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An important class of game-theoretic incentive mechanisms for elicitingeffort from a crowd are the peer based mechanisms, in which workers are paid bymatching their answers with one another. The other classic mechanism is to havethe workers solve some gold standard tasks and pay them according to theiraccuracy on gold tasks. This mechanism ensures stronger incentive compatibilitythan the peer based mechanisms but assigning gold tasks to all workers becomesinefficient at large scale. We propose a novel mechanism that assigns goldtasks to only a few workers and exploits transitivity to derive accuracy of therest of the workers from their peers' accuracy. We show that the resultingmechanism ensures a dominant notion of incentive compatibility and fairness.

Deep Bayesian Trust : A Dominant and Fair Incentive Mechanism for Crowd