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Robust coordination in adversarial social networks: From human behavior to agent-based modeling

Published online by Cambridge University Press:  17 May 2021

Chen Hajaj Open the ORCID record for Chen Hajaj [Opens in a new window] ,
Zlatko Joveski Open the ORCID record for Zlatko Joveski [Opens in a new window] ,
Sixie Yu  and
Yevgeniy Vorobeychik Open the ORCID record for Yevgeniy Vorobeychik [Opens in a new window]
Chen Hajaj*
Affiliation:
Department of Industrial Engineering and Management, Ariel University, Ariel, Israel Cyber Innovation Center, Ariel University, Ariel, Israel
Zlatko Joveski
Affiliation:
Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA (e-mail: [email protected])
Sixie Yu
Affiliation:
Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA (e-mails: [email protected], [email protected])
Yevgeniy Vorobeychik
Affiliation:
Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA (e-mails: [email protected], [email protected])
*
*Corresponding author. Email: [email protected]
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Abstract

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Decentralized coordination is one of the fundamental challenges for societies and organizations. While extensively explored from a variety of perspectives, one issue that has received limited attention is human coordination in the presence of adversarial agents. We study this problem by situating human subjects as nodes on a network, and endowing each with a role, either regular (with the goal of achieving consensus among all regular players), or adversarial (aiming to prevent consensus among regular players). We show that adversarial nodes are, indeed, quite successful in preventing consensus. However, we demonstrate that having the ability to communicate among network neighbors can considerably improve coordination success, as well as resilience to adversarial nodes. Our analysis of communication suggests that adversarial nodes attempt to exploit this capability for their ends, but do so in a somewhat limited way, perhaps to prevent regular nodes from recognizing their intent. In addition, we show that the presence of trusted nodes generally has limited value, but does help when many adversarial nodes are present, and players can communicate. Finally, we use experimental data to develop computational models of human behavior and explore additional parametric variations: features of network topologies and densities, and placement, all using the resulting data-driven agent-based (DDAB) model.

Keywords

coordinationsocial networksadversariesagent-based modeling
Type
Research Article
Information
Network Science , Volume 9 , Issue 3 , September 2021 , pp. 255 - 290
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

Action Editor: Fernando Vega-Redondo

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