Tag
A study on Large Language Models playing the game Diplomacy in multi-agent simulations reveals which models kept their promises when allowed to lie.
Game Arena is an open platform for evaluating large language models through competitive games like Chess, Poker, and Werewolf, enabling dynamic assessment of strategic planning and robustness.
This research paper explores using a reference policy to deliberately steer equilibrium selection in regularized self-play for two-player zero-sum games, providing experimental results and theoretical analysis on tractable games.
This paper proposes a hierarchical hybrid LLM-MARL architecture for agentic AI networking in low-altitude wireless networks, enabling coordinated coexistence of heterogeneous unmanned aerial systems by adapting to changing service requirements without retraining.
Aaron Levie responds to Dario Amodei's essay on AI pacing, discussing practical realities of industry self-regulation, political influences, and game theory in ensuring broad participation for AI safety.
Researchers use a mathematical model to show that introducing random variations in rewards enriches classic game-theory insights, allowing cooperation and defection to coexist in games like the prisoner's dilemma.
This paper investigates the conditions under which information sharing can improve decentralized discovery processes, focusing on concepts like aggregation, independent rescue, and equilibrium selection.
This paper proposes a fully distributed continuous-time algorithm for computing Generalized Nash Equilibria in multi-agent systems with shared constraints, eliminating the need for multiplier exchange to reduce communication overhead and enhance privacy, validated on a multi-robot placement task.
This study explores how LLMs behave in game theory scenarios requiring coordination, revealing that communication among agents can increase rebellion tendencies and that adversarial surveillance reduces participation.
Princeton University announces its Spring 2025 course on Economics and Computation, taught by Matt Weinberg and Mark Braverman, covering topics like game theory, auctions, mechanism design, and cryptocurrencies.
This paper presents a mechanism-design framework for verifying negotiation and allocation tasks in LLM agents using A2A/MCP protocols. It evaluates rational behavior across models, finding that mechanism-level incentive compatibility does not automatically transfer to LLM agents.
The author celebrates the release of their book 'Game Theory' in the UK, which is the English translation of their original work 'Die Tabelle lügt immer'.
Yohei Nakajima discusses a new paper testing whether LLMs can replace human subjects in behavioral experiments, finding that a GPT-4.1 persona panel passed coarse marginal checks but failed to provide precise treatment-response estimates, so human substitutability is not established.
This paper models the strategic interaction between content providers and generative search engines as a repeated Stackelberg game, showing how GEO can escalate into citation wars, and proposes a verifiable-content reward mechanism (VCR) to align incentives and achieve win-win outcomes.
This paper studies how LLM agents negotiate in a dynamic supply chain bargaining problem, benchmarking nine models from OpenAI, Google, and Alibaba against a Bayesian equilibrium and finding that capability, provider identity, and prompt design shape surplus creation and division.
This paper introduces Mixed-Strategy Decision Tree (MDT), a method that uses solver output to teach large language models equilibrium strategies in imperfect-information games, reducing distance to equilibrium by 52.6% across LLM configurations on No-Limit Texas Hold'em.
This paper introduces IFlowNets, extending Adversarial Flow Networks to incomplete information games, proving prior constraints invalid and showing comparable or better performance than existing methods in preliminary experiments.
A free open-access textbook on game theory, with 585 pages and 165 solved exercises, is promoted as a resource for economics, AI, computer science, and more.
An academic paper analyzing the concept of coordination failure, likely in multi-agent systems or economic contexts.
This paper introduces a mean-field privacy game framework for federated learning, enabling tractable Nash equilibrium analysis for arbitrarily many clients with heterogeneous privacy preferences and yielding a personalized privacy guarantee.