Bridging between Hypothetical and Incentivized Choice

假设选择和激励选择之间的桥梁

• 批准号: 411120839
• 负责人: Professor Dr. Thomas Otter
• 金额: --
• 依托单位: Professur für Quantitatives Marketing
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/411120839?language=en
• 关键词: Bridging; between; Hypothetical; Incentivized; Choice

The hypothetical nature of choices collected in typical discrete choice experiments for market research has long been a source of concern. Because choices in these experiments are more or less inconsequential for respondents, inferences gleaned from this data may lack validity. Recent research in marketing indeed demonstrates increased predictive validity of models calibrated based on choices by properly incentivized respondents (e.g. Ding et al.,2005; Ding2007, Ding et al., 2009; Dong et al., 2010). However, conducting so called incentive-aligned discrete choice experiments is more effortful and costly compared to the standard hypothetical setting. The goal of this proposal therefore is to develop a model based framework that parsimoniously bridges between data from hypothetical discrete choice experiments and data from incentive-aligned discrete choice experiments for the purpose of conserving on data collection effort and cost, however, in keeping with the goal of predicting to incentivized choices. In a nutshell, the framework leverages certain invariance assumptions to fuse a large amount of data from hypothetical discrete choice experiment and a relatively much smaller amount of data from incentive-aligned discrete choice experiment collected in independent experiments, but in the same population, for the purpose of simulating incentivized choices in this population. The framework assumes, in line with economic theory, a common set of invariant ('deep') preference parameters, but explicitly accounts for differential decision effort between the hypothetical and the incentive-aligned setting. Differential decision effort is conceptualized as both affecting the amount of cognitive processing, as well as the information set selected for processing. As a consequence, the amount of decision effort may materially change choice probabilities and outcomes, even if underlying, deep preference parameters are invariant. Operationally, we build on process-based choice models developed in mathematical psychology and specifically the recently proposed dependent Poisson race model. Our bridging framework will facilitate valid inferences in situations where it is very or even prohibitively costly to conduct incentive aligned experiments with a sufficiently large number of respondents.

在典型的市场研究离散选择实验中收集的选择的假设性一直是人们关注的焦点。由于这些实验中的选择对受访者来说或多或少是无关紧要的，因此从这些数据中收集的推论可能缺乏有效性。 最近的市场营销研究确实表明，基于适当激励的受访者的选择校准的模型的预测有效性增加（例如，Ding等人，2005; Ding 2007，Ding等人，2009年; Dong等人，2010年）。然而，与标准假设设置相比，进行所谓的激励一致的离散选择实验更加费力和昂贵。 因此，本提案的目标是开发一个基于模型的框架，该框架在来自假设的离散选择实验的数据和来自激励一致的离散选择实验的数据之间建立了节俭的桥梁，以节省数据收集工作和成本，然而，与预测激励选择的目标保持一致。 简而言之，该框架利用一定的不变性假设来融合来自假设离散选择实验的大量数据和来自独立实验中收集的激励一致的离散选择实验的相对少量的数据，但在同一人群中，为了模拟该人群中的激励选择。 该框架假设，符合经济理论，一套共同的不变（“深”）的偏好参数，但明确占差分决策努力之间的假设和激励对齐设置。 差异性决策努力被概念化为既影响认知处理的量，又影响选择用于处理的信息集。因此，即使潜在的深层偏好参数是不变的，决策努力的量也可能会实质性地改变选择概率和结果。 在操作上，我们建立在数学心理学中开发的基于过程的选择模型之上，特别是最近提出的依赖泊松种族模型。我们的桥接框架将促进有效的推理的情况下，它是非常或甚至是昂贵的，以进行激励对齐的实验与足够多的受访者。