Data-Driven Contract Design for Multi-Agent Systems With Collusion Detection

In applications such as participatory sensing and crowd sensing, self-interested agents exert costly effort towards achieving an objective for the system operator. We study such a setup where a principal designs a sequence of contracts to incentivize multiple agents of different types to achieve a global objective. The agents can collude with each other to derive rent, since the principal cannot observe the efforts exerted directly, but only the outcome of the task which is a noisy function of the effort. The type of each agent influences the effort cost and task output. For a duopoly in which agent payments are coupled, we show that i) if the principal and the agents interact finitely many times, the agents can derive rent by colluding even if the principal knows the types of the agents, and ii) if the principal and the agents interact infinitely often, the principal can disincentivize agent collusion through a dynamic data-driven contract.