Carrots and sticks — Incentives that make mobile crowdsensing work (CASPer 2016 Keynote Paper)
Salil S. Kanhere · 2016
Crowdsourcing offers a cost-effective approach to distributed problem solving and data collection by soliciting contributions (solutions, ideas, data, etc.) from a large group of people. Recently, due to the burgeoning smartphone industry and the surging demand for sensing data, a new mobile computing and sensing paradigm called mobile crowdsensing has emerged and has created significant momentum in both industry and academia. Pivotal to the viability of all such crowdsourcing systems, is whether there is enough incentive to attract sufficient participation. Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers types (e.g., abilities and costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this talk, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions for each potential winner (worker). We prove and characterize the unique equilibrium of this contest, and solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In the second part of the talk, we propose the use of Tullock contests as an alternative framework to design incentive mechanisms for crowdsourcing. We explore a new dimension of optimal Tullock contents design by provisioning the prize as a function. We are inspired by the conduciveness of Tullock contests to attracting user entry in other domains. In this talk, we explore a new dimension in optimal Tullock contest design, by superseding the contest prize-which is fixed in conventional Tullock contests-with a prize function that is dependent on the (unknown) winners contribution, in order to maximize the crowdsourcers utility. We show that this approach leads to several attractive practical advantages.