  Many cooperative physical tasks require that individuals play specialized
roles (e.g., leader-follower). Humans are adept cooperators, negotiating these
roles and transitions between roles innately. Yet how roles are delegated and
reassigned is not well understood. Using a genetic algorithm, we evolve
simulated agents to explore a space of feasible role-switching policies.
Applying these switching policies in a cooperative manual task, agents process
visual and haptic cues to decide when to switch roles. We then analyze the
evolved virtual population for attributes typically associated with
cooperation: load sharing and temporal coordination. We find that the best
performing dyads exhibit high temporal coordination (anti-synchrony). And in
turn, anti-synchrony is correlated to symmetry between the parameters of the
cooperative agents. These simulations furnish hypotheses as to how human
cooperators might mediate roles in dyadic tasks.
