TY - GEN
T1 - Mechanistic and data-driven agent-based models to explain human behavior in online networked group anagram games
AU - Cedeno-Mieles, Vanessa
AU - Hu, Zhihao
AU - Deng, Xinwei
AU - Ren, Yihui
AU - Adiga, Abhijin
AU - Barrett, Christopher
AU - Ekanayake, Saliya
AU - Korkmaz, Gizem
AU - Kuhlman, Chris J.
AU - Machi, Dustin
AU - Marathe, Madhav V.
AU - Ravi, S. S.
AU - Goode, Brian J.
AU - Ramakrishnan, Naren
AU - Saraf, Parang
AU - Self, Nathan
AU - Contractor, Noshir
AU - Epstein, Joshua M.
AU - Macy, Michael W.
N1 - Funding Information:
ACKNOWLEDGMENT We thank the anonymous reviewers for their helpful feedback. We thank our colleagues at NSSAC and computer system administrators: Dominik Borkowski, Jason Decker, Miles Gentry, Jeremy Johnson, William Marmagas, Douglas McMaster, and Kevin Shinpaugh. This work has been partially supported by NSF CRISP 2.0 (Grant 1832587), DARPA Co-operative Agreement D17AC00003 (NGS2), DTRA CNIMS (Contract HDTRA1-11-D-0016-0001), DTRA Comprehensive National Incident Management System Contract HDTRA1-17-D-0023. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/8/27
Y1 - 2019/8/27
N2 - In anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goal-setting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data-driven models of human decision-making to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human decision-making and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone.
AB - In anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goal-setting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data-driven models of human decision-making to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human decision-making and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone.
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U2 - 10.1145/3341161.3342965
DO - 10.1145/3341161.3342965
M3 - Conference contribution
T3 - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
SP - 357
EP - 364
BT - Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
A2 - Spezzano, Francesca
A2 - Chen, Wei
A2 - Xiao, Xiaokui
PB - Association for Computing Machinery, Inc
T2 - 11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
Y2 - 27 August 2019 through 30 August 2019
ER -