1 research outputs found
Partial Truthfulness in Minimal Peer Prediction Mechanisms with Limited Knowledge
We study minimal single-task peer prediction mechanisms that have limited
knowledge about agents' beliefs. Without knowing what agents' beliefs are or
eliciting additional information, it is not possible to design a truthful
mechanism in a Bayesian-Nash sense. We go beyond truthfulness and explore
equilibrium strategy profiles that are only partially truthful. Using the
results from the multi-armed bandit literature, we give a characterization of
how inefficient these equilibria are comparing to truthful reporting. We
measure the inefficiency of such strategies by counting the number of dishonest
reports that any minimal knowledge-bounded mechanism must have. We show that
the order of this number is , where is the number of
agents, and we provide a peer prediction mechanism that achieves this bound in
expectation