使用机器学习的态度和潜在阶级选择模型

IF 2.8 3区 经济学 Q1 ECONOMICS Journal of Choice Modelling Pub Date : 2023-10-10 DOI:10.1016/j.jocm.2023.100452
Lorena Torres Lahoz, Francisco Camara Pereira, Georges Sfeir, Ioanna Arkoudi, Mayara Moraes Monteiro, Carlos Lima Azevedo
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引用次数: 0

摘要

潜在类别选择模型(LCCM)是离散选择模型(DCM)的扩展,它通过基于偏好相似性的假设对人群进行分割来捕捉选择过程中未观察到的异质性。我们提出了一种有效地将态度指标纳入LCCM规范的方法,通过引入人工神经网络(ANN)来制定潜在变量结构。鉴于机器学习(ML)在捕捉未观察到的复杂行为特征(如态度和信念)方面的灵活性和能力,该公式在探索态度指标与决策选择之间关系的能力方面克服了结构方程。所有这些,同时仍然保持广义随机效用模型中提出的理论假设的一致性和估计参数的可解释性。我们用丹麦哥本哈根的既定偏好数据来测试我们提出的用于估计汽车共享(CS)服务订阅选择的框架。结果表明,我们提出的方法提供了一个完整而现实的分割,有助于设计更好的策略。
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Attitudes and Latent Class Choice Models using Machine Learning

Latent Class Choice Models (LCCM) are extensions of discrete choice models (DCMs) that capture unobserved heterogeneity in the choice process by segmenting the population based on the assumption of preference similarities. We present a method of efficiently incorporating attitudinal indicators in the specification of LCCM, by introducing Artificial Neural Networks (ANN) to formulate latent variables constructs. This formulation overcomes structural equations in its capability of exploring the relationship between the attitudinal indicators and the decision choice, given the Machine Learning (ML) flexibility and power in capturing unobserved and complex behavioural features, such as attitudes and beliefs. All of this while still maintaining the consistency of the theoretical assumptions presented in the Generalized Random Utility model and the interpretability of the estimated parameters. We test our proposed framework for estimating a Car-Sharing (CS) service subscription choice with stated preference data from Copenhagen, Denmark. The results show that our proposed approach provides a complete and realistic segmentation, which helps design better policies.

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来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
期刊最新文献
Editorial Board Latent class choice models with an error structure: Investigating potential unobserved associations between latent segmentation and behavior generation Model choice and framing effects: Do discrete choice modeling decisions affect loss aversion estimates? A consistent moment equations for binary probit models with endogenous variables using instrumental variables Transformation-based flexible error structures for choice modeling
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