We investigate the power of voting among diverse, randomized software agents.
With teams of computer Go agents in mind, we develop a novel theoretical model
of two-stage noisy voting that builds on recent work in machine learning. This
model allows us to reason about a collection of agents with different biases (determined by the first-stage noise models), which, furthermore, apply randomized
algorithms to evaluate alternatives and produce votes (captured by the secondstage noise models). We analytically demonstrate that a uniform team, consisting
of multiple instances of any single agent, must make a significant number of mistakes, whereas a diverse team converges to perfection as the number of agents
grows. Our experiments, which pit teams of computer Go agents against strong
agents, provide evidence for the effectiveness of voting when agents are diverse.