Nonparametric Bayesian modelling of monotone preferences for discrete choice experiments
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离散选择实验中单调偏好的非参数贝叶斯建模
- 研究问题
- 当产品属性没有独立市场、属性可量化且偏好单调时，如何灵活刻画离散选择实验中的个体效用与偏好异质性？
- 方法与思路
- 作者用 Dirichlet 先验建模效用组成部分，分析先验对单调不减效用函数的支持，并开发用于后验模拟的 MCMC 算法。
- 材料陈述的贡献
- 摘要称，单调性是该情境中适当的唯一形状约束，先验支持全部单调不减效用函数；算法展示偏好异质性并用于车险选择实验。
- 阅读关注点
- 应核对单调性结论所依赖的情境、效用归一化与识别、先验支持的含义，以及 MCMC 在典型实验规模下的计算诊断。
- 阅读边界
- 仅据所给出版社摘要，不能判断效用识别条件、先验敏感性或算法性能数值；单调性约束的适用性不能推广到所有消费选择问题。
原文摘要
Discrete choice experiments are widely used to learn about the distribution of individual preferences for product attributes. Such experiments are often designed and conducted deliberately for the purpose of designing new products. There is a long-standing literature on nonparametric and Bayesian modelling of preferences for the study of consumer choice when there is a market for each product, but this work does not apply when such markets fail to exist as is the case with most product attributes. This paper takes up the common case in which attributes can be quantified and preferences over these attributes are monotone. It shows that monotonicity is the only shape constraint appropriate for a utility function in these circumstances. The paper models components of utility using a Dirichlet prior distribution and demonstrates that all monotone nondecreasing utility functions are supported by the prior. It develops a Markov chain Monte Carlo algorithm for posterior simulation that is reliable and practical given the number of attributes, choices and sample sizes characteristic of discrete choice experiments. The paper uses the algorithm to demonstrate the flexibility of the model in capturing heterogeneous preferences and applies it to a discrete choice experiment that elicits preferences for different auto insurance policies.