Stat Nonparametric Bayes to Infer Playing Strategies Adopted in a Population of Mobile Gamers

Analysis of trace logging data collections of interactions of a heterogenous and diverse population of consumers of digital software with mobile devices provides unprecedented possibilities for understanding how software is actually used and for finding recurring patterns of software usage over the population that are exhibited to greater or lesser degree in each individual software user. In this work, we consider an elementary mobile game played by a population of mobile gamers and collect pieces of game sessions over an extended period of time resulting in a collection of users' trace logs for multiple sessions. We develop a simple, yet flexible, nonparametric Bayes approach to infer playing strategies adopted in the population from the logged traces of game interactions. We demonstrate our approach finds interpretable strategies and provides good predictive performance compared to alternative modelling assumptions using a nonparametric Bayes framework