Bridging between Hypothetical and Incentivized Choice

• Applicant: Professor Dr. Thomas Otter
• Subject Area: Accounting and Finance ; General, Cognitive and Mathematical Psychology ; Statistics and Econometrics
• Term: from 2018 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 411120839

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.

• DFG Programme: Research Grants