Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
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 t
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- PossiblePossibly related (embedding) · 55%LAMDA-NeSy/ChinaTravel →
- PossiblePossibly related (embedding) · 51%Comparing the algorithmic fidelity of large language models in predicting human decision making: a case study of vaccination choice - Nature →
- LinkedLinked via arxiv author · 85%Jiangtao Han →
“Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling”
- LinkedLinked via arxiv author · 85%Shoufeng Ma →
“Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling”
- LinkedLinked via arxiv author · 85%Shuxian Xu →
“Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling”
- LinkedLinked via arxiv author · 85%Yungeng Liu →
“Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling”
- LinkedLinked via arxiv author · 85%Shuai Ling →
“Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling”
- LinkedLinked via arxiv author · 85%Ning Jia →
“Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling”