Large Language Models Playing Mixed Strategy Nash Equilibrium Games

• URL: http://arxiv.org/abs/2406.10574v2
• Date: Sat, 12 Oct 2024 07:32:26 GMT
• Title: Large Language Models Playing Mixed Strategy Nash Equilibrium Games
• Authors: Alonso Silva,
• Abstract summary: This paper focuses on the capabilities of Large Language Models to find the Nash equilibrium in games with a mixed strategy Nash equilibrium and no pure strategy Nash equilibrium. The study reveals a significant enhancement in the performance of LLMs when they are equipped with the possibility to run code. It is evident that while LLMs exhibit remarkable proficiency in well-known standard games, their performance dwindles when faced with slight modifications of the same games.
• Score: 1.060608983034705
• License:
• Abstract: Generative artificial intelligence (Generative AI), and in particular Large Language Models (LLMs) have gained significant popularity among researchers and industrial communities, paving the way for integrating LLMs in different domains, such as robotics, telecom, and healthcare. In this paper, we study the intersection of game theory and generative artificial intelligence, focusing on the capabilities of LLMs to find the Nash equilibrium in games with a mixed strategy Nash equilibrium and no pure strategy Nash equilibrium (that we denote mixed strategy Nash equilibrium games). The study reveals a significant enhancement in the performance of LLMs when they are equipped with the possibility to run code and are provided with a specific prompt to incentivize them to do so. However, our research also highlights the limitations of LLMs when the randomization strategy of the game is not easy to deduce. It is evident that while LLMs exhibit remarkable proficiency in well-known standard games, their performance dwindles when faced with slight modifications of the same games. This paper aims to contribute to the growing body of knowledge on the intersection of game theory and generative artificial intelligence while providing valuable insights into LLMs strengths and weaknesses. It also underscores the need for further research to overcome the limitations of LLMs, particularly in dealing with even slightly more complex scenarios, to harness their full potential.

Related papers

• Evaluating Creativity and Deception in Large Language Models: A Simulation Framework for Multi-Agent Balderdash [6.65572931991284]
  Large Language Models (LLMs) have shown impressive capabilities in complex tasks and interactive environments. This paper introduces a simulation framework utilizing the game Balderdash to evaluate both the creativity and logical reasoning of LLMs.
  arXiv  Detail & Related papers  (2024-11-15T18:42:48Z)
• Game-theoretic LLM: Agent Workflow for Negotiation Games [30.83905391503607]
  This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts. We design multiple game-theoretic that guide the reasoning and decision-making processes of LLMs. The findings have implications for the development of more robust and strategically sound AI agents.
  arXiv  Detail & Related papers  (2024-11-08T22:02:22Z)
• WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents [55.64361927346957]
  We propose a neurosymbolic approach to learn rules gradient-free through large language models (LLMs) Our embodied LLM agent "WALL-E" is built upon model-predictive control (MPC) On open-world challenges in Minecraft and ALFWorld, WALL-E achieves higher success rates than existing methods.
  arXiv  Detail & Related papers  (2024-10-09T23:37:36Z)
• Evaluating and Enhancing LLMs Agent based on Theory of Mind in Guandan: A Multi-Player Cooperative Game under Imperfect Information [36.11862095329315]
  Large language models (LLMs) have shown success in handling simple games with imperfect information. This study investigates the applicability of knowledge acquired by open-source and API-based LLMs to sophisticated text-based games.
  arXiv  Detail & Related papers  (2024-08-05T15:36:46Z)
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing [56.75702900542643]
  We introduce AlphaLLM for the self-improvements of Large Language Models. It integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop. Our experimental results show that AlphaLLM significantly enhances the performance of LLMs without additional annotations.
  arXiv  Detail & Related papers  (2024-04-18T15:21:34Z)
• GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations [87.99872683336395]
  Large Language Models (LLMs) are integrated into critical real-world applications. This paper evaluates LLMs' reasoning abilities in competitive environments. We first propose GTBench, a language-driven environment composing 10 widely recognized tasks.
  arXiv  Detail & Related papers  (2024-02-19T18:23:36Z)
• Rethinking Interpretability in the Era of Large Language Models [76.1947554386879]
  Large language models (LLMs) have demonstrated remarkable capabilities across a wide array of tasks. The capability to explain in natural language allows LLMs to expand the scale and complexity of patterns that can be given to a human. These new capabilities raise new challenges, such as hallucinated explanations and immense computational costs.
  arXiv  Detail & Related papers  (2024-01-30T17:38:54Z)
• ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents [77.34720446306419]
  Alympics is a systematic simulation framework utilizing Large Language Model (LLM) agents for game theory research. Alympics creates a versatile platform for studying complex game theory problems.
  arXiv  Detail & Related papers  (2023-11-06T16:03:46Z)
• Leveraging Word Guessing Games to Assess the Intelligence of Large Language Models [105.39236338147715]
  The paper is inspired by the popular language game Who is Spy'' We develop DEEP to evaluate LLMs' expression and disguising abilities. We then introduce SpyGame, an interactive multi-agent framework.
  arXiv  Detail & Related papers  (2023-10-31T14:37:42Z)
• Deception Abilities Emerged in Large Language Models [0.0]
  Large language models (LLMs) are currently at the forefront of intertwining artificial intelligence (AI) systems with human communication and everyday life. This study reveals that such strategies emerged in state-of-the-art LLMs, such as GPT-4, but were non-existent in earlier LLMs. We conduct a series of experiments showing that state-of-the-art LLMs are able to understand and induce false beliefs in other agents.
  arXiv  Detail & Related papers  (2023-07-31T09:27:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.