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

Author:

Cedeno-Mieles Vanessa1,Hu Zhihao1,Deng Xinwei1,Ren Yihui2,Adiga Abhijin3,Barrett Christopher3,Ekanayake Saliya3,Korkmaz Gizem3,Kuhlman Chris J.3,Machi Dustin3,Marathe Madhav V.3,Ravi S. S.3,Goode Brian J.1,Ramakrishnan Naren1,Saraf Parang1,Self Nathan1,Contractor Noshir4,Epstein Joshua M.5,Macy Michael W.6

Affiliation:

1. Virginia Tech

2. Brookhaven National Laboratory

3. University of Virginia

4. Northwestern University

5. Santa Fe Institute

6. Cornell University

Funder

NSF CRISP 2.0

DARPA Cooperative Agreement

DTRA CNIMS

Publisher

ACM

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An uncertainty quantification framework for agent-based modeling and simulation in networked anagram games;Journal of Simulation;2024-02-29

2. A Calibration Model For Bot-Like Behaviors In Agent-Based Anagram Game Simulation;2023 Winter Simulation Conference (WSC);2023-12-10

3. Modeling fashion as an emergent collective behavior of bored individuals;Scientific Reports;2023-11-22

4. An Uncertainty Quantification Approach For Agent-Based Modeling Of Human Behavior In Networked Anagram Games;2021 Winter Simulation Conference (WSC);2021-12-12

5. Compression for Very Sparse Big Social Data;2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM);2020-12-07

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