Mechanistic and data-driven agent-based models to explain human behavior in online networked group anagram games

Vanessa Cedeno-Mieles, Zhihao Hu, Xinwei Deng, Yihui Ren, Abhijin Adiga, Christopher Barrett, Saliya Ekanayake, Gizem Korkmaz, Chris J. Kuhlman, Dustin Machi, Madhav V. Marathe, S. S. Ravi, Brian J. Goode, Naren Ramakrishnan, Parang Saraf, Nathan Self, Noshir Contractor, Joshua M. Epstein, Michael W. Macy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
EditorsFrancesca Spezzano, Wei Chen, Xiaokui Xiao
PublisherAssociation for Computing Machinery, Inc
Pages357-364
Number of pages8
ISBN (Electronic)9781450368681
DOIs
StatePublished - Aug 27 2019
Event11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 - Vancouver, Canada
Duration: Aug 27 2019Aug 30 2019

Publication series

NameProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

Conference

Conference11th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019
CountryCanada
CityVancouver
Period8/27/198/30/19

ASJC Scopus subject areas

  • Communication
  • Computer Networks and Communications
  • Information Systems and Management
  • Sociology and Political Science

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  • Cite this

    Cedeno-Mieles, V., Hu, Z., Deng, X., Ren, Y., Adiga, A., Barrett, C., Ekanayake, S., Korkmaz, G., Kuhlman, C. J., Machi, D., Marathe, M. V., Ravi, S. S., Goode, B. J., Ramakrishnan, N., Saraf, P., Self, N., Contractor, N., Epstein, J. M., & Macy, M. W. (2019). Mechanistic and data-driven agent-based models to explain human behavior in online networked group anagram games. In F. Spezzano, W. Chen, & X. Xiao (Eds.), Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 (pp. 357-364). (Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3341161.3342965