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

TL;DR

This paper explores how a principal can design data-driven contracts to prevent collusion among self-interested agents in multi-agent systems, especially when interactions are infinite, to ensure effort incentives are maintained.

Contribution

It introduces a novel contract design framework that disincentivizes agent collusion in repeated interactions using data-driven methods, addressing a key challenge in multi-agent systems.

Findings

Agents can collude in finite interactions despite known types.

Infinite interactions enable the principal to prevent collusion with proper contracts.

Data-driven contract design effectively disincentivizes collusion in repeated settings.

Abstract

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 incentivizes multiple agents of different types who can collude with each other to derive rent. 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 agents are coupled in their payments, we show that 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. However, if the principal and the agents interact infinitely often, the principal can disincentivize agent collusion through a suitable data-driven contract.