arXiv 2511.021942025-11-04cs.AIcs.CLcs.CYcs.LG

个性化决策建模：效用优化或文本化符号推理

Personalized Decision Modeling: Utility Optimization or Textualized-Symbolic Reasoning

Department of Civil and Systems Engineering（土木与系统工程系）；Johns Hopkins University（约翰霍普金斯大学）；Department of Systems and Information Engineering（系统与信息工程系）；University of Virginia（弗吉尼亚大学）；Johns Hopkins Data Science and AI Institute（约翰霍普金斯大学数据科学与人工智能研究所）

Yibo Zhao, Yang Zhao, Hongru Du, Hao Frank Yang

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AI总结：

本文提出自适应文本符号以人为中心的推理框架ATHENA，通过结合LLM文本推理与效用理论，分群体级符号发现和个体级语义适应两阶段建模个性化决策，在出行和疫苗选择任务上F1分数较最强模型提升至少6.5%。

摘要：

针对个人的决策模型，特别是在疫苗接种等高风险场景下，往往偏离群体最优预测。这种差异源于个体决策过程的独特性，它受数值属性（如成本、时间）和语言影响（如个人偏好和约束）共同塑造。基于效用理论并利用大型语言模型（LLMs）的文本推理能力，本文提出了自适应文本符号以人为中心的推理框架（ATHENA）来解决最优信息整合问题。ATHENA独特地整合了两个阶段：首先，通过LLM增强的符号发现，发现稳健的群体级符号效用函数；其次，实施个体级语义适应，在最优效用的指导下创建个性化语义模板，以对个性化选择进行建模。在真实世界的出行方式和疫苗选择任务上验证表明，ATHENA始终优于基于效用的模型、机器学习模型和其他基于LLM的模型，与最强大的前沿模型相比，F1分数至少提升了6.5%。此外，消融研究证实ATHENA的两个阶段都是关键且互补的，因为移除任一阶段都会明显降低整体预测性能。通过有机整合符号效用建模和语义适应，ATHENA为以人为中心的决策建模提供了一种新方案。项目页面可在https://yibozh.github.io/Athena找到。

英文摘要：

Decision-making models for individuals, particularly in high-stakes scenarios like vaccine uptake, often diverge from population optimal predictions. This gap arises from the uniqueness of the individual decision-making process, shaped by numerical attributes (e.g., cost, time) and linguistic influences (e.g., personal preferences and constraints). Developing upon Utility Theory and leveraging the textual-reasoning capabilities of Large Language Models (LLMs), this paper proposes an Adaptive Textual-symbolic Human-centric Reasoning framework (ATHENA) to address the optimal information integration. ATHENA uniquely integrates two stages: First, it discovers robust, group-level symbolic utility functions via LLM-augmented symbolic discovery; Second, it implements individual-level semantic adaptation, creating personalized semantic templates guided by the optimal utility to model personalized choices. Validated on real-world travel mode and vaccine choice tasks, ATHENA consistently outperforms utility-based, machine learning, and other LLM-based models, lifting F1 score by at least 6.5% over the strongest cutting-edge models. Further, ablation studies confirm that both stages of ATHENA are critical and complementary, as removing either clearly degrades overall predictive performance. By organically integrating symbolic utility modeling and semantic adaptation, ATHENA provides a new scheme for modeling human-centric decisions. The project page can be found at https://yibozh.github.io/Athena.

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。