60 papers
This paper proposes a distributed non-cooperative game-theoretic framework for jointly optimizing linear MIMO transceivers to resolve semantic mismatches and mitigate multi-user interference in goal-oriented communication systems, deriving a closed-form Nash Equilibrium that balances information compression, interference management, and task performance.
This paper introduces temporally sensitive Alternation (ALT) metrics to reveal that conventional outcome-based evaluations can severely mischaracterize multi-agent coordination, as demonstrated by Q-learning agents in a Battle of the Exes variant that achieve high traditional fairness scores but perform significantly worse than random baselines in actual turn-taking dynamics.
This paper demonstrates that Large Language Model-based pricing agents in oligopoly and auction settings can autonomously achieve supracompetitive prices and profits, a behavior significantly influenced by prompt variations and driven by price-war concerns, thereby posing unique challenges for future AI regulation.
This paper introduces an algorithm capable of computing all evolutionarily stable strategies within nondegenerate normal-form games involving three or more players.
This paper proposes a novel three-level Stackelberg game framework that incorporates heterogeneous leaders and non-follower agents to accurately model congestion-coupled strategic interactions, demonstrating that ignoring non-participating agents leads to distorted equilibrium predictions in systems like EV charging markets.
This paper proposes a distributed auction framework for repeated spectrum allocation in heterogeneous networks where user equipments utilize large language models as reasoning agents to strategically adapt their bidding and association decisions over time, resulting in superior channel access and budget efficiency compared to traditional static policies.
This paper proposes a regularized online RLHF framework using Generalized Bilinear Preference Models to identify Nash Equilibria, establishing the first statistically efficient, dimension-free regret bounds for high-dimensional settings through two simple algorithms that leverage strong convexity and low-rank structures.
This paper establishes that a moderator can learn agents' utility functions in unknown multi-agent games through recommendation feedback, demonstrating that quantal-response models enable precise identification with logarithmic sample complexity while best-response models yield a broader identifiable set, and further providing a low-regret online algorithm for both scenarios.
This paper refutes the conjecture that sink equilibria are in one-to-one correspondence with the attractors of the replicator dynamic by presenting counterexamples based on "local sources," while establishing "pseudoconvexity" as a sufficient condition for this correspondence to hold in two-player games.
This paper reviews the technical details and key results of using deep learning to design mechanisms that approximately satisfy conflicting properties like incentive compatibility and welfare maximization, demonstrating the approach's effectiveness through case studies in vehicular energy management, mobile network resource allocation, and agricultural procurement auctions.
This paper extends the theory of Monotone Comparative Statics to environments lacking a lattice structure by introducing the pseudo-lattice property, thereby enabling the analysis of mixed-strategy and trembling-hand perfect equilibria in multidimensional games.
This paper establishes the existence of two distinct equilibrium concepts for non-cooperative games under uncertainty—Nash equilibrium with capacity-based mixed strategies and Dow-Werlang equilibrium with non-additive beliefs—by utilizing max-plus integrals and fixed point theorems within an abstract convexity framework.
This paper proposes a framework that integrates linguistic framing with Bayesian signaling to optimize information design, leveraging Large Language Models as behavioral proxies to theoretically characterize tractability conditions and empirically demonstrate effective optimization through iterative prompt tuning.
The paper introduces -IPOMDP, a computational framework that equips model-based reinforcement learning agents with anomaly detection and out-of-belief policies to identify and deter deception from more sophisticated opponents, thereby mitigating exploitation in mixed-motive and zero-sum games.
This paper introduces an algebraic method, based on Eisenbud et al. (1977), for computing the index of completely mixed equilibria in finite games to demonstrate that any integer can be such an index, while showing that for monogenic equilibria the index is restricted to with non-zero values equivalent to payoff-robustness, and further extends these findings to extensive-form and boundary equilibria.
This paper introduces a branch-and-cut algorithm that reformulates mixed-integer Nash equilibrium problems as bilevel optimization tasks to either compute a pure Nash equilibrium or prove its non-existence, with specific adaptations and finite termination guarantees for both standard and generalized settings.
This paper introduces an axiomatic framework for allocation mechanisms in decentralized exchange markets that account for frictional transfer costs, characterizing robust linear mechanisms and "Robust Conditional Mean" mechanisms while linking them to literature on decentralized risk sharing.
This paper extends tracking guarantees for time-varying variational inequalities to non-monotone functions and periodic problems without sublinear solution paths, while also demonstrating that the associated discrete dynamical systems can exhibit either convergence or provably chaotic behavior.
This paper introduces Local Shapley, a framework that leverages model-induced locality to transform Shapley value computation from an exponential coalition enumeration problem into an efficient, optimal subset-centric process via the LSMR and LSMR-A algorithms, achieving significant speedups without sacrificing valuation accuracy.
This paper investigates fair allocation of indivisible goods with limited, cost-sensitive sharing by proving the existence of exact or approximate Maximin Share guarantees under specific conditions, introducing the Sharing Maximin Share (SMMS) fairness notion, and establishing both existence results and impossibility bounds for this extended framework.