Introducing reputation systems to the economics of outsourcing computations to rational workers

Abstract

Outsourcing computation to remote parties (“workers”) is an increasingly common practice, owing in part to the growth of cloud computing. However, outsourcing raises concerns that outsourced tasks may be completed incorrectly, whether by accident or because workers cheat to minimize their cost and optimize their gain. The goal of this paper is to explore, using game theory, the conditions under which the incentives for all parties can be configured to efficiently disincentivize worker misbehavior, either inadvertent or deliberate. By formalizing multiple scenarios with game theory, we establish conditions to discourage worker cheating that take into account the dynamics of multiple workers, workers with limited capacity, and changing levels of trust. A key novelty of our work is modeling the use of a reputation system to decide how computation tasks are allocated to workers based on their reliability, and we provide insights on strategies for using a reputation system to increase the expected quality of results. Overall, our results contribute to make outsourcing computation more reliable, consistent, and predictable.