用GPT对话代理模拟群体人类选择行为和偏差
Emulating Aggregate Human Choice Behavior and Biases with GPT Conversational Agents
AI总结：
本研究利用GPT对话代理模拟人类决策偏差，通过对话实验揭示LLMs在个体层面预测和再现偏差的能力，为设计适应性AI系统提供重要启示。
摘要：
认知偏差常常影响人类决策。尽管大型语言模型（LLMs）已被证明能够再现众所周知的偏差，但更关键的问题是LLMs能否在个体层面预测偏差，并在情境因素（如认知负荷）与这些偏差相互作用时，模拟有偏见的人类行为动态。我们把三个已确立的决策场景改编成对话环境，并进行了一项人类实验（N=1100）。参与者与一个促进决策的聊天机器人互动，通过简单或复杂的对话进行决策。结果揭示了稳健的偏差。为了评估LLMs在类似互动条件下如何模拟人类决策，我们使用参与者的人口统计数据和对话记录，基于GPT-4和GPT-5的LLMs模拟这些条件。LLMs能够精确再现人类偏差。我们发现不同模型在对齐人类行为方面存在显著差异。这在设计和评估交互环境中的适应性、具有偏差意识的基于LLM的AI系统方面具有重要影响。
英文摘要：
Cognitive biases often shape human decisions. While large language models (LLMs) have been shown to reproduce well-known biases, a more critical question is whether LLMs can predict biases at the individual level and emulate the dynamics of biased human behavior when contextual factors, such as cognitive load, interact with these biases. We adapted three well-established decision scenarios into a conversational setting and conducted a human experiment (N=1100). Participants engaged with a chatbot that facilitates decision-making through simple or complex dialogues. Results revealed robust biases. To evaluate how LLMs emulate human decision-making under similar interactive conditions, we used participant demographics and dialogue transcripts to simulate these conditions with LLMs based on GPT-4 and GPT-5. The LLMs reproduced human biases with precision. We found notable differences between models in how they aligned human behavior. This has important implications for designing and evaluating adaptive, bias-aware LLM-based AI systems in interactive contexts.
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。
