  Global coordination is required to solve a wide variety of challenging
collective action problems from network colorings to the tragedy of the
commons. Recent empirical study shows that the presence of a few noisy
autonomous agents can greatly improve collective performance of humans in
solving networked color coordination games. To provide further analytical
insights into the role of behavioral randomness, here we study myopic
artificial agents attempt to solve similar network coloring problems using
decision update rules that are only based on local information but allow random
choices at various stages of their heuristic reasonings. We consider that
agents are distributed over a random bipartite network which is guaranteed to
be solvable with two colors. Using agent-based simulations and theoretical
analysis, we show that the resulting efficacy of resolving color conflicts is
dependent on the specific implementation of random behavior of agents,
including the fraction of noisy agents and at which decision stage noise is
introduced. Moreover, behavioral randomness can be finely tuned to the specific
underlying population structure such as network size and average network degree
in order to produce advantageous results in finding collective coloring
solutions. Our work demonstrates that distributed greedy optimization
algorithms exploiting local information should be deployed in combination with
occasional exploration via random choices in order to overcome local minima and
achieve global coordination.
