Automated Incentive Management for Social Computing

Human participation in socio-technical systems is overgrowing conventional crowdsourcing 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. Controllability of such systems requires different direct and indirect methods of influencing the participating humans. Conventional human organizations, such as companies or institutions, have been using incentives for decades to align the interests of workers and organizations. With the collaborations managed by the socio-technical platforms growing ever more complex and resembling, or even surpassing in complexity, the conventional ones, there is a need to apply advanced incentivizing techniques in the virtual environment as well. However, existing incentive management techniques in use in crowdsourcing/sociotechnical platforms are not suitable for the described (complex or intellectually-challenging) tasks. In addition, existing platforms currently use custom-developed solutions. 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 presents a complete set of models and tools for programmable incentive management for social computing platforms. In particular, it introduces: (i) A comprehensive, multidisciplinary review of existing literature on incentives as well as an extensive survey of real-world incentive practices in social computing milieu, (ii) A low-level model of incentives suitable for use in socio-technical systems (iii) princ – a model and framework for execution of programmable incentive mechanisms, allowing the offering of incentives through a service model. (iv) pringl – 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 tweakable incentives. The tools are meant to allow system and incentive designers a complete environment for modeling, administering/executing and adjusting a whole spectrum of realistic incentive mechanisms in a privacy-preserving manner. No known comparable systems were known to exist at the time of writing of the thesis.