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PhD Dissertation Defense - Alexander Bisberg

Title: Modeling Competitive and Social Success in Multiplayer Online Games
 
Date and Time: Thursday, May 8th, 2025 | 2:15pm
 
Location: GCS 502C
 
Committee Members: Emilio Ferrara (Chair), Luca Luceri, Dmitri Williams
 
Abstract: 
This dissertation examines the continuum from competitive to social success in online multiplayer games, employing quantitative and qualitative methods to analyze player behavior and performance across diverse virtual environments. Beginning with a framework for systematically comparing skill rating models in competitive contexts (FRAGEM-S), the research progresses to novel applications of graph neural networks for win prediction, demonstrating improved accuracy and cross-league generalizability. At the intersection of competitive and social domains, analysis of communication patterns in World of Tanks clan networks reveals that high-performing teams exhibit distinctive communication structures characterized by distributed connectivity rather than mere volume. Moving toward the social end of the spectrum, a quasi-experimental study in Sky: Children of Light provides compelling evidence for both generalized reciprocity and third-party influence in virtual worlds, showing that experiencing or witnessing generosity significantly increases future prosocial behavior and game engagement. Finally, unsupervised learning techniques identify persistent behavioral archetypes—including Lone Wolves, Newbies, and Socialites that remain consistent across different time periods and game genres. Together, these findings provide a comprehensive framework for understanding competitive and social success in online games, with implications extending to virtual teams and online communities more broadly.

Location: Ginsburg Hall (GCS) - 502C

Audiences: Everyone Is Invited

Contact: Alex Bisberg

This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.

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