RUM-NN: A Neural Network Model Compatible with Random Utility Maximisation for Discrete Choice Setups
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RUM-NN：兼容随机效用最大化的离散选择神经网络
- 研究问题
- 如何在神经网络离散选择模型中保留随机效用最大化解释，同时容许非线性效用、不同误差分布及误差相关性？
- 方法与思路
- 作者构建线性与非线性 RUM-NN，将随机效用选择概率纳入网络估计，并用蒙特卡洛及两套交通选择数据进行评估。
- 材料陈述的贡献
- 摘要称线性效用与独立同分布 Gumbel 误差下可复现多项 Logit 表现，放宽这些限制后在两套应用数据上表现更好。
- 阅读关注点
- 应核对选择概率的计算、效用尺度归一化和参数识别，并检查误差相关结构、训练方法及预测比较是否采用一致评估设置。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未给出识别条件和性能差异数值，不能推断所有数据上均有优势。
原文摘要
This paper introduces a framework for capturing stochasticity of choice probabilities in neural networks, derived from and fully consistent with the Random Utility Maximization (RUM) theory, referred to as RUM-NN. Neural network models show remarkable performance compared with statistical models; however, they are often criticized for their lack of transparency and interoperability. The proposed RUM-NN is introduced in both linear and nonlinear structures. The linear RUM-NN retains the interpretability and identifiability of traditional econometric discrete choice models while using neural network-based estimation techniques. The nonlinear RUM-NN extends the model's flexibility and predictive capabilities to capture nonlinear relationships between variables within utility functions. Additionally, the RUM-NN allows for the implementation of various parametric distributions for unobserved error components in the utility function and captures correlations among error terms. The performance of RUM-NN in parameter recovery and prediction accuracy is rigorously evaluated using synthetic datasets through Monte Carlo experiments. Additionally, RUM-NN is evaluated on the Swissmetro and the London Passenger Mode Choice (LPMC) datasets with different sets of distribution assumptions for the error component. The results demonstrate that RUM-NN under a linear utility structure and IID Gumbel error terms can replicate the performance of the Multinomial Logit (MNL) model, but relaxing those constraints leads to superior performance for both Swissmetro and LPMC datasets. By introducing a novel estimation approach aligned with statistical theories, this study empowers econometricians to harness the advantages of neural network models.