Incentive Mechanisms for Social Computing

Human participation in hybrid collective adaptive systems (hCAS) is overgrowing conventional social computing where humans solve simple, independent tasks. Novel systems are attempting to leverage humans for more intellectually challenging tasks, involving longer lasting worker engagement and complex collaboration patterns. This poses the problem of finding, engaging, motivating, retaining and assessing workers, thus adapting the participating workforce. Existing incentive management techniques in use in socio-technical platforms are not suitable for the more intellectually-challenging tasks. In addition, each platform currently develops custom solutions and implements them anew. This approach is not portable, and effectively prevents reuse of common incentive logic and reputation transfer. Consequently, this prevents workers from comparing different platforms, hindering the competitiveness of the virtual labor market and making it less attractive to skilled workers. This research attempts to develop an end-to-end solution for programmable incentive management for hybrid CASs. In particular, it presents a model and framework for execution of programmable incentive mechanisms, and a high-level domain-specific language for encoding complex incentive strategies for socio-technical systems, encouraging a modular approach in building incentive strategies, cutting down development and adjustment time and creating a basis for development of standardized but tweak able incentives. The presented contributions are based on a comprehensive, multidisciplinary review of existing literature on incentives and real-world incentive practices in social computing milieu.