An agent-based model for crowdsourcing systems

Crowdsourcing is a complex system composed of many interactive distributed agents whom we have little information about. Agent-based modeling (ABM) is a natural way to study complex systems since they share common properties, such as the global behavior emerging on the basis of local interactions between elements. Although significant attention has been given to dynamics of crowdsourcing systems, relatively little is known about how workers react to varying configurations of tasks. In addition, existing ABMs for crowdsourcing systems are theoretical, and not based on data from real crowdsourcing platforms. The focus of this paper is on capturing the relationships among properties of tasks, characteristics of workers, and performance metrics via an ABM. This approach is validated by running experiments on Amazon Mechanical Turk (AMT).