Game Theory and Applications

Últimos artículos

• Mean field games as a tool for AI safety: a worked example from the July 2026 Hugging Face incident

  P. Jameson Graber · 2 de octubre de 2026

  One way to make AI systems safe is to shape what the system is: its objective and dispositions. We take a complementary route: treat the agents' characteristics as partly unknown and ask what structure of interaction ensures that bad collective outcomes are not equilibria. Mean field games suit this…

• Learning to Harvest Without Collapse in a Regenerative Commons: A Lagrangian Framework

  Jose Tupayachi, Xueping Li, Soham Das · 30 de septiembre de 2026

  The tragedy of the commons poses a multi-agent safety problem: reward-seeking agents can deplete a shared resource, and cooperation among its users does not itself specify how much must be preserved. We make preservation an explicit requirement by formulating a regenerative commons as a constrained …

• Interactive Distributionally Robust Multi-Agent Learning with General Function Approximation

  Debamita Ghosh, George K. Atia, Yue Wang · 29 de septiembre de 2026

  Model misspecification poses a fundamental challenge in multi-agent reinforcement learning, where transition uncertainty can be amplified by strategic interactions among agents. Distributionally robust Markov games (DRMGs) provide a principled framework for addressing such uncertainty, yet existing …

• Context-dependent agent evaluation with orthogonal equilibrium learning

  Haorui Ma, Zehua Zang, Jiangmeng Li, Yi Li, Fanjing Xu, Stefan Feuerriegel · 29 de septiembre de 2026

  Many applications require to evaluate agents under contextual information (e.g., a prompt, task, or user group). We study how to perform such context-dependent agent evaluation from offline feedback. Existing score-based models for this purpose (e.g., Bradley-Terry) impose a transitive preference or…

• A Decentralized Partially Observable Team Decision Methodology with Delayed Information Sharing

  Xiaoxing Ren, Thomas Parisini, Andreas A. Malikopoulos · 23 de septiembre de 2026

  We study decentralized partially observable team decision problems with low-rank latent dynamics and unknown system models. The proposed framework combines team-theoretic equivalence with low-rank model representations to address cooperative decision-making in partially observable Markov decision pr…

• A Horizon-Independent Regret Bound for Optimistic Hedge in General-Sum Games

  Junsoo Ha · 22 de septiembre de 2026

  Can simple learning rules keep their regret bounded in self-play? Recent work achieves constant regret bounds through modified regularization and higher-order prediction. Yet for Optimistic Hedge, arguably the most canonical method in games, the best known individual regret bound remains logarithmic…

• Algorithmic Collusion and the Complexity of Information-Value-Free Equilibria

  Ioannis Anagnostides, Weiqiang Zheng · 22 de septiembre de 2026

  A (coarse) correlated equilibrium (CE) is information-value-free (IVF) if a player can match the payoff obtained from recommendations by committing to a fixed action. Motivated by the problem of regulating algorithmic collusion, this refinement was introduced by Hartline, Wang, and Zhang [EC'26], wh…

• Mitigating Retaliatory Algorithmic Collusion in Repeated Games

  Karthik Sivachandran, Rohan Paleja · 18 de septiembre de 2026

  Reinforcement learning agents trained to maximize their own reward in repeated interactions can converge to supra-competitive outcomes resembling explicit collusion, without communication or shared design. Existing mitigation approaches are largely tied to specific economic settings, like two-sided …

• Efficient Nash Equilibrium Computation for Cybersecurity Games

  Michael Lanier, David Farmer, Yevgeniy Vorobeychik · 18 de septiembre de 2026

  Computing Nash equilibria of simulation-based cybersecurity games with policy-space response oracles (PSRO) is bottlenecked by payoff estimation: every payoff-matrix entry costs Monte-Carlo rollouts of a slow simulator, while policies and restricted-game solves are cheap. We introduce Regret-Weighte…

• Steering Equilibrium Selection in Regularized Self-Play via the Reference Policy

  Luis Leal · 18 de septiembre de 2026

  Regularized self-play -- the family behind DeepNash's Stratego play -- drives a two-player zero-sum policy to a Nash equilibrium by best-responding to a slowly moving, entropy-regularized reference policy $\rho$. When the game has a polytope of value-equivalent equilibria, the regularizer silently b…

• Decentralized Optimal Equilibrium Learning Over Dynamic Networks

  Seref Taha Kiremitci, Muhammed O. Sayin · 17 de septiembre de 2026

  This paper studies decentralized learning of socially optimal equilibria in finite normal-form games over dynamic communication networks. Each agent observes only its own realized payoffs, does not know the game a priori, and can communicate only with time-varying neighbors using low-bandwidth messa…

• Constant Swap Regret in General-Sum Games via Optimistic Transition Matrices

  Tung Mai · 16 de septiembre de 2026

  We give deterministic and uncoupled learning dynamics for finite multiplayer general-sum games under full-information feedback that achieve constant individual swap regret, independent of the horizon $T$. With $n$ players and at most $m$ actions each, the individual swap regret of every player is $O…

• Input-to-State Stability Framework for Fully Distributed Primal-Dual Dynamics for Quadratic GNEPs Without Multiplier Consensus

  Shao-An Yin · 9 de septiembre de 2026

  Generalized Nash Equilibrium Problems (GNEPs) often arise in multi-agent engineering applications that require distributed algorithms. Unlike traditional approaches that enforce consensus on multipliers, our method removes the need to share multipliers, reducing communication and improving privacy. …

• Robust PAC Learning of Concurrent Stochastic Games

  Angel Y. He, David Parker · 4 de septiembre de 2026

  We introduce the first Probably Approximately Correct (PAC) learning framework for general-sum concurrent stochastic games (CSGs) with transition uncertainty, while addressing the challenge of Nash equilibrium (NE) existence. Our algorithm maintains data-driven $L^1$ confidence sets over transition …

• Constant regret in general games via higher-order optimism

  Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos · 4 de septiembre de 2026

  We introduce an uncoupled learning algorithm which, when employed by all players of an arbitrary $N$-player normal form game with up to $K$ actions per player, guarantees $O(N^3\log^2 K)$ individual regret, uniformly over the horizon of play. The proposed algorithm - which we call higher-order optim…

• When Does Information Sharing Improve Decentralized Discovery? Aggregation, Independent Rescue, and Equilibrium Selection

  Yohei Nakajima · 3 de septiembre de 2026

  Information sharing can improve a pooled estimate while eliminating independent rescue actions. This paper separates those effects in exact finite discovery models. A centralized action-budget profile shows that equal one-person accuracy can coexist with different portfolio values. Under a registere…

• NashDreamer: Model-Based Reinforcement Learning for Zero-Sum Imperfect-Information Games

  Tom\'a\v{s} Hole\v{c}ek, Viliam Lis\'y · 2 de septiembre de 2026

  Model-based reinforcement learning (MBRL) has achieved remarkable results in single-agent domains, yet its extension to competitive imperfect information games (IIGs) remains underexplored. In multi-agent settings, opponent-induced non-stationarity complicates the learning process, and decentralized…

• Independent Reinforcement Learning in Discounted Markov Games

  Asrin Efe Yorulmaz, Ugur Aydin, Tamer Basar · 2 de septiembre de 2026

  In this work, we study radically uncoupled learning in discounted general-sum Markov games. Assuming ``$\mathsf{ETH}$ for $\mathsf{PPAD}$", we show that, for every fixed discount factor, there is no polynomial-time algorithm for computing inverse-polynomially accurate coarse correlated equilibria in…

• Individualized Algorithmic Advice as a Strategic Signal on Competitive Markets

  Tobias R. Rebholz, Maxwell Uphoff, Christian H. R. Bernges, Florian Scholten · 2 de septiembre de 2026

  As algorithms increasingly mediate competitive decision-making, their influence extends beyond individual outcomes to shaping strategic market dynamics. In our experiment, we examined how algorithmic advice affects human behavior in a classic economic game with a unique, non-collusive, and analytica…

• Reasoning or Rambling? Exploring the Effect of Thinking on Agent Persuasion

  Haodong Zhao, Jidong Li, Zhaomin Wu, Tianjie Ju, Zhuosheng Zhang, Bingsheng He, Gongshen Liu · 27 de agosto de 2026

  Understanding persuasion is critical for the safety and reliability of multi-agent systems built on large language models (LLMs). This paper studies persuasion dynamics by contrasting general LLMs with Large Reasoning Models (LRMs) that employ explicit ``thinking'' processes. Through large-scale exp…

• Policy Optimization and Statistical Inference for Online Contextual Matrix Games

  Liner Xiang, Yixin Wang, Hengrui Cai · 19 de agosto de 2026

  Online decision making often requires navigating a landscape shaped by both dynamic contexts and strategic interactions. In competitive pricing, for example, hotels must account for both dynamic contextual factors and rivals' strategic responses. Existing approaches address only part of this challen…

• Memory Is Communication: The Frontier Between Remembering and Signaling

  Yashar Talebirad, Eden Redman, Ali Parsaee, Osmar R. Zaiane · 19 de agosto de 2026

  A bounded agent may obtain information for a decision from its own past, from peers, or from both sources. Retaining task-relevant history can reduce later communication, while a peer message can supply what memory lacks. Under limits on both resources, how should an agent allocate its information b…

• The Open-Strategy Dictator Game: Cooperation Under Mutual Transparency

  Michael Glass · 18 de agosto de 2026

  We introduce the Open-Strategy Dictator Game (OSDG), a variant of the classic dictator game in which each player's strategy is a natural-language document visible to all participants. The dictator's decision, to SHARE or TAKE an endowment, may depend on the text of the recipient's strategy. A large …

• What preferences can - and cannot - predict in multi-agent online learning

  Omar Abbadi, Rida Laraki, Panayotis Mertikopoulos · 17 de agosto de 2026

  We examine the interplay between ordinal, preference-based solution concepts in games and the long-run behavior of game dynamics, asking in particular to what extent the combinatorial data of a game -- its preference graph -- determine the outcomes of no-regret learning dynamics -- such as follow-th…

• Decentralized Multi-Player Q-Learning in Episodic Markov Decision Processes with Information Asymmetry

  Larissa Xu, King Bi, William Chang · 14 de agosto de 2026

  We study decentralized multi-player reinforcement learning in episodic tabular Markov decision processes (MDPs) under three forms of information asymmetry: (A) unobserved actions with common rewards, (B) observed actions with independent rewards, and (C) unobserved actions with independent rewards. …