A Double Decoy Experiment to Distinguish Theories of Dominance Effects

18 Pages Posted: 17 May 2019 Last revised: 16 Apr 2020

See all articles by Remi Daviet

Remi Daviet

Wharton Marketing Department, University of Pennsylvania

Ryan Webb

University of Toronto

Date Written: March 15, 2019

Abstract

We implement a “Double Decoy” experiment designed to separate two competing accounts of the asymmetric dominance effect in choice behaviour. In our experiment, we place an additional decoy alternative within the range of existing alternatives, therefore a theory which weights attributes by their range would predict a null effect on relative choice probabilities. Instead, we observe a significant decrease in the relative proportion of targets chosen (on average) in our sample. We also observe considerably more variation in individual behaviour than expected under the null hypothesis. To address these features of the data, we consider an alternative theory in which attributes values are compared two by two and normalized. Using a hierarchical Bayesian framework, we apply this pairwise normalization model both to our Double Decoy data and a standard discrete choice experiment. We find that it captures the variation in behaviour that we observe in both datasets better than range normalization and the standard linear additive Logit model, both in-sample and in an out-of-sample prediction exercise. We therefore propose this model as a useful empirical tool for researchers in applied settings.

JEL Classification: D87

Suggested Citation

Daviet, Remi and Webb, Ryan, A Double Decoy Experiment to Distinguish Theories of Dominance Effects (March 15, 2019). Available at SSRN: https://ssrn.com/abstract=3374514 or http://dx.doi.org/10.2139/ssrn.3374514

Remi Daviet

Wharton Marketing Department, University of Pennsylvania ( email )

406 North 42nd Street
3730 Walnut Street
Philadelphia, PA 19104
United States
2675066499 (Phone)

Ryan Webb (Contact Author)

University of Toronto ( email )

105 St. George Street
Toronto, Ontario M5S 3E6 M5S1S4
Canada

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