用大模型预测文本描述彩票的选择，发现GPT-4o比传统理论模型更准。
Predicting Human Choice Between Textually Described Lotteries
- 用微调的GPT-4o分析文本彩票描述，直接预测人选择
- GPT-4o在文本彩票任务上准确率超越融合行为理论的混合模型
- 适合研究人类决策、风险认知或大模型应用的读者
预测人类在风险与不确定性下的决策是认知科学、经济学和人工智能中的长期挑战。以往研究多聚焦于数值描述的彩票，但现实决策常依赖文本描述。本研究首次利用大规模数据集，对文本描述彩票的一次性二选一决策进行系统探索。我们评估了多种计算方法，包括微调大型语言模型（LLMs）、使用嵌入表示，以及整合风险决策的行为理论。结果表明，微调后的LLM（特别是GPT-4o）在性能上优于融合行为理论的混合模型，挑战了数值场景下的既有方法。该发现揭示了文本与数值信息在决策中影响机制的根本差异，凸显了建立新建模策略以弥合这一鸿沟的必要性。
原文摘要 · Abstract (English)
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 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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