  We consider a simple form of pricing for a crowdsourcing system, where
pricing policy is published a priori, and workers then decide their task
acceptance. Such a pricing form is widely adopted in practice for its
simplicity, e.g., Amazon Mechanical Turk, although additional sophistication to
pricing rule can enhance budget efficiency. With the goal of designing
efficient and simple pricing rules, we study the impact of the following two
design features in pricing policies: (i) personalization tailoring policy
worker-by-worker and (ii) bonus payment to qualified task completion. In the
Bayesian setting, where the only prior distribution of workers' profiles is
available, we first study the Price of Agnosticism (PoA) that quantifies the
utility gap between personalized and common pricing policies. We show that PoA
is bounded within a constant factor under some mild conditions, and the impact
of bonus is essential in common pricing. These analytic results imply that
complex personalized pricing can be replaced by simple common pricing once it
is equipped with a proper bonus payment. To provide insights on efficient
common pricing, we then study the efficient mechanisms of bonus payment for
several profile distribution regimes which may exist in practice. We provide
primitive experiments on Amazon Mechanical Turk, which support our analytical
findings.
