用可微编程实现无需先验知识的可解释人类行为建模
Fully Data-driven but Interpretable Human Behavioural Modelling with Differentiable Discrete Choice Model
- 通过可微编程自动学习可解释的效用函数
- 十秒内完成计算，小数据量下表现稳定
- 适合需要可解释干预策略的研究者
离散选择模型在人类行为建模中至关重要，但传统方法严重依赖领域专家知识，完全自动化且可解释地建模复杂人类行为仍是长期挑战。本文提出可微离散选择模型（Diff-DCM），一种完全数据驱动的方法，通过可微编程实现复杂人类行为的可解释建模、学习、预测与控制。仅凭输入特征和选择结果，无需任何先验知识，Diff-DCM即可估计出能复现观测行为的可解释闭式效用函数。在合成与真实世界数据上的全面实验表明，Diff-DCM可适用于多种数据类型，估算仅需少量计算资源，在无加速器的笔记本上可在数十秒内完成。同时利用其可微性，该模型可揭示人类行为的优化干预路径，为行为建模、预测与控制提供可靠自动化基础。
原文摘要 · Abstract (English)
Discrete choice models are essential for modelling various decision-making processes in human behaviour. However, the specification of these models has depended heavily on domain knowledge from experts, and the fully automated but interpretable modelling of complex human behaviours has been a long-standing challenge. In this paper, we introduce the differentiable discrete choice model (Diff-DCM), a fully data-driven method for the interpretable modelling, learning, prediction, and control of complex human behaviours, which is realised by differentiable programming. Solely from input features and choice outcomes without any prior knowledge, Diff-DCM can estimate interpretable closed-form utility functions that reproduce observed behaviours. Comprehensive experiments with both synthetic and real-world data demonstrate that Diff-DCM can be applied to various types of data and requires only a small amount of computational resources for the estimations, which can be completed within tens of seconds on a laptop without any accelerators. In these experiments, we also demonstrate that, using its differentiability, Diff-DCM can provide useful insights into human behaviours, such as an optimal intervention path for effective behavioural changes. This study provides a strong basis for the fully automated and reliable modelling, prediction, and control of human behaviours.
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