Abstract: 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.

Citation: PhD Thesis: Ognjen Scekic: Automated Incentive Management for Social Computing – Foundations, Models, Tools and Algorithms, TU Wien, March 2016.

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Abstract: Modern advances of computing systems allow humans to participate not only as service consumers but also as service providers, yielding the so-called human-based computation. In this paradigm, some computational steps to solve a problem can be outsourced to humans. Such an interweaving of humans and machines as compute units can be observed in various computing systems, such as collective intelligence systems, Process-Aware Information Systems (PAISs) with human tasks, and Cyber-Physical-Social Systems (CPSSs). Even with the multitude realizations of such systems — herein we refer to as Hybrid Human-Machine Computing System (HCS) — yet we still lack important building blocks to develop an HCS, where humans and machines are both considered as first class problem solvers from the ground up. These building blocks should tackle issues arise from different phases of an HCS’ lifecycle, i.e., pre-runtime, runtime, and post-runtime. Each phase introduces unique challenges, mainly due to the diversity of the involved compute units, which bring in different characteristics and behaviors that need to be taken into consideration. This thesis contributes to some important building blocks in managing HCSs’ lifecycle: the provisioning of compute units, the monitoring of the running system, and the reliability analysis of the task executions.
Our first contribution deals with the quality-aware provisioning of a group of compute units, a so-called compute units collective, by discovering and composing compute units obtained from various sources either on-premise or in the Cloud. We propose a novel solution model for tackling the problem in the quality-aware provisioning of compute units collectives, and employ some heuristic techniques to solve the problem. Our approach allows service consumers to specify quality requirements, which contain constraints and optimization objectives with respect to functional capabilities and non-functional properties.
In our second contribution, we develop a monitoring framework for capturing and analyzing runtime metrics occurring on various facets of HCSs. This framework is developed based on metric models, which deals with diverse compute units. Our approach also utilizes Quality of Data (QoD) to enable elastic monitoring catering different monitoring needs.
While the reliability analysis for machine-based compute units has been widely developed, the reliability analysis for HCSs has not been extensively studied. In our final
contribution, we present models and a framework for analyzing the reliability of compute units collectives.

Citation: PhD Thesis: Muhammad Z. C. Candra: Hybrid Human-Machine Computing Systems – Provisioning, Monitoring, and Reliability Analysis, TU Wien, June 2016.

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