{"title":"基于 LLM 的社会模拟增强代理建模:挑战与机遇","authors":"Önder Gürcan","doi":"arxiv-2409.00100","DOIUrl":null,"url":null,"abstract":"As large language models (LLMs) continue to make significant strides, their\nbetter integration into agent-based simulations offers a transformational\npotential for understanding complex social systems. However, such integration\nis not trivial and poses numerous challenges. Based on this observation, in\nthis paper, we explore architectures and methods to systematically develop\nLLM-augmented social simulations and discuss potential research directions in\nthis field. We conclude that integrating LLMs with agent-based simulations\noffers a powerful toolset for researchers and scientists, allowing for more\nnuanced, realistic, and comprehensive models of complex systems and human\nbehaviours.","PeriodicalId":501043,"journal":{"name":"arXiv - PHYS - Physics and Society","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Modelisation a base d'Agent Augmentes par LLM pour les Simulations Sociales: Defis et Opportunites\",\"authors\":\"Önder Gürcan\",\"doi\":\"arxiv-2409.00100\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As large language models (LLMs) continue to make significant strides, their\\nbetter integration into agent-based simulations offers a transformational\\npotential for understanding complex social systems. However, such integration\\nis not trivial and poses numerous challenges. Based on this observation, in\\nthis paper, we explore architectures and methods to systematically develop\\nLLM-augmented social simulations and discuss potential research directions in\\nthis field. We conclude that integrating LLMs with agent-based simulations\\noffers a powerful toolset for researchers and scientists, allowing for more\\nnuanced, realistic, and comprehensive models of complex systems and human\\nbehaviours.\",\"PeriodicalId\":501043,\"journal\":{\"name\":\"arXiv - PHYS - Physics and Society\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Physics and Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.00100\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Physics and Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.00100","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modelisation a base d'Agent Augmentes par LLM pour les Simulations Sociales: Defis et Opportunites
As large language models (LLMs) continue to make significant strides, their
better integration into agent-based simulations offers a transformational
potential for understanding complex social systems. However, such integration
is not trivial and poses numerous challenges. Based on this observation, in
this paper, we explore architectures and methods to systematically develop
LLM-augmented social simulations and discuss potential research directions in
this field. We conclude that integrating LLMs with agent-based simulations
offers a powerful toolset for researchers and scientists, allowing for more
nuanced, realistic, and comprehensive models of complex systems and human
behaviours.