ADVERSARIAL COORDINATION ON SOCIAL NETWORKS

Extensive literature exists studying decentralized coordination and consensus, with considerable attention devoted to ensuring robustness to faults and attacks.

However, most of the latter literature assumes that non-malicious agents follow simple stylized rules. In reality, decentralized protocols often involve humans, and understanding how people coordinate in adversarial settings is an open problem.

We initiate a study of this problem, starting with a human subjects investigation of human coordination on networks in the presence of adversarial agents, and subsequently using the resulting data to bootstrap the development of a credible agent-based model of adversarial decentralized coordination.

In human subjects experiments, we observe that while adversarial nodes can successfully prevent consensus, the ability to communicate can significantly improve robustness, with the impact particularly significant in scale-free networks. On the other hand, and contrary to typical stylized models of behavior, we show that the existence of trusted nodes has limited utility.
Next, we use the data collected in human subject experiments to develop a data-driven agent-based model of adversarial coordination.
We show that this model successfully reproduces observed behavior in experiments, is robust to small errors in individual agent models, and illustrate its utility by using it to explore the impact of optimizing network location of trusted and adversarial nodes.