AI Decodes Why We Decide: LLMs Map Verbal Reasoning to Actions
Researchers combine fine-tuned large language models with formal choice mathematics to analyze thousands of participants’ free-text rationales for gambling decisions. The LLMs classify reasons (e.g., maximax vs. loss aversion) and are validated by comparing the text-derived motives with the actual choices, yielding about 95% alignment. The study shows that people’s decision strategies adapt to how a problem is framed, and presents a scalable framework for analyzing verbal reports that could inform public policy and complex real-world decision making.