发表机构
Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University, China; College of Management and Economics, Tianjin University, China; Faculty of Science and Engineering, University of Nottingham Ningbo China（复杂管理系统的计算与分析实验室，天津大学，中国; 管理与经济学院，天津大学，中国; 科学与工程学院，诺丁汉大学宁波校区，中国）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究探讨大语言模型能否在不明确前景理论参数时重现人类路径选择行为偏差，设计行为评估框架并比较，发现其能重现偏差及展现相关决策行为，为人类决策建模及相关研究提供可扩展替代方案。
AI 中文摘要
人类的选择行为，包括路径选择，表现出偏离完全理性假设的系统性行为偏差。累积前景理论（CPT）被广泛认为是刻画此类行为模式的有效框架。但其大规模应用，尤其在模拟和基于智能体的建模中，严重依赖于指定个体层面的CPT参数，这仍是主要瓶颈。传统方法通常依靠调查和控制实验来校准CPT参数，但这些方法难以推广，且常无法捕捉人类决策的全部多样性。为应对这一挑战，本文研究大语言模型（LLMs）能否在不明确指定前景理论参数的情况下重现人类在选择中的行为偏差。以路径选择为代表性场景，设计行为评估框架，系统比较LLM生成的决策与CPT预测的既定人类行为模式。实验结果表明，LLMs能够重现非理性的人类选择偏差，并在不确定性下展现出与前景理论效应一致的决策行为。这些发现表明，生成式人工智能模型可为人类决策过程建模提供可扩展的替代方案，并为下一代大规模基于智能体的模拟和人工智能驱动的行为研究提供有前景的基础。
英文摘要
Human choice behavior, including route choice, exhibits systematic behavioral biases that deviate from the assumptions of full rationality. Cumulative prospect theory (CPT) has been widely recognized as an effective framework for characterizing such behavioral patterns. However, its large-scale application, particularly in simulation and agent-based modeling, critically depends on specifying individual-level CPT parameters, which remain a major bottleneck. Conventional approaches typically rely on surveys and controlled experiments to calibrate CPT parameters, yet these methods are difficult to generalize and often fail to capture the full diversity of human decision-making. To address this challenge, this paper investigates whether large language models (LLMs) can reproduce human behavioral biases in choice-making without explicit specification of prospect-theoretic parameters. Using route choice as a representative scenario, we design a behavioral evaluation framework and systematically compare LLM-generated decisions with established human behavioral patterns predicted by CPT. Experimental results demonstrate that LLMs are capable of reproducing non-rational human choice biases and can exhibit decision behaviors consistent with prospect-theoretic effects under uncertainty. These findings suggest that generative AI models may provide a scalable alternative for modeling human decision processes and offer a promising foundation for next-generation large-scale agent-based simulation and AI-driven behavioral research.
