Robust coordination in adversarial social networks
From human behavior to agent-based modeling
Bibliographic Data
| ID | 6161374 |
|---|---|
| Authors | Chen Hajaj (0000-0001-9940-5654, Ariel University, corresponding author), Zlatko Joveski (0000-0002-6856-801X, Vanderbilt University), Sixie Yu (Washington University in St. Louis), Yevgeniy Vorobeychik (0000-0003-2471-5345, Washington University in St. Louis) |
| Year | 2021 |
| Volume | 9 |
| Issue | 3 |
| Pages | 255-290 |
| Publication date | 2021-05-17 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Network Science (JOURNAL) |
| Journal identifiers | ISSN: 2050-1250 • E-ISSN: 2050-1242 |
| Publisher | Cambridge University Press (PUBLISHER • US) |
| DOI | 10.1017/nws.2021.5 |
| OpenAlex | W3162965289 |
| Language | EN |
| Citations received | 1 |
| References cited | 13 |
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
Adversarial system · Computer network · Computer security · Exploit · Network topology · Parametric statistics · Variety (cybernetics · Complex Network Analysis Techniques · Computer Science · Evolutionary Game Theory and Cooperation · Mathematics · Opinion Dynamics and Social Influence · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.33 |
| Citation span | 2023 - 2023 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |