20 Pages Posted: 1 Feb 2017 Last revised: 15 Mar 2017
Date Written: March 15, 2017
A large body of laboratory-based research suggests that arbitrary group assignments (i.e. "minimal groups"') can lead to in-group bias. We use the release of a popular augmented reality game Pokemon Go to study this phenomenon in a hybrid lab-field experiment. We analyze the behavior of 940 Pokemon Go players randomly matched with other Pokemon Go players to participate in a 1-shot Prisoner's Dilemma game. We find that participants are more cooperative when their partner is from the same Pokemon Go team, demonstrating an ecologically valid occurrence of the minimal group paradigm. We also use transformed outcome lasso regressions to look for heterogeneity in treatment effects. Machine learning, rather than unprincipled manual data mining, minimizes overfitting and reduces susceptibility to multiple comparison issues and researcher degrees of freedom. Our approach finds an important moderator of the in-group bias effect: the salience of Pokemon Go. As it's popularity wanes, so does the size of the in-group bias in our experiments. Thus our full set of results show that real-world minimal group bias is quick to arise but also potentially fragile.
Keywords: behavioral economics, in group bias, machine learning
Suggested Citation: Suggested Citation
Peysakhovich, Alexander and Rand, David G., In-Group Favoritism Caused by Pokemon Go and the Use of Machine Learning for Principled Investigation of Potential Moderators (March 15, 2017). Available at SSRN: https://ssrn.com/abstract=2908978