When Decision Theories Empower AI Agents: Learning and Orchestrating Theory–Agent Alignment for Personalized Decision-Making

Large language models (LLMs) hold great promise as personalized decision companions. However, using LLM agents for decision modeling also introduces new challenges: while LLMs offer richer and more flexible modeling capabilities, they often lack theoretical grounding, exhibit unstable reasoning behavior, and provide limited interpretability. This raises two central questions: how can AI agents be aligned with decision theories to support trustworthy reasoning, and how can the most appropriate theory-guided agents be selected or orchestrated for different tasks and personas? We address these questions through the lens of the wisdom of crowds: rather than searching for a single universal decision theory, we treat diverse decision theories as complementary behavioral experts. Building on this idea, we propose Learning to Agentic Orchestration (L2AO), a framework that orchestrates theory-guided AI agents for trustworthy and personalized decision modeling. L2AO consists of two key components: Theory-guided Agent Learning (TAL), which constructs and trains decision agents under different behavioral theories, and Consensus-First Expert Adaptation (CFEA), which coordinates learned agents through a democratic AI mechanism that prioritizes cross-theory consensus while adapting to task- and persona-specific differences. Experiments on five real-world survey-based decision datasets show that L2AO achieves state-of-the-art performance compared with utility-based, machine-learning, and LLM-based baselines, improving average accuracy by 15.0% over the strongest baseline. Beyond prediction, L2AO further reveals potential systematic biases that can arise when a single decision theory is embedded into an LLM agent to model diverse human decisions. The source code will be fully released upon paper acceptance.