AI 中文总结
本研究分析四种机器学习模型在复杂离散选择任务中学习和预测五类选择规则的性能，发现半参数及非参数模型总体优于参数模型，孪生神经网络在能源政策偏好案例中表现最佳。
AI 中文摘要
离散选择建模是政策制定过程中用于偏好 elicitation 的常用工具，但通常采用参数模型。机器学习可通过数据驱动方法或学习个体偏好，拓展离散选择建模在政策导向偏好 elicitation 中的应用边界。然而，目前对机器学习方法在个体异质性下估计个体离散选择规则的能力知之甚少，尤其在偏好 elicitation 常面临的挑战背景下。本研究评估四种机器学习模型（多项逻辑回归、广义加性模型、孪生神经网络（TNN）、高斯过程）学习和预测行为与社会科学中重要的五种选择规则（线性强效用、单调强效用、理想点、词典半序、多属性线性弹道累加器）的能力。开展蒙特卡洛实验，评估当选择选项的属性数量、训练选择集数量、选择规则的确定性程度增加时的模型性能。模拟结果表明，半参数和非参数模型在所有选择规则和实验场景中总体优于参数模型；随着训练选择集数量增加，模型性能总体提升6%至96%，随选择规则确定性程度增加，提升0%至55%。还开展了使用真实能源政策偏好数据的案例研究，其中孪生神经网络（TNN）表现最佳，贝叶斯信息准则（BIC）为13.351。本研究证明了半参数和非参数模型在政策导向离散选择建模中的可行性与局限性，表明选择任务场景应驱动模型选择。
英文摘要
Discrete choice modeling is a common tool used for preference elicitation during policy-making, but this is typically done through parametric models. Machine learning can push the boundaries of discrete choice modeling for policy-based preference elicitation by adopting a data-driven approach or learning individual preferences. However, there is limited knowledge of how well machine learning methods can estimate individual discrete choice rules under individual heterogeneity, especially in the context of challenges often experienced during preference elicitation. This study evaluates four machine learning models (multinomial logistic regression, generalized additive model, twinned neural network, and Gaussian process) with respect to their capacity to learn and predict five choice rules that are important in the behavioral and social sciences (linear strong utility, monotonic strong utility, ideal point, lexicographic semiorder, and multiattribute linear ballistic accumulator). Monte Carlo experiments were performed to assess model performance when increasing a) the number of attributes in the choice alternatives, b) the number of training choice sets, and c) the choice rule's determinism. The simulation results demonstrated that semi-parametric and non-parametric models generally outperform parametric models across all choice rules and experimental contexts. Model performance also generally improves by 6% to 96% and 0% to 55%, respectively, with an increase in training choice sets and choice rule determinism. A case study using real energy policy preference data was also conducted, where TNN performed best with a BIC of 13.351. This work demonstrated the viability and limitations of semi-parametric and non-parametric models in the context of policy-centric discrete choice modeling and showed how the choice task context should drive model selection.
CommentsPublished in Decision Analytics Journal, Dec 16 2025
DOI：10.1016/j.dajour.2025.100668
