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O. Scekic, C. Dorn, S. Dustdar:
"Simulation-Based Modeling and Evaluation of Incentive Schemes in Crowdsourcing Environments";
Talk: 21st International Conference on Cooperative Information Systems (CoopIS 2013), Graz, Austria; 2013-09-11 - 2013-09-13; in: "On the Move to Meaningful Internet Systems: OTM 2013 Conferences, Confederated International Conferences: CoopIS, DOA-Trusted Cloud, and ODBASE 2013, Proceedings", R. Meersman, H. Panetto, T. Dillon, J. Eder, Z. Bellahsene, N. Ritter, P. De Leenheer, D. Dou (ed.); Springer, LNCS 8185 (2013), ISBN: 978-3-642-41029-1; 167 - 184.

Conventional incentive mechanisms were designed for business environments involving static business processes and a limited number of actors. They are not easily applicable to crowdsourcing and other social computing platforms, characterized by dynamic collaboration patterns and high numbers of actors, because the effects of incentives in these environments are often unforeseen and more costly than in a well-controlled environment of a traditional company.
In this paper we investigate how to design and calibrate incentive schemes for crowdsourcing processes by simulating joint effects of a combination of different participation and incentive mechanisms applied to a working crowd. More specifically, we present a simulation model of incentive schemes and evaluate it on a relevant real-world scenario. We show how the model is used to simulate
different compositions of incentive mechanisms and model parameters, and how these choices influence the costs on the system provider side and the number of malicious workers.

rewards, incentives, crowdsourcing, social computing, collective adaptive systems.

http://dx.doi.org/10.1007/978-3-642-41030-7_11

Project Head Schahram Dustdar:
Hybrid and Diversity-Aware Collective Adaptive Systems: When People Meet Machines to Build a Smarter Society