Power of Bonus in Pricing for Crowdsourcing

TL;DR

This paper analyzes simple yet effective pricing strategies in crowdsourcing, demonstrating that adding bonus payments to common pricing can achieve near-optimal utility, reducing the need for complex personalization.

Contribution

It introduces the concept of the Price of Agnosticism, showing that common pricing with bonuses can approximate personalized pricing, supported by theoretical bounds and practical experiments.

Findings

Bonus payments are crucial in common pricing for efficiency.

Personalized pricing offers limited advantage over simple common pricing.

Experiments validate the theoretical bounds and effectiveness of bonus payments.

Abstract

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.