Robustness of reputation-based trust

We consider the problem of user agents selecting processor agents to processor tasks. We assume that processor agents are drawn from two populations: high and low-performing processors with different averages but similar variance in performance. For selecting a processor, a user agent queries other user agents for their high/low rating of different processors. We assume that a known percentage of "liar" users, who give inverse estimates of processors. We develop a trust mechanism that determines the number of users to query given a target guarantee threshold likelihood of choosing high-performance processors in the face of such "noisy" reputation mechanisms. We evaluate the robustness of this reputation-based trusting mechanism over varying environmental parameters like percentage of liars, performance difference and variances for high and low-performing agents, learning rates, etc.