英文标题：
《Deep Neural Networks for Choice Analysis: A Statistical Learning Theory
  Perspective》
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作者：
Shenhao Wang, Qingyi Wang, Nate Bailey, Jinhua Zhao
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最新提交年份：
2019
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英文摘要：
  While researchers increasingly use deep neural networks (DNN) to analyze individual choices, overfitting and interpretability issues remain as obstacles in theory and practice. By using statistical learning theory, this study presents a framework to examine the tradeoff between estimation and approximation errors, and between prediction and interpretation losses. It operationalizes the DNN interpretability in the choice analysis by formulating the metrics of interpretation loss as the difference between true and estimated choice probability functions. This study also uses the statistical learning theory to upper bound the estimation error of both prediction and interpretation losses in DNN, shedding light on why DNN does not have the overfitting issue. Three scenarios are then simulated to compare DNN to binary logit model (BNL). We found that DNN outperforms BNL in terms of both prediction and interpretation for most of the scenarios, and larger sample size unleashes the predictive power of DNN but not BNL. DNN is also used to analyze the choice of trip purposes and travel modes based on the National Household Travel Survey 2017 (NHTS2017) dataset. These experiments indicate that DNN can be used for choice analysis beyond the current practice of demand forecasting because it has the inherent utility interpretation, the flexibility of accommodating various information formats, and the power of automatically learning utility specification. DNN is both more predictive and interpretable than BNL unless the modelers have complete knowledge about the choice task, and the sample size is small. Overall, statistical learning theory can be a foundation for future studies in the non-asymptotic data regime or using high-dimensional statistical models in choice analysis, and the experiments show the feasibility and effectiveness of DNN for its wide applications to policy and behavioral analysis.
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中文摘要：
虽然研究人员越来越多地使用深层神经网络（DNN）来分析个人选择，但过度拟合和可解释性问题仍然是理论和实践中的障碍。通过使用统计学习理论，本研究提出了一个框架，以检查估计和近似误差之间的权衡，以及预测和解释损失之间的权衡。它通过将解释损失的度量公式化为真实选择概率函数和估计选择概率函数之间的差异，从而在选择分析中实现DNN的可解释性。本研究还利用统计学习理论对DNN中预测和解释损失的估计误差上界，揭示了DNN不存在过拟合问题的原因。然后对三种情况进行模拟，以比较DNN和二进制logit模型（BNL）。我们发现，对于大多数情景，DNN在预测和解释方面都优于BNL，并且更大的样本量释放了DNN的预测能力，而不是BNL。DNN还用于根据2017年全国家庭旅游调查（NHTS2017）数据集分析出行目的和出行方式的选择。这些实验表明，DNN可以用于当前需求预测实践之外的选择分析，因为它具有固有的效用解释、适应各种信息格式的灵活性以及自动学习效用规范的能力。DNN比BNL更具预测性和可解释性，除非建模人员对选择任务有完整的了解，并且样本量较小。总的来说，统计学习理论可以为未来在非渐近数据领域的研究或在选择分析中使用高维统计模型奠定基础，实验表明DNN在政策和行为分析中的广泛应用是可行和有效的。
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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