Abstract: Novel types of crowdsourcing systems require a wider spectrum of incentives for efficient motivation and management of human workers taking part in complex collaborations. Incentive management techniques used in conventional crowdsourcing platforms are not suitable for more intellectually-challenging tasks. Currently, incentives are custom-developed and managed by each particular platform. This prevents incentive portability and cross-platform comparison. In this paper we present PRINGL – a domain-specific language for programming and managing complex incentive strategies for socio-technical platforms in general. It promotes re-use of proven incentive logic and simplifies modeling, adjustment and enactment of complex incentives for socio-technical systems. We demonstrate its applicability and expressiveness on a set of realistic use-cases and discuss its properties.

Citation: Ognjen Scekic, Hong Linh Truong, Schahram Dustdar: PRINGL – A domain-specific language for incentive management in crowdsourcing. Computer Networks 90: 14-33(2015).

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Abstract: Novel web-based socio-technical systems require incentives for efficient management and motivation of human workers taking part in complex collaborations. Incentive management techniques used in existing crowdsourcing platforms are not suitable for intellectually-challenging tasks; platform-specific solutions prevent both workers from comparing working conditions across different platforms as well as platform owners from attracting skilled workers. In this paper we present PRINGL, a domain-specific language for programming complex incentive strategies. It promotes re-use of proven incentive logic and allows composing of complex incentives suitable for novel types of socio-technical systems. We illustrate its applicability and expressiveness and discuss its properties and limitations.

Citation: Ognjen Scekic, Hong-Linh Truong, Schahram Dustdar, “Managing Incentives in Social Computing Systems with PRINGL”, 15th Intl. Conf. on Web Information Systems Engineering (WISE), Thessaloniki, Greece, October, 2014.

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This work was presented at HAIDM 2015. The 2015 workshop on Human-Agent Interaction Design and Models was co-organised by SmartSociety.

Abstract: We use provenance graphs to solve a problem within incentive engineering: motivating humans to accept proposals generated by agents. Across several provenance graphs created within the HAC-ER disaster-management system, we ran retrospectively a bespoke algorithm for subgraph matching in order to extract narrative information from the provenance data. The output of the algorithm comprised a series of text messages which, had they been generated at the time of the disaster trial, would have been transmissable with the specific intention of encouraging participants not to reject certain tasks.

The algorithm found all expected subgraphs within the provenance graphs, on an any-time basis and in a time linearly proportional to the number of nodes. Our algorithm is extendable to other situations in which agents present tasks to humans.

Keywords: Incentive engineering, subgraph matching, task allocation, human-agent collectives, provenance graphs, disaster management.

Citation: Mark Ebden, Trung Dong Huynh, Luc Moreau and Stephen Roberts. Incentive Engineering through Subgraph Matching – with Application to Task Allocation.

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Abstract. Information systems are becoming ever more reliant on different forms of social computing, employing individuals, crowds or assembled teams of professionals. With humans as first-class elements, the success of such systems depends heavily on how well we can motivate people to act in a planned fashion. Incentives are an important part of human resource management, manifesting selective and motivating effects. However, support for defining and executing incentives in today’s information systems is underdeveloped, often being limited to simple, per-task cash rewards. Furthermore, no systematic approach to program incentive functionalities for this type of platforms exists.

In this paper we present fundamental elements of a framework for programmable incentive management in information systems. These elements form the basis necessary to support modeling, programming, and execution of various incentive mechanisms. They can be integrated with different underlying systems, promoting portability and reuse of proven incentive strategies. We carry out a functional design evaluation by illustrating modeling and composing capabilities of a prototype implementation on realistic incentive scenarios.

Keywords: rewards, incentives, social computing, crowdsourcing.

doi: http://dx.doi.org/10.1007/978-3-642-38709-8_44

Citation: Ognjen Scekic, Hong-Linh Truong, Schahram Dustdar, “Programming Incentives in Information Systems”, 25th International Conference on Advanced Information Systems Engineering(CAiSE’13), Springer-Verlag, Valencia, Spain, 17-21 June, 2013.

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Abstract. 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.

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

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

Citation: Ognjen Scekic, Christoph Dorn, Schahram Dustdar, “Simulation-Based Modeling and Evaluation of Incentive Schemes in Crowdsourcing Environments”, 21st International Conference on Cooperative Information Systems (CoopIS’13), September 11-13, 2013, Graz, Austria.

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Introduction. Incentives and rewards help align the interests of employees and organizations. They first appeared with the division of labor and have since followed the increasing complexity of human labor and organizations. As a single incentive measure always targets a specific behavior and sometimes additionally induces unwanted responses from workers, multiple incentives are usually combined to counteract the dysfunctional behavior and produce  desired results. Numerous studies have shown the effectiveness of different incentive mechanisms and their selective and motivational effects. Their importance is reflected in the fact that most big and mid-size companies employ some kind of incentive measures.

Expansion of social computing will include not only better exploitation of crowdsourcing but also solutions that extend traditional business processes; increasing research interest seems to confirm the trend. Several frameworks aiming to support such new collaboration models are being developed (such as socially enhanced computing). These new forms of social computing are intended to support greater task complexity, more intelligent task division, complex organizational and managerial structures for virtual teams, and virtual “careers.” With envisioned changes, incentives will also gain importance and complexity to address workers’ dysfunctional behavior. This new emphasis calls for automated ways of handling incentives and rewards. However, the social computing market is dominated by flat and short-lived organizational structures, employing a limited number of simple incentive mechanisms. That is why we view the state of the social computing market as an opportunity to add novel ways of handling incentives and rewards.

Here, we analyze incentive mechanisms and suggest how they can be used for next-generation social computing. We start with a classification of incentive mechanisms in the literature and in traditional business organizations, then identify elements that can be used as building blocks for any composite incentive mechanism and show the same elements are also used in social computing, even though the resulting schemes lack the complexity needed to support advanced business processes; we conclude with our vision
for future developments.

doi: http://doi.acm.org/10.1145/2461256.2461275

Citation: Ognjen Scekic, Hong-Linh Truong, Schahram Dustdar, “Incentives and Rewarding in Social Computing”, Communications of the ACM, Vol. 65, No. 6, pp. 72-82.

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