英文标题：
《Deep Neural Networks for Choice Analysis: Extracting Complete Economic
  Information for Interpretation》
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作者：
Shenhao Wang, Qingyi Wang, Jinhua Zhao
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最新提交年份：
2021
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英文摘要：
  While deep neural networks (DNNs) have been increasingly applied to choice analysis showing high predictive power, it is unclear to what extent researchers can interpret economic information from DNNs. This paper demonstrates that DNNs can provide economic information as complete as classical discrete choice models (DCMs). The economic information includes choice predictions, choice probabilities, market shares, substitution patterns of alternatives, social welfare, probability derivatives, elasticities, marginal rates of substitution (MRS), and heterogeneous values of time (VOT). Unlike DCMs, DNNs can automatically learn the utility function and reveal behavioral patterns that are not prespecified by domain experts. However, the economic information obtained from DNNs can be unreliable because of the three challenges associated with the automatic learning capacity: high sensitivity to hyperparameters, model non-identification, and local irregularity. To demonstrate the strength and challenges of DNNs, we estimated the DNNs using a stated preference survey, extracted the full list of economic information from the DNNs, and compared them with those from the DCMs. We found that the economic information either aggregated over trainings or population is more reliable than the disaggregate information of the individual observations or trainings, and that even simple hyperparameter searching can significantly improve the reliability of the economic information extracted from the DNNs. Future studies should investigate other regularizations and DNN architectures, better optimization algorithms, and robust DNN training methods to address DNNs\' three challenges, to provide more reliable economic information from DNN-based choice models.
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中文摘要：
虽然深度神经网络（DNN）越来越多地应用于选择分析，显示出很高的预测能力，但尚不清楚研究人员能在多大程度上解释来自DNN的经济信息。本文证明了DNNs可以提供与经典离散选择模型（DCMs）一样完整的经济信息。经济信息包括选择预测、选择概率、市场份额、替代品替代模式、社会福利、概率导数、弹性、边际替代率（MRS）和异质时间值（VOT）。与DCMs不同，DNNs可以自动学习效用函数并揭示领域专家未预先指定的行为模式。然而，从DNNs获得的经济信息可能不可靠，因为与自动学习能力相关的三个挑战：对超参数的高度敏感性、模型不可识别性和局部不规则性。为了证明DNN的优势和挑战，我们使用规定的偏好调查对DNN进行了评估，从DNN中提取了完整的经济信息列表，并将其与DCMs中的经济信息进行了比较。我们发现，无论是在训练中还是在总体中聚合的经济信息都比个体观察或训练的分解信息更可靠，即使是简单的超参数搜索也可以显著提高从DNN中提取的经济信息的可靠性。未来的研究应研究其他正则化和DNN体系结构、更好的优化算法和稳健的DNN训练方法，以应对DNN的三大挑战，从而从基于DNN的选择模型中提供更可靠的经济信息。
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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：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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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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