论文标题
用神经网络代表随机的实用程序选择模型
Representing Random Utility Choice Models with Neural Networks
论文作者
论文摘要
在深度学习的成功中，我们提出了一类基于神经网络的离散选择模型，称为Runnets，灵感来自随机效用最大化（RUM）框架。该模型使用样品平均近似值制定了代理的随机效用函数。我们表明，Runmets急剧近似于朗姆酒离散选择模型的类别：从随机效用最大化中得出的任何模型都具有选择概率，可以通过Rumnet任意密切近似。相互地，任何runmet都与朗姆酒原则一致。我们在选择数据上拟合的Rumnet的概括错误中得出了一个上限，并根据数据集和体系结构的关键参数预测新的，看不见的数据的选择能力。通过利用开源库作为神经网络，我们发现，从两个真实世界数据集上的预测准确性方面，Rumnet与多种选择建模和机器学习方法具有竞争力。
Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, inspired by the random utility maximization (RUM) framework. This model formulates the agents' random utility function using a sample average approximation. We show that RUMnets sharply approximate the class of RUM discrete choice models: any model derived from random utility maximization has choice probabilities that can be approximated arbitrarily closely by a RUMnet. Reciprocally, any RUMnet is consistent with the RUM principle. We derive an upper bound on the generalization error of RUMnets fitted on choice data, and gain theoretical insights on their ability to predict choices on new, unseen data depending on critical parameters of the dataset and architecture. By leveraging open-source libraries for neural networks, we find that RUMnets are competitive against several choice modeling and machine learning methods in terms of predictive accuracy on two real-world datasets.
