  This work seeks to design decisionmaking rules for autonomous agents to
jointly influence and optimize the behavior of teamed human decisionmakers in
the presence of an adversary. We study a situation in which computational jobs
are scheduled on servers by a collection of autonomous machines in concert with
self-interested human decisionmakers, and the human and machine schedulers must
react to an adversary's attack on one of the servers. We show a simple machine
scheduling policy such that if all schedulers have permission to schedule jobs
on all servers, increasing the penetration of machine schedulers always
increases the level of security in the system, even when the machine schedulers
have no explicit coordination or communication amongst themselves. However, we
show a companion result in which simple constraints on server availability can
nullify the machine schedulers' ability to effectively influence human
schedulers; here, even if machine schedulers control an overwhelming majority
of jobs, are socially-aware, and fully coordinated amongst themselves, they are
incapable of influencing human decisionmakers to mitigate the harm of an
attack.
