Research on Multi-Agent Competition Based on Large Language Models

Research on Multi-Agent Competition Based on Large Language Models | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 12 October 2025 V1 Latest version Share on Research on Multi-Agent Competition Based on Large Language Models Authors : Jibin Yin 0000-0003-1278-671X , Jiang Mulin 0009-0006-8938-0899 , Hong Qiuhong 0009-0004-7018-2353 , and Zhang Xiangliang [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176025089.91562731/v1 271 views 190 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Large Language Models (LLMs), with their advanced capabilities in semantic understanding, dynamic strategy generation, and complex behavior simulation, represent a transformative tool for studying multi-agent interactions. Their exceptional performance in areas such as natural language processing and social behavior modeling enables the simulation of socio-economic phenomena through LLM-driven agents. However, existing research has primarily focused on agent collaboration, leaving a significant gap in the exploration of competition mechanisms an essential driver of social evolution and economic innovation. To address this theoretical gap, this paper introduces a bidirectional competition-cooperation framework, where service agents and experience agents are treated as equal participants in a game. A three-dimensional competitive space of ”resources-influence-information” is constructed. Leveraging LLM-driven strategy generators and reinforcement learning optimization mechanisms, service agents can dynamically adjust strategies such as pricing and menus, while experience agents influence market dynamics through behaviors such as social network dissemination and hidden information exploration. This research overcomes the unidirectional constraints of traditional models, highlighting the symbiotic evolution of competition-cooperation behaviors in digital societies. It also provides a computationally viable theoretical tool for applications such as platform economy governance and metaverse business simulation. Supplementary Material File (research on multi-agent competition.pdf) Download 6.37 MB Information & Authors Information Version history V1 Version 1 12 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords agent competition dynamic game theory large language models resource allocation social capital Authors Affiliations Jibin Yin 0000-0003-1278-671X Kunming University of Science and Technology View all articles by this author Jiang Mulin 0009-0006-8938-0899 Kunming University of Science and Technology View all articles by this author Hong Qiuhong 0009-0004-7018-2353 Kunming University of Science and Technology View all articles by this author Zhang Xiangliang [email protected] Zhejiang University View all articles by this author Metrics & Citations Metrics Article Usage 271 views 190 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jibin Yin, Jiang Mulin, Hong Qiuhong, et al. Research on Multi-Agent Competition Based on Large Language Models. Authorea . 12 October 2025. DOI: https://doi.org/10.22541/au.176025089.91562731/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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