model how coalitions form and change. Methods include axiomatic analysis, complexity and approximation algorithms, and scalable estimation, with applications to data and feature attribution, multi-agent systems, and federated learning. Key challenges are computing values across many coalitions and reconciling fairness axioms with joint contributions, while welfare, stability, and individual incentives may point to different outcomes.

Latest papers

41 papers7 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits
Apr 9, 2026Ramakrishnan Krishnamurthy, Arpit Agarwal, Lakshminarayanan Subramanian +1Multi-Agent Bandits Recommender Systems
Courant Institute, New York University · Indian Institute of Technology Bombay · FAIR, Meta AI
Competition and Cooperation of LLM Agents in Games
Apr 1, 2026Jiayi Yao, Cong Chen, Baosen ZhangLLM Agents Multi-Agent LLM Systems
Department of Electrical and Computer Engineering, University of Washington, Seattle. · Thayer School of Engineering at Dartmouth College.
Visualizing Coalition Formation: From Hedonic Games to Image Segmentation
Mar 9, 2026Pedro Henrique de Paula França, Lucas Lopes Felipe, Daniel Sadoc MenaschéCoalition Formation Cooperative Game Theory
UFRJ
Fair feature attribution for multi-output prediction: a Shapley-based perspective
Feb 26, 2026Umberto Biccari, Alain Ibáñez de Opakua, José María Mato +3Feature Attribution Shapley Value Attribution
Stability Under Valuation Updates in Coalition Formation
Feb 24, 2026Fabian Frank, Matija Novaković, René RomenCoalition Formation Cooperative Game Theory
Technical University of Munich
Control in Hedonic Games
Feb 18, 2026Jiehua Chen, Jakob Guttmann, Merisa Mustajbašić +1Coalition Formation Game Theory
TU Wien Vienna, Austria
Fairness-Aware Performance Evaluation for Multi-Party Multi-Objective Optimization
Jan 30, 2026Zifan Zhao, Peilan Xu, Wenjian LuoMulti-Party Multi-Objective Optimization Cooperative Game Theory
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China · Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China
Who Gets the Reward & Who Gets the Blame? Evaluation-Aligned Training Signals for Multi-LLM Agents
Nov 11, 2025Chih-Hsuan, Yang, Tanwi Mallick +5Multi-Agent LLM Systems Cooperative Game Theory
Bella · Argonne National Laboratory · & University of Chicago
Equilibria in routing games with connected autonomous vehicles may not be strong, as exclusive clubs may form
Oct 14, 2025Anastasia Psarou, Natello Descormier, Rafał KucharskiNash Equilibrium Intelligent Transportation Systems
Jagiellonian University, Faculty of Mathematics and Informatics, Kraków, Poland · École polytechnique, Institut Polytechnique de Paris, Palaiseau, France
Non-Obvious Manipulability in Additively Separable and Fractional Hedonic Games
May 19, 2025Diodato Ferraioli, Maria Fomenko, Giovanna VarricchioCooperative Game Theory Mechanism Design
University of Salerno, Italy · University of Calabria, Italy
Neural Dynamic Data Valuation via Stochastic State-Adjoint Trajectories
Apr 30, 2024Zhangyong Liang, Ji Zhang, Huanhuan GaoCooperative Game Theory Game Theory
National Center for Applied Mathematics Tianjin University Tianjin 300072, China · School of Mathematics, Physics and Computing University of Southern Queensland Toowoomba, Queensland 4350, Australia · School of Mechanical and Aerospace Engineering Jilin University Changchun 130025, China

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Creator Incentives in Recommender Systems: A Cooperative Game-Theoretic Approach for Stable and Fair Collaboration in Multi-Agent Bandits

Ramakrishnan Krishnamurthy, Arpit Agarwal, Lakshminarayanan Subramanian, Maximilian Nickel

Courant Institute, New York University · Indian Institute of Technology Bombay · FAIR, Meta AI

Multi-Agent Bandits Recommender Systems Fairness in Recommender Systems Cooperative Game Theory Shapley Value Attribution

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User interactions in online recommendation platforms create interdependencies among content creators: feedback on one creator's content influences the system's learning and, in turn, the exposure of other creators' contents. To analyze incentives in such settings, we model collaboration as a multi-agent stochastic linear bandit problem with a transferable utility (TU) cooperative game formulation, where a coalition's value equals the negative sum of its members' cumulative regrets. We show that, for identical (homogenous) agents with fixed action sets, the induced TU game is convex under mild algorithmic conditions, implying a non-empty core that contains the Shapley value and ensures both stability and fairness. For heterogeneous agents, the game still admits a non-empty core, though convexity and Shapley value core-membership are no longer guaranteed. To address this, we propose a simple regret-based payout rule that satisfies three out of the four Shapley axioms and also lies in the core. Experiments on MovieLens-100k dataset illustrate when the empirical payout aligns with -- and diverges from -- the Shapley fairness across different settings and algorithms.