Paper Number
ECIS2025-1738
Paper Type
CRP
Abstract
We examine the depth of large language models’ (LLMs) potential to serve as tools for simulating human decision-making processes. Using GPT-4 Turbo in a sequential prisoner’s dilemma, we find significant discrepancies between LLM-generated and human behaviors: GPT consistently exhibits a higher propensity to cooperate and forms overly optimistic expectations of human cooperation, leading to outcomes that diverge from observed human decision-making patterns. Yet, GPT’s decisions do not appear random; they align with a formal model of human preferences for fairness and efficiency that explains the behavior of most human participants in similar settings.We find a striking contrast: while GPT may fall short as accurate surrogate for human decisions at the surface level, the model nonetheless exhibits a capacity to reflect the underlying preferences that drive human behavior in strategic contexts. Our findings have implications for LLM’s potential to serve as a managerial or research tool in simulating human behaviors.
Recommended Citation
Bauer, Kevin; Liebich, Lena; and Kosfeld, Michael, "CAN GPT MIMIC HUMAN PREFERENCES? AN EMPIRICAL AND STRUCTURAL INVESTIGATION" (2025). ECIS 2025 Proceedings. 4.
https://aisel.aisnet.org/ecis2025/ai_anthro/ai_anthro/4
CAN GPT MIMIC HUMAN PREFERENCES? AN EMPIRICAL AND STRUCTURAL INVESTIGATION
We examine the depth of large language models’ (LLMs) potential to serve as tools for simulating human decision-making processes. Using GPT-4 Turbo in a sequential prisoner’s dilemma, we find significant discrepancies between LLM-generated and human behaviors: GPT consistently exhibits a higher propensity to cooperate and forms overly optimistic expectations of human cooperation, leading to outcomes that diverge from observed human decision-making patterns. Yet, GPT’s decisions do not appear random; they align with a formal model of human preferences for fairness and efficiency that explains the behavior of most human participants in similar settings.We find a striking contrast: while GPT may fall short as accurate surrogate for human decisions at the surface level, the model nonetheless exhibits a capacity to reflect the underlying preferences that drive human behavior in strategic contexts. Our findings have implications for LLM’s potential to serve as a managerial or research tool in simulating human behaviors.
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