Developing Niche Persona for Personalized Decision-making through Symbolic Learning

Ecological niches illustrate the close relationship between the information organisms perceive and the behavioral strategies they employ. A similar interdependence arises in modeling human decisions across heterogeneous populations, which requires determining both which information is relevant and how that information should be used. The usefulness of a feature set depends on the decision model, while the model’s effectiveness depends on the information it receives. This paper captures this interdependence through the concept of decision niches. We propose Niche Persona from Observation (NPO), a theory-guided LLM-agent framework that jointly conducts feature selection and human decision modeling. NPO defines a decision niche as the alignment between three elements: the population being modeled, the information exposed to the model, and the behavioral theory used to structure reasoning. For each group, theory-guided decision agents learn choices under candidate behavioral assumptions, while selection agents refine the visible feature space using validation feedback and error patterns. We evaluate NPO on three real-world datasets covering job choice, marijuana behavior, and alcohol behavior. Across all LLM backbones, NPO achieves average relative F1 gains of 9.38%–10.24% over the strongest baseline for each dataset, while using up to 43.75% fewer features than the strongest LLM-based baseline. Beyond prediction, NPO uncovers task- and group-specific decision niches, such as the stronger role of structured trade-offs in job choice and distinct theory–feature alignments across health-related behaviors. With NPO, personalized decision agents achieve stronger prediction with less information, select group-specific behavioral theories, and lay the foundation for future decision companions. The source code will be fully released upon acceptance.