Designing Graph Convolutional Neural Networks for Discrete Choice with Network Effects
面向具有网络效应的离散选择设计图卷积神经网络AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究离散选择，并以图卷积网络刻画同伴影响和网络效应。
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面向具有网络效应的离散选择设计图卷积神经网络
- 研究问题
- 如何在离散选择模型中容纳同伴和网络影响，同时提高预测能力，并保留用于行为解释及经济指标计算的模型结构？
- 方法与思路
- 作者设计图卷积神经网络，将个人、选项和同伴相关信息纳入离散选择，使用纽约通勤选择及美国县级选举数据开展比较。
- 材料陈述的贡献
- 摘要称，该架构相较标准离散选择模型预测更好，并具有解释优势；通勤应用还展示了出行时间节约价值等经济指标的估计。
- 阅读关注点
- 应核对网络的构建方式、同伴效应与共同因素的区分、经济指标的推导，以及类别不平衡下的预测比较与验证设计。
- 阅读边界
- 这是预印本，不能据此声称已同行评审或期刊接收。摘要未给出性能数值，也未说明同伴影响的因果识别条件或推断有效性。
原文摘要
We introduce a novel model architecture that incorporates network effects into discrete choice problems, achieving higher predictive performance than standard discrete choice models while offering greater interpretability than general-purpose flexible model classes. Econometric discrete choice models aid in studying individual decision-making, where agents select the option with the highest reward from a discrete set of alternatives. Intuitively, the utility an individual derives from a particular choice depends on their personal preferences and characteristics, the attributes of the alternative, and the value their peers assign to that alternative or their previous choices. However, most applications ignore peer influence, and models that do consider peer or network effects often lack the flexibility and predictive performance of recently developed approaches to discrete choice, such as deep learning. We propose a novel graph convolutional neural network architecture to model network effects in discrete choices, achieving higher predictive performance than standard discrete choice models while retaining the interpretability necessary for inference--a quality often lacking in general-purpose deep learning architectures. We evaluate our architecture using revealed commuting choice data, extended with travel times and trip costs for each travel mode for work-related trips in New York City, as well as 2016 U.S. election data aggregated by county, to test its performance on datasets with highly imbalanced classes. Given the interpretability of our models, we can estimate relevant economic metrics, such as the value of travel time savings in New York City. Finally, we compare the predictive performance and behavioral insights from our architecture to those derived from traditional discrete choice and general-purpose deep learning models.