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English(EN) Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling

研究发现：大型语言模型可重现人类决策中的偏见

作者 PulseAugur 编辑部 · [3 个来源] ·

一篇新的研究论文探讨了大型语言模型（LLMs）在复制人类行为偏见方面的潜力，特别是在路线选择场景中。该研究提出了一个框架，用于评估LLMs在没有明确参数指定的情况下，如何表现出与累积前景理论（CPT）一致的决策行为。研究结果表明，LLMs可以作为一种可扩展的替代方案来模拟人类决策过程，为高级的基于代理的模拟和人工智能驱动的行为研究奠定基础。 AI

影响 大型语言模型可能提供一种可扩展的方法来模拟人类决策，从而推动基于代理的建模和行为研究。

排序理由 该集群包含一篇详细介绍研究结果的学术论文。

在 arXiv cs.AI 阅读 →

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研究发现：大型语言模型可重现人类决策中的偏见

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报道来源 [3]

  1. arXiv cs.AI TIER_1 English(EN) · Jiangtao Han, Shoufeng Ma, Shuxian Xu, Geng Li, Shuai Ling, Ning Jia, Zhengbing He ·

    使用大型语言模型复现路线选择中的人类偏见：迈向可扩展的行为建模

    arXiv:2607.11632v1 Announce Type: new Abstract: 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 ch…

  2. arXiv cs.AI TIER_1 English(EN) · Zhengbing He ·

    使用大型语言模型复现路线选择中的人类偏见：迈向可扩展的行为建模

    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, …

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    使用大型语言模型复现路线选择中的人类偏见：迈向可扩展的行为建模

    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, …

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