The extent to which players feel compelled to play a game is a primary factor in determining that game's success. Using ideas from self-determination theory, we propose that the drive to play games is related to player’s ability to exercise their mastery drive. In self-determination theory, increasing one’s control is intrinsically rewarding, however it is difficult to instantiate this theory to make concrete predictions of when a player will quit a game early in favor of another activity. The problem of predicting within-game churn events involves modeling and predicting a player's motivational state to remain in a game.
We combine new motivation theory ideas with machine learning methods to develop a computational model that postulates that player’s satisfaction is directly related to their perceived locus of control, extrinsic vs. intrinsic, and hypothesize a set of measurable signals that mediate a player's locus of control. An agent will continue goal pursuit within the game based on its "empowerment", which is essentially its ability to predict the future based on its own actions. This indicator of locus of control accounts for both the feasibility of the goal and whether expected progress toward a selected goal is being achieved. A player's behavior can be used to estimate their empowerment by correlating their continuation of goal pursuit with measures of their expectations of progress and goal-achievability. We apply these concepts and build a model to predict player when players will quit a game early for the multiplayer game, Dota2.