  Decentralized coordination is one of the fundamental challenges for societies
and organizations. While extensively explored from a variety of perspectives,
one issue which 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 a
computational model of human behavior, and explore a number of additional
parametric variations, such as features of network topologies, using the
resulting data-driven agent-based model.
