Bayesian learning and skill accumulation in video game play

We study players’ engagement decisions with different game modes in a popular multi-generational franchise video game. Some game modes are competitive where players’ utility might derive from performance, whereas other game modes are casual and feature the aspect of social interactions with other players. Model-free data patterns suggest that three potential mechanisms could jointly determine players’ decisions to engage in competitive versus casual game mode: unobserved preference heterogeneity, learning about their preferences for different modes, and skill accumulation. We develop a novel model that explains players’ decisions along both the extensive and the intensive margins with respect to different modes, while incorporating all three mechanisms. These mechanisms can be separately identified in our data set due to the panel feature of the multi-generational game. Our estimation results show several key insights about each mechanism. First, playing the competitive mode accumulates skills much faster than the casual mode. In addition, we find that 27% of the players are inherently more competitive, and accumulate skills much faster by playing the competitive mode more often. Thus, these players spend more time playing and learn about their true preferences faster than others. Building on these insights, we propose several implementable game mode related actions for the managers to improve player engagement along the extensive and intensive margins. For example, introducing a new game mode with competitive features improves players’ extensive margins more effectively (by 15 $$\sim $$ 32%), while a new game mode with casual features improves players’ intensive margins more effectively (by 20 $$\sim $$ 28%).