Predicting Human Choice Between Textually Described Lotteries

• URL: http://arxiv.org/abs/2503.14004v1
• Date: Tue, 18 Mar 2025 08:10:33 GMT
• Title: Predicting Human Choice Between Textually Described Lotteries
• Authors: Eyal Marantz, Ori Plonsky,
• Abstract summary: This study conducts the first large-scale exploration of human decision-making in such tasks.<n>We evaluate multiple computational approaches, including fine-tuning Large Language Models.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting human decision-making under risk and uncertainty is a long-standing challenge in cognitive science, economics, and AI. While prior research has focused on numerically described lotteries, real-world decisions often rely on textual descriptions. This study conducts the first large-scale exploration of human decision-making in such tasks using a large dataset of one-shot binary choices between textually described lotteries. We evaluate multiple computational approaches, including fine-tuning Large Language Models (LLMs), leveraging embeddings, and integrating behavioral theories of choice under risk. Our results show that fine-tuned LLMs, specifically RoBERTa and GPT-4o outperform hybrid models that incorporate behavioral theory, challenging established methods in numerical settings. These findings highlight fundamental differences in how textual and numerical information influence decision-making and underscore the need for new modeling strategies to bridge this gap.

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