Inspired by behavioral science, we propose Behavior Learning (BL), a novel general-purpose machine learning framework that learns interpretable and identifiable optimization structures from data, ranging from single optimization problems to hierarchical compositions. It unifies predictive performance, intrinsic interpretability, and identifiability, with broad applicability to scientific domains involving optimization. BL parameterizes 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, the latter modeling hierarchical optimization structures. Its smooth and monotone variant (IBL) guarantees identifiability. Theoretically, we establish the universal approximation property of BL, and analyze the M-estimation properties of IBL. Empirically, BL demonstrates strong predictive performance, intrinsic interpretability and scalability to high-dimensional data. Code: https://github.com/MoonYLiang/Behavior-Learning ; install via pip install blnetwork.

翻译：受行为科学启发，我们提出行为学习（BL）——一种新颖的通用机器学习框架，能够从数据中学习可解释且可识别的优化结构，涵盖从单一优化问题到层次化组合的广泛范畴。该框架统一了预测性能、内在可解释性与可识别性，对涉及优化的科学领域具有广泛适用性。BL通过内在可解释的模块化单元参数化构建组合效用函数，该函数诱导出用于预测和生成的数据分布。每个模块均可表示并能够以符号形式写成效用最大化问题（UMP）——这是行为科学的基础范式，也是优化的通用框架。BL支持从单一UMP到层次化组合的多种架构，后者可建模层次化优化结构。其平滑单调变体（IBL）保证了可识别性。理论上，我们建立了BL的通用逼近性质，并分析了IBL的M估计特性。实验表明，BL在预测性能、内在可解释性及对高维数据的可扩展性方面均表现出色。代码：https://github.com/MoonYLiang/Behavior-Learning ；可通过 pip install blnetwork 安装。