Bayesian Deep Learning for Discrete Choice
离散选择的贝叶斯深度学习AI 解读 ↓
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离散选择的贝叶斯深度学习
- 研究问题
- 如何在离散选择模型中兼顾深度学习的非线性表达能力与经济解释，使边际替代率等经济量能够获得稳定估计和不确定性量化？
- 方法与思路
- 作者设计可结合近似贝叶斯推断的网络架构，在数据有限时收缩至行为假说，并用随机梯度朗之万动力学进行模拟及交通选择应用。
- 材料陈述的贡献
- 摘要称，该架构旨在缓解小样本过拟合与参数不稳定；研究同时评估预测表现和边际替代率区间覆盖，并提供纽约与瑞士选择数据案例。
- 阅读关注点
- 应核对行为约束如何嵌入网络、少量数据下的收缩机制及先验设定；重点检查近似后验的可靠性和边际替代率区间的经验覆盖。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未报告具体性能与覆盖率，不能据此认定其预测和经济推断均优于传统模型。
原文摘要
Discrete choice models (DCMs) are used to analyze individual decision-making in contexts such as transportation choices, political elections, and consumer preferences. DCMs play a central role in applied econometrics by enabling inference on key economic variables, such as marginal rates of substitution, rather than focusing solely on predicting choices on new unlabeled data. However, while traditional DCMs offer high interpretability and support for point and interval estimation of economic quantities, these models often underperform in predictive tasks compared to deep learning (DL) models. Despite their predictive advantages, DL models remain largely underutilized in discrete choice due to concerns about their lack of interpretability, unstable parameter estimates, and the absence of established methods for uncertainty quantification. Here, we introduce a deep learning model architecture specifically designed to integrate with approximate Bayesian inference methods, such as Stochastic Gradient Langevin Dynamics (SGLD). Our proposed model collapses to behaviorally informed hypotheses when data is limited, mitigating overfitting and instability in underspecified settings while retaining the flexibility to capture complex nonlinear relationships when sufficient data is available. We demonstrate our approach using SGLD through a Monte Carlo simulation study, evaluating both predictive metrics--such as out-of-sample balanced accuracy--and inferential metrics--such as empirical coverage for marginal rates of substitution interval estimates. Additionally, we present results from two empirical case studies: one using revealed mode choice data in NYC, and the other based on the widely used Swiss train choice stated preference data.