预测影响，缺失分布：评估LLMs作为运营管理中的人类行为模拟器
Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management
AI总结：
本文评估LLMs在运营管理中模拟人类行为的能力，通过九个实验发现LLMs能复制假设效应，但分布不匹配问题显著，轻量策略可缓解此问题。
摘要：
大型语言模型（LLMs）在商业、经济和社会科学中被用来模拟人类行为，提供低成本补充实验室实验、实地研究和调查。本文评估LLMs在运营管理中复制人类行为的能力。使用九个已发表的行为-运营实验，我们从两个维度评估LLMs的表现：LLMs生成的数据是否复制原始假设检验结果，以及它们的完整响应分布是否与人类数据一致，通过Wasserstein距离测量。我们发现LLMs经常复制假设层面的效果，表明它们能够捕捉显著的决策偏差和行为规律。然而，它们的响应分布经常与人类数据偏离，即使对于强大的专有模型也是如此，分散不匹配起着重要作用。我们还检查了两种轻量级缓解策略：思维链提示和超参数调整。两者都能减少分布不匹配，适当调整有时可以让较小或开源模型达到或超越较大专有系统的水平。
英文摘要：
Large language models (LLMs) are increasingly used to simulate human behavior in business, economics, and the social sciences, offering a low-cost complement to laboratory experiments, field studies, and surveys. This paper evaluates how well LLMs replicate human behavior in operations management. Using nine published behavioral-operations experiments, we assess LLM performance along two dimensions: whether LLM-generated data reproduce the original hypothesis-test outcomes, and whether their full response distributions align with human data, measured by Wasserstein distance. We find that LLMs often replicate hypothesis-level effects, suggesting that they can capture salient decision biases and behavioral regularities. However, their response distributions frequently diverge from human data, even for strong proprietary models, with dispersion mismatch playing an important role. We also examine two lightweight mitigation strategies: chain-of-thought prompting and hyperparameter tuning. Both can reduce distributional misalignment, and appropriate tuning can sometimes allow smaller or open-source models to match or outperform larger proprietary systems.
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。
