在对话环境中使用大语言模型预测有偏差的人类决策
Predicting Biased Human Decision-Making with Large Language Models in Conversational Settings
AI总结：
研究大语言模型能否预测对话环境中的偏差决策，通过预注册研究发现参与者有认知偏差及负载偏差交互，评估LLMs预测能力，结果表明GPT-4家族表现出色，有助于理解LLMs模拟人类决策及设计适应偏差的对话代理。
摘要：
我们研究大语言模型（LLMs）是否能预测对话环境中的偏差决策，以及其预测是否不仅能捕捉人类认知偏差，还能反映这些影响在认知负载下如何变化。研究发现参与者有框架效应和现状偏差，认知负载增加会增强偏差，LLMs预测在关键场景更准确，GPT-4家族表现最佳。
英文摘要：
We examine whether large language models (LLMs) can predict biased decision-making in conversational settings, and whether their predictions capture not only human cognitive biases but also how those effects change under cognitive load. In a pre-registered study (N = 1,648), participants completed six classic decision-making tasks via a chatbot with dialogues of varying complexity. Participants exhibited two well-documented cognitive biases: the Framing Effect and the Status Quo Bias. Increased dialogue complexity resulted in participants reporting higher mental demand. This increase in cognitive load selectively, but significantly, increased the effect of the biases, demonstrating the load-bias interaction. We then evaluated whether LLMs (GPT-4, GPT-5, and open-source models) could predict individual decisions given demographic information and prior dialogue. While results were mixed across choice problems, LLM predictions that incorporated dialogue context were significantly more accurate in several key scenarios. Importantly, their predictions reproduced the same bias patterns and load-bias interactions observed in humans. Across all models tested, the GPT-4 family consistently aligned with human behavior, outperforming GPT-5 and open-source models in both predictive accuracy and fidelity to human-like bias patterns. These findings advance our understanding of LLMs as tools for simulating human decision-making and inform the design of conversational agents that adapt to user biases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
