Title: Behavior Learning
Primary area: interpretability and explainable AI
Keywords: Utility Maximization, Intrinsic Interpretability, Identifiability, Performance–Interpretability Trade-off, Statistical Consistency, Counterfactual Prediction, Energy-Based Models (EBMs)

Abstract: Interpretable machine learning is increasingly vital for scientific research, yet the performance–interpretability trade-off, tool-centric designs, and non-identifiability undermine its scientific credibility. Grounded in behavioral science, we propose Behavior Learning (BL), a novel general-purpose interpretable machine learning framework that jointly achieves high predictive performance, intrinsic interpretability, and identifiability for scientifically credible modeling. BL learns interpretable latent optimization structures from data. It does so by parameterizing a compositional utility function built from intrinsically interpretable modular blocks, which induces a data distribution for prediction and generation. Each block represents and can be written in symbolic form as a utility maximization problem (UMP), a foundational paradigm in behavioral science and a universal framework of optimization. BL supports architectures ranging from a single UMP to hierarchical compositions, offering both expressiveness and structural transparency. Its smooth and monotone variant (IBL) guarantees identifiability under mild conditions. Theoretically, we establish the universal approximation property of both BL and IBL, and analyze the M-estimation properties of IBL. Empirically, BL demonstrates strong predictive performance, intrinsic interpretability, effectiveness in counterfactual prediction, and scalability to high-dimensional data.

TL;DR: Grounded in behavioral science, we propose Behavior Learning (BL), a general-purpose interpretable ML framework that jointly achieves high predictive performance, intrinsic interpretability, and identifiability for scientifically credible modeling.