game-theory

A 585-page open-access PDF textbook on game theory from arXiv, containing 165 solved exercises.

A blog post exploring how Nim with infinite ordinals always terminates, using the concept of well-foundedness and epsilon-zero ordinals.

The article discusses a consulting firm's argument that model collapse, human cognitive debt (skill atrophy), and competitive pressure form a self-reinforcing feedback loop in AI, and questions whether organizations can resist the race to automate.

This paper investigates how structured reasoning interventions affect the strategic economic reasoning of LLMs using Hotelling's linear city model, finding that intervention effectiveness depends on model architecture (GPT-4.1-mini vs. GPT-5-mini) with statistically significant crossover interactions.

This paper introduces a class of nonlinear axiomatic attribution methods for cooperative games to overcome the limitations of the linear Shapley value, which has an excessively large null space. Experimental results demonstrate the potential effectiveness of these methods in terms of inclusion AUC metric compared to Shapley value variants.

This paper proposes a unified approach to interpret knowledge distillation in LLMs using game-theoretic interactions, discovering that distillation sparsifies interactions, and introduces a loss function CIP to improve performance.

This paper investigates formal mechanisms, such as Mediation, to maintain market stability among self-interested LLM agents (DeepSeek-V3) in a simulated marketplace, finding that Mediation enables recovery even under sustained adversarial attacks.

Introduces G-Frame, a game theory-driven multi-agent framework that reduces hallucinations in lightweight LLMs by internalizing domain constraints, achieving a 79.46% reduction in hallucinations and performance parity with GPT-4o mini on chemistry benchmarks.

Elias Bareinboim announces his group's multiple ICML 2025 papers on causal AI, covering relational world models, robust offline RL, counterfactual identification, and causal game theory, highlighting the need for causal knowledge in AI reasoning.

This paper investigates learning useful policy representations (embeddings) in two-player zero-sum imperfect-information games, introducing methods for creating policy datasets, learning embeddings, and evaluating them on downstream tasks using Kuhn and Leduc Poker.

This paper introduces a contextual-bandit team game with two-sided informational asymmetry for runtime human oversight of AI agents, characterizing gaps between team-optimal and myopic human oversight strategies.

Recommendation of four free and open-access textbooks by Professor Giacomo Bonanno of UC Davis, covering game theory, decision making, uncertainty risk and information, and the economics of uncertainty and insurance, with accompanying videos and exercises.

This paper models takeover bidding as auction games with imperfect information and studies optimal due diligence strategies using game-theoretic solvers and reinforcement learning, finding that PPO and PPG are effective for large games, and providing a computable threshold for when additional diligence ceases to be beneficial.

This paper uses evolutionary game theory to model competition between a harm-minimizing AI agent and an approval-seeking (RLHF) agent in a community, analyzing conditions for adoption and welfare outcomes. The results show that while a self-audited agent can fixate, it is not sufficient to prevent community harm, and alignment and timeframe are critical.

This paper introduces an entropy-regularized reinforcement learning framework for zero-sum stochastic differential games in regime-switching jump-diffusion processes, deriving HJBI equations and an actor-critic algorithm with applications to investment games.

Introduces SidConArena, a benchmark framework for evaluating LLM agents in open-ended, positive-sum bargaining games, combining negotiation, production, and auctions to assess mixed-motive interaction and economic planning.

EMAgnet introduces parameter-space exponential moving average regularization for policy gradient self-play in large two-player zero-sum games, achieving lower exploitability compared to uniform regularization targets.

A 58-page paper from Google DeepMind on building agents specialized in game theory, highlighting key insights from the research.

This paper studies decentralized coalition formation as a dynamical process driven by unilateral exit-and-join decisions, using the Aumann-Dreze value for local payoff evaluation. It establishes equilibrium characterizations, Lyapunov and potential representations, and analyzes the impact of switching/acceptance costs on stability.

A blog post discussing how increased granularity in systems, such as tick sizes in financial markets and time slots for booking sports courts, can introduce strategic gaming and inefficiencies, arguing that finer choices are not always beneficial.