Machine Behaviorism: Exploring the Behavioral Dynamics of Large Language Models in Decision Making

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dc.contributor.authorNi, Yongxin
dc.contributor.authorXu, Tianqi
dc.contributor.authorLi, Chunxiao
dc.contributor.authorZheng, Eric
dc.contributor.authorGu, Bin
dc.date.accessioned2024-12-26T21:07:56Z
dc.date.available2024-12-26T21:07:56Z
dc.date.issued2025-01-07
dc.description.abstractLarge Language Models (LLMs) have empowered AI agents to autonomously make decisions on behalf of humans, exhibiting humanoid behavior. However, the intricate dynamics driving such behavior remain largely unknown, making it crucial to understand and guide the behavior of these LLM agents, especially in high-stakes decision environments. This paper proposes a new concept of Machine Behaviorism, unlocking the potential of LLM-based agents by providing a systematic framework to reshape the agent behavior. By tracking these agents’ actions under different decision environments, we identify AI behavioral biases when delegated with high-stakes decisions, investigate the origins of such behaviors, and propose corrective interventions accordingly. Specially, using a typical representational behavioral bias commonly found in human investors, we provide a detailed demonstration of machine behaviorism in the financial trading environment. Finally, we advocate for the establishment of machine behaviorism as an emerging discipline, as AI agents increasingly undertake autonomous decision-making and become more integrated into our daily lives.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2025.503
dc.identifier.isbn978-0-9981331-8-8
dc.identifier.other9d1c6461-1ad2-4f0c-948d-8d132726318d
dc.identifier.urihttps://hdl.handle.net/10125/109348
dc.relation.ispartofProceedings of the 58th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectEconomic and Societal Impacts of Technology, Data, and Algorithms
dc.subjectai agents, human-like bias, large language models, machine behaviorism
dc.titleMachine Behaviorism: Exploring the Behavioral Dynamics of Large Language Models in Decision Making
dc.typeConference Paper
dc.type.dcmiText
prism.startingpage4196

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