works on

From the 1 of 17 linked papers with an AI index.

activity
20242026
collaborators

17 papers

cs.GT2026

Paradoxes of Game Theoretic Equilibria and Price of Anarchy

Georgios Piliouras, Ian Gemp, Siqi Liu +1

The paper shows that static equilibrium concepts like Nash and correlated equilibria can hide unstable dynamics in multi‑agent learning, leading to unbounded or chaotic inefficienc…

cs.GT2026

Nash without Numbers: A Social Choice Approach to Mixed Equilibria in Context-Ordinal Games

Ian Gemp, Crystal Qian, Marc Lanctot +1

Nash equilibrium serves as a fundamental mathematical tool in economics and game theory. However, it classically assumes knowledge of player utilities, whereas economics generally…

cs.AI2026

Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning

Zun Li, Marc Lanctot, Kevin R. McKee +7

Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents' strategies, and exploiting this opponent model by playing a best respo…

cs.MA2026

LLM-Mediated Guidance of MARL Systems

Philipp D. Siedler, Ian Gemp

In complex multi-agent environments, achieving efficient learning and desirable behaviours is a significant challenge for Multi-Agent Reinforcement Learning (MARL) systems. This wo…

cs.AI2026

Active Evaluation of General Agents: Problem Definition and Comparison of Baseline Algorithms

Marc Lanctot, Kate Larson, Ian Gemp +1

As intelligent agents become more generally-capable, i.e. able to master a wide variety of tasks, the complexity and cost of properly evaluating them rises significantly. Tasks tha…

cs.GT2026

Chaos in Autobidding Auctions

Ioannis Anagnostides, Ian Gemp, Georgios Piliouras +1

As autobidding systems increasingly dominate online advertising auctions, characterizing their long-term dynamical behavior is brought to the fore. In this paper, we examine the dy…