What Prize Is Right? How to Learn the Optimal Structure for Crowdsourcing Contests

• Published in: PRICAI 2019: Trends in Artificial Intelligence Vol. 11670; pp. 85 - 97
• Main Authors: Truong, Nhat Van-Quoc, Stein, Sebastian, Tran-Thanh, Long, Jennings, Nicholas R.
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Bayesian optimisation; Crowdsourcing; Incentive
• ISBN: 3030299074; 9783030299071
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-29908-8_7

In crowdsourcing, one effective method for encouraging par-ticipants to perform tasks is to run contests where participants compete against each other for rewards. However, there are numerous ways to implement such contests in specific projects. They could vary in their structure (e.g., performance evaluation and the number of prizes) and parameters (e.g., the maximum number of participants and the amount of prize money). Additionally, with a given budget and a time limit, choosing incentives (i.e., contest structures with specific parameter values) that maximise the overall utility is not trivial, as their respective effectiveness in a specific project is usually unknown a priori. Thus, in this paper, we propose a novel algorithm, BOIS (Bayesian-optimisation-based incentive selection), to learn the optimal structure and tune its parameters effectively. In detail, the learning and tuning problems are solved simultaneously by using online learning in combination with Bayesian optimisation. The results of our extensive simulations show that the performance of our algorithm is up to 85% of the optimal and up to 63% better than state-of-the-art benchmarks.