Comparing Risk Preferences and Loss Aversion in Humans and AI: A Persona-Based Approach with Fine-Tuning

Ryota Iwamoto, Takunori Ishihara and Takanori Ida

Discussion papers from Graduate School of Economics , Kyoto University

Abstract: This study empirically investigates the differences in risk preferences and loss aversion between humans and generative AI. We conduct a nationwide online survey of 4,838 individuals and generate AI responses under identical conditions by using personas constructed from demographic attributes. The results show that in gain domains, both humans and the AI select risk-averse options and exhibit similar preference patterns. However, in loss domains, AI shows a stronger risk-loving tendency and responds more sharply to individual attributes such as gender, age, and income. We retrain the AI by fine-tuning it based on human choice data. After fine-tuning, the AI’s preference distribution moves closer to that of humans, with loss-related decisions showing the greatest improvement. Using Wasserstein distance, we also confirm that fine-tuning reduces the behavioral gap between AI and humans.

Keywords: bias; bias; loss aversion; risk preference; generative AI; persona; fine-tuning; Wasserstein distance (search for similar items in EconPapers)
JEL-codes: C91 D91 (search for similar items in EconPapers)
Pages: 48
Date: 2025-07
New Economics Papers: this item is included in nep-ain, nep-cbe and nep-exp
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Persistent link: https://EconPapers.repec.org/RePEc:kue:epaper:e-25-006

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