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

Abhijin Adiga, Gizem Korkmaz, Chris Kuhlman, Madhav Marathe, Naren Ramakrishnan, Parang Saraf, Nathan Self

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.

Vanessa Cedeno-Mieles , Zhihao Hu, Xinwei Deng, Yihui Ren, Abhijin Adiga, Christopher L. 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: Mechanistic and data-driven agent-based models to explain human behavior in online networkedgroup anagram games. ASONAM 2019: 357-364