Stimulating skill evolution in market-based crowdsourcing

Abstract

Crowdsourcing has emerged as an important paradigm in human problem-solving techniques on the Web. One application of crowdsourcing is to outsource certain tasks to the crowd that are difficult to implement in software. Another potential benefit of crowdsourcing is the on-demand allocation of a flexible workforce. Businesses may outsource tasks to the crowd based on temporary workload variations. A major challenge in crowdsourcing is to guarantee high-quality processing of tasks. We present a novel crowdsourcing marketplace that matches tasks to suitable workers based on auctions. The key to ensuring high quality lies in skilled members whose capabilities can be estimated correctly. We present a novel auction mechanism for skill evolution that helps to correctly estimate workers and to evolve skills that are needed. Evaluations show that this leads to improved crowdsourcing. © 2011 Springer-Verlag.