How Different AI Chatbots Behave? Benchmarking Large Language Models in Behavioral Economics Games

• URL: http://arxiv.org/abs/2412.12362v1
• Date: Mon, 16 Dec 2024 21:25:45 GMT
• Title: How Different AI Chatbots Behave? Benchmarking Large Language Models in Behavioral Economics Games
• Authors: Yutong Xie, Yiyao Liu, Zhuang Ma, Lin Shi, Xiyuan Wang, Walter Yuan, Matthew O. Jackson, Qiaozhu Mei,
• Abstract summary: This paper presents a comprehensive analysis of five leading large language models (LLMs) as they navigate a series of behavioral economics games.<n>We aim to uncover and document both common and distinct behavioral patterns across a range of scenarios.<n>The findings provide valuable insights into the strategic preferences of each LLM, highlighting potential implications for their deployment in critical decision-making roles.
• Score: 20.129667072835773
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The deployment of large language models (LLMs) in diverse applications requires a thorough understanding of their decision-making strategies and behavioral patterns. As a supplement to a recent study on the behavioral Turing test, this paper presents a comprehensive analysis of five leading LLM-based chatbot families as they navigate a series of behavioral economics games. By benchmarking these AI chatbots, we aim to uncover and document both common and distinct behavioral patterns across a range of scenarios. The findings provide valuable insights into the strategic preferences of each LLM, highlighting potential implications for their deployment in critical decision-making roles.

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