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

• 成果类型: Article
• 署名作者: Zhao, Yi; Yang, Sha; Shum, Matthew; Dutta, Shantanu
• 署名单位: University System of Georgia; Georgia State University; University of Southern California; California Institute of Technology
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2021.4255
• 发表日期: 2022
• 页码: 8062-8082
• 关键词: Learning; bounded rationality; prediction bias; risk preference; choice model; dynamic structural model; online gaming

摘要

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/underestimation tendencies and risk-averse/ neutral/-seeking preferences-two features that are particularly relevant when modeling game-playing behavior. We develop an algorithm for estimating such a 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 to be risk seeking with a moderate amount of uncertainty. We uncover two segments of players labeled as experiencers versus achievers-the former tend to derive a higher utility from the playing process, and 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.