A Dynamic Model of Player Level-Progression Decisions in Online Gaming

Abstract

A key feature of online gaming, which serves as an important measure of consumer engagement with a game, is level progression, wherein players make play-or-quit decisions at each level of the game. Understanding users’ level progression behavior is therefore fundamental to game designers. In this paper, we propose a dynamic model of consumer level-progression decisions to shed light on the underlying motivational drivers. We cast the individual play-or-quit decisions in a dynamic framework with forward-looking players and consumer learning about the evolution patterns of their operation efficiencies (defined as the average score earned per operation for passing a level). We develop a boundedly rational approach to model how individuals form predictions of their own operation efficiency and playing utility. This new approach allows researchers to flexibly capture players’ over/unbiased/under estimation tendencies and risk averse/neutral/seeking preferences, two features that are particularly relevant when modeling the game-playing behavior. We develop an algorithm for estimating such dynamic model, and apply our model to level-progression data from individual players with one online game. We find that players in the sample tend to overestimate their operation efficiency, as their predicted values are significantly higher than the mean estimates inferred from their playing history with their completed levels. Furthermore, players are found risk seeking with moderate amount of uncertainty. We uncover two segments of players labeled as “Experiencers” vs. “Achievers”: while the former tend to derive a higher utility from the playing process, the latter are more goal oriented and derive a higher benefit from completing the entire game. Two counterfactual simulations demonstrate that the proposed model can help adjust the uncertainty level and configure a more effective level-progression point schedule to better engage players and improve the game developer’s revenue.

Keywords: Learning, Bounded Rationality, Prediction Bias, Risk Preference, Choice Model, Dynamic Structural Model, Online Gaming

JEL Classification: M31,C11,C61,D81,D83