This paper studies a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers, and develops a data-driven algorithm to learn parameters and op…

The paper proposes a unified framework that maps the geometry of games to effective solver dynamics, suggesting that solvability is governed by continuous structural properties rather than discrete cl…

The paper models cryptographic hybridization as a Stackelberg game where the defender optimizes algorithm selection against a resource-constrained attacker who performs conditional optimization.

The paper introduces Mindgames, a comprehensive multi-game arena for evaluating LLM agents' sustained social and strategic reasoning, demonstrating that current evaluations are limited by structural s…

The study extends cooperative bias testing across diverse, next-generation LLMs, finding that provider identity is a stronger predictor of cooperative equilibrium than model generation, and that noise…

This paper introduces Repeated Policy Regret (RP-Regret), a novel game-theoretic metric for analyzing regret in repeated games with adaptive opponents, and proposes algorithms to minimize it.

This paper analyzes the computational complexity of evaluating recurrent functions, showing that the complexity depends heavily on how the input offsets are encoded and the structure of the recurrence…

The paper proposes D-BOS, a novel differentiable method that shapes opponent behavior by directly manipulating the opponent's inferred belief state, outperforming existing techniques in multi-agent ga…

The paper proposes DNQ, a scalable solver-in-the-loop framework for training agents in multi-turn simultaneous bidding games by leveraging pairwise payoff estimation to approximate complex equilibrium…

The paper introduces Global PSRO, a novel deep reinforcement learning framework that efficiently approximates Nash equilibria in large two-player zero-sum games by intelligently expanding the strategy…

The paper simulates bargaining scenarios using LLM agents to analyze how optimizing agents for financial profit affects their honesty and trust, finding that while fine-tuning improves deal-making, it…

The paper introduces PTCG-Bench, a new benchmark using the Pokémon TCG to evaluate LLM agents' strategic decision-making and ability to self-evolve, finding that sustained self-evolution remains chall…

The paper establishes that finding approximate Hylland-Zeckhauser equilibria (a type of market allocation) is computationally hard, specifically showing it is PPAD-hard under certain complexity assump…

The paper introduces Safe Equilibrium Policy Optimization (σepo{}) to train language models for multi-agent strategic tasks, achieving improved safety and robustness across various game domains.

This paper shows that standard optimal control in Markov Decision Processes (MDPs) with an absorbing catastrophic state naturally generates behavioral signatures mimicking prospect theory, even withou…

The paper introduces Context-Dependent Argumentation Frameworks (CDAFs) to model how an agent strategically manipulates the success of arguments by choosing the external evaluation context.

This paper investigates the 'faithfulness gap' in LLM agents—the discrepancy between stated reasoning and actual action—by decomposing it into two opposing steps: reasoning-to-conclusion and conclusio…

This paper adapts LLM watermarking techniques, specifically the KGW watermark, to create detectable watermarks for AI game-playing strategies in perfect-information games, showing minimal impact on ga…

This paper introduces a welfare-centric framework for designing institutional incentives, showing that optimizing for total social welfare often requires different incentive levels than those optimize…

The paper introduces an outer-loop AI agent that autonomously redesigns LLM policy-synthesis pipelines for multi-agent social dilemmas, demonstrating that the optimal pipeline structure depends critic…