Leave a rating: Towards principles for the design of market feedback systems
(A2019-9863)

AUTHORS

Gary Bolton, University of Texas at Dallas; Alina Ferecatu, Rotterdam School of Management, Erasmus University; David Kusterer, University of Cologne

KEYWORDS

Market design; Recommendation systems; Hidden Markov Mixture of Experts models

ABSTRACT

Feedback systems in online markets are designed to provide reliable information on traders’ trustworthiness. In this paper, we aim to outline guidelines towards how feedback systems should be designed. We conduct an incentive aligned experiment mirroring an online market, to characterizes buyer behavior, in terms of ratings given and use of feedback scores. We investigate the friction in information transmission due to the choice of the feedback scale (3-point vs. 5-point scale) and feedback elicitation question (general or directed feedback). We characterise the impact of these feedback system design choices on system informativeness with an entropy model forecasting sellers’ performance. Using a hidden Markov mixture of experts model, we investigate whether buyers switch between giving feedback based on quality received, or based on their profits. Our analysis suggests that feedback systems mapping the feedback request and elicitation scale on the most diagnostic dimension of seller future performance are most informative.