Maximizing Revenue From Selfish Agents in Crowd Tasks: Indirect Incentive Strategies

We study mechanisms incentivizing the contribution of selfish agents addressing crowd tasks in a Bayesian setting with externalities due to network effects. An example is crowdsensing, where a large group of people share data collected by their mobile devices. The central problem we investigate is the relationship between direct and indirect mechanisms. Indeed, while direct mechanisms represent tools to address implementation problems optimally, the requirement that the contribution level of every agent when addressing the task is chosen by the mechanism makes these mechanisms hardly used in practice. On the other hand, while indirect mechanisms allow every agent to be free to choose their contribution level, these mechanisms may be highly inefficient. Our desideratum is to design indirect mechanisms that closely match the performance of optimal direct mechanisms. We design an indirect mechanism such that the Price of Stability over the revenue is bounded and in special cases without network effects the Price of Anarchy over the revenue is one. Our results suggest that indirect revelation mechanisms can be an excellent option in real-world applications.