Revenue Management in Video Games and with Fairness
By: Xiao Lei
Publisher: [publisher not identified]
Published: 2022
Place of publication: [New York, N.Y.?]
Language: eng
Description: Video games represent the largest and fastest-growing segment of the entertainment industry. Despite its popularity in practice, it has received limited attention from the operations community. Managing product monetization and engagement presents unique challenges due to the characteristics of gaming platforms, where players and the gaming platform have repeated (and endogenously controlled) interactions. These practices have also led to new customer concerns and thus regulation challenges. In this thesis, we describe a body of work that provides the first analytical results for revenue management and matchmaking problems in video games, as well as the fairness issues in many e-commerce platforms. In the first part, we discuss a prevailing selling mechanism in online gaming known as a loot box. A loot box can be viewed as a random bundle of virtual items, whose contents are not revealed until after purchase. We consider how to optimally price and design loot boxes from the perspective of a revenue-maximizing video game company, and provide insights on customer surplus and protection under such selling strategies.

In the second part, we consider how to manage player engagement in a game where players are repeatedly matched to compete against one another. Players have different skill levels which affect the outcomes of matches, and the win-loss record influences their willingness to remain engaged. Leveraging optimization and real data, we provide insights on how engagement may increase with optimal matchmaking policies and adding AI bots. In the third part, we consider an increasingly important concern in many e-commerce platforms: the inequality induced by price discrimination. While the practice of discriminatory pricing is generally widespread, it can result in disparate impact against protected groups. We consider the problem of setting prices for different groups of customers under fairness regulations, which limit the differences of various metrics (such as price and demand) across the groups. We show that different types of fairness constraints may not coexist in general, and the impact of fairness levels on social welfare could be non-monotonic and non-trivial.
Notes: Department: Industrial Engineering and Operations Research.

Thesis advisor: Adam Elmachtoub.

Thesis (Ph.D.)--Columbia University, 2022.