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Dr. Yang Sha's Lecture

Post Time:2016-12-21

Time:  Dec. 26th, 2016   10:00 a.m.

Room:Information Building 0224

Lecturer: Dr. Yang Sha, tenuredprofessor at University of Southern California, Yangtze Scholar Chair Professor,expert of One-hundred Talent Program of Sichuan Province, was one of the topscholars in the field of marketing and management science.

TitleOptimist,Pessimist or Realist:Modeling Consumer Level Progression Decisionin Online Gaming

Introduction:Level progression, wherein players make play-or-quit decisions ateach level of the game, marks a key feature of online gaming and serves as animportant measure of consumer engagement with a game. Understanding users’level progression behavior is therefore fundamental to game designers. In thispaper, we propose a dynamic model of consumer level progression decisions toshed lights on the underlying motivational drivers. The proposed model hasseveral innovative aspects. First, we model individual players’ learning on theevolutionary patterns of their ability. Second, we develop a heuristic approachto capture how players predict their playing utility at each level. Theproposed heuristic approach flexibly captures players’ optimistic/pessimistictendencies and risk preferences. Third, we cast the individual play-or-quitdecisions in a dynamic framework with forward looking players, and develop analgorithm for estimating such dynamic models under the proposed heuristicapproach. We apply our model to level progression data associated with anonline game from 471 players. We find that the heuristic-based approachoutperforms the standard expectation-based approach on model fit. Inparticular, the majority of players in the sample are found to be optimisticwhen forming predictions on their future playing ability and utility, and asmall group of players show neither optimism nor pessimism. Comparing the two groups,we find that optimists tend to derive a higher utility from the process ofplaying (i.e. the “Experiencers”), whereas the realists are more goal orientedand derive a higher benefit from completing the entire game (i.e. the“Achievers”). Counterfactual analysis shows that the proposed model can helpconfigure a more effective level-progression point schedule to better engageplayers and improve the game developer’s revenue.