Personalized Decision Modeling: Utility Optimization or Textualized-Symbolic Reasoning
Yibo Zhao, Yang Zhao, Hongru Du, Hao Frank Yang
Individual decision-making, particularly in high-stakes scenarios like vaccine uptake, often diverges from predictions by population-optimal. This gap arises from the uniqueness of the individual decision-making process, shaped by quantifiable attributes (e.g., cost, time) and unstructured influences (e.g., personal preferences and constraints).Building 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 this challenge.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 significantly degrades overall predictive performance. By organically integrating symbolic utility modeling and semantic adaptation, ATHENA provides a new scheme for modeling human-centric decisions.