TY - JOUR
T1 - Human Team Behavior and Predictability in the Massively Multiplayer Online Game WOT Blitz
AU - Emmert-Streib, Frank
AU - Tripathi, Shailesh
AU - Dehmer, Matthias
N1 - Publisher Copyright:
© 2023 Copyright held by the owner/author(s).
PY - 2023/10/11
Y1 - 2023/10/11
N2 - Massively multiplayer online games (MMOGs) played on the Web provide a new form of social, computer-mediated interactions that allow the connection of millions of players worldwide. The rules governing team-based MMOGs are typically complex and nondeterministic giving rise to an intricate dynamical behavior. However, due to the novelty and complexity of MMOGs, their behavior is understudied. In this article, we investigate the MMOG World of Tanks Blitz by using a combined approach based on data science and complex adaptive systems. We analyze data on the population level to get insights into organizational principles of the game and its game mechanics. For this reason, we study the scaling behavior and the predictability of system variables. As a result, we find a power-law behavior on the population level revealing long-range interactions between system variables. Furthermore, we identify and quantify the predictability of summary statistics of the game and its decomposition into explanatory variables. This reveals a heterogeneous progression through the tiers and identifies only a single system variable as key driver for the win rate.
AB - Massively multiplayer online games (MMOGs) played on the Web provide a new form of social, computer-mediated interactions that allow the connection of millions of players worldwide. The rules governing team-based MMOGs are typically complex and nondeterministic giving rise to an intricate dynamical behavior. However, due to the novelty and complexity of MMOGs, their behavior is understudied. In this article, we investigate the MMOG World of Tanks Blitz by using a combined approach based on data science and complex adaptive systems. We analyze data on the population level to get insights into organizational principles of the game and its game mechanics. For this reason, we study the scaling behavior and the predictability of system variables. As a result, we find a power-law behavior on the population level revealing long-range interactions between system variables. Furthermore, we identify and quantify the predictability of summary statistics of the game and its decomposition into explanatory variables. This reveals a heterogeneous progression through the tiers and identifies only a single system variable as key driver for the win rate.
KW - complex system
KW - computational social science
KW - human behavior
KW - Massively multiplayer online games
KW - prediction
KW - statistical model
UR - http://www.scopus.com/inward/record.url?scp=85183330519&partnerID=8YFLogxK
U2 - 10.1145/3617509
DO - 10.1145/3617509
M3 - Article
AN - SCOPUS:85183330519
SN - 1559-1131
VL - 18
JO - ACM Transactions on the Web
JF - ACM Transactions on the Web
IS - 1
M1 - 5
ER -