Simultaneous vs. Sequential Crowdsourcing Contests

Abstract

In a crowdsourcing contest, innovation is outsourced by a firm to an open crowd that compete in generating innovative solutions. Given that the projects typically consist of multiple attributes, how should the firm optimally design a crowdsourcing contest for such a project? We consider two alternative mechanisms. One is a simultaneous contest, where the best solution is chosen from the aggregate solutions simultaneously submitted by all contestants. The other is multiple sequential sub-contests, with each dedicated to one attribute and the contestants asked to build upon the best work in progress from previous sub-contests. It is intuitive that the sequential contest has the advantage of potentially creating a cooperative final solution contributed by different contestants. However, somewhat surprisingly, we show that the sequential contest may reduce the incentive for the crowd to exert effort. The comparison of the expected best performances in the two contests depends on the project's characteristics. For example, if the project is relatively difficult (resp., easy), the sequential (resp., simultaneous) contest is optimal. If the number of contestants is large enough (resp., small enough), the sequential (resp., simultaneous) contest is optimal. When the firm optimally allocates the total prize to sub-contests, if the difficulty is sufficiently different across attributes, the sequential contest performs better by motivating contestants to make more efforts than the simultaneous contest. Otherwise, the simultaneous contest may perform better. We also consider the expected average performance as an alternative criterion and extend the model to account for contestants with heterogeneous cost functions and starting points.