Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Large 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.
Recommended Citation
Ni, Yongxin; Xu, Tianqi; Li, Chunxiao; Zheng, Eric; and Gu, Bin, "Machine Behaviorism: Exploring the Behavioral Dynamics of Large Language Models in Decision Making" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 4.
https://aisel.aisnet.org/hicss-58/in/impacts/4
Machine Behaviorism: Exploring the Behavioral Dynamics of Large Language Models in Decision Making
Hilton Waikoloa Village, Hawaii
Large 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.
https://aisel.aisnet.org/hicss-58/in/impacts/4