Behavioral Market Design for Online Gaming Platforms

In this paper, we investigate market design for online gaming platforms. We ask what motivates people to continue participation—success or failure. Using data from an online chess platform, we find strong evidence of heterogeneous history-dependent stopping behavior. We identify two behavioral types of people: those who are more likely to stop playing after a loss and those who are more likely to stop playing after a win. We propose a behavioral dynamic choice model in which the utility from playing another game is directly affected by the previous game’s outcome. We estimate this time nonseparable preference model and conduct counterfactual analyses to study alternative market designs. A matching algorithm designed to leverage stopping behavior can substantially alter the length of play. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03628 .