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: 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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Abstract: Corruption frequently occurs in many aspects of multi-party interaction between private agencies and government employees. Past works studying corruption in a lab context have explicitly included covert or illegal activities in participants’ strategy space or have relied on surveys like the Corruption Perception Index (CPI). This paper studies corruption in ecologically realistic settings in which corruption is not suggested to the players a priori but evolves during repeated interaction. We ran studies involving hundreds of subjects in three countries: China, Israel, and the United States. Subjects interacted using a four-player board game in which three bidders compete to win contracts by submitting bids in repeated auctions, and a single auctioneer determines the winner of each auction. The winning bid was paid to an external “government” entity, and was not distributed among the players. The game logs were analyzed posthoc for cases in which the auctioneer was bribed to choose a bidder who did not submit the highest bid. We found that although China exhibited the highest corruption level of the three countries, there were surprisingly more cases of corruption in the U.S. than in Israel, despite the higher PCI in Israel as compared to the U.S. We also found that bribes in the U.S. were at times excessively high, resulting in bribing players not being able to complete their winning contracts. We were able to predict the occurrence of corruption in the game using machine learning. The significance of this work is in providing a novel paradigm for investigating covert activities in the lab without priming subjects, and it represents a first step in the design of intelligent agents for detecting and reducing corruption activities in such settings.

Citation: Ya’akov Gal, Avi Rosenfeld, Sarit Kraus, Michele Gelfand, Bo An and Jun Lin. A new paradigm for the study of corruption across cultures. International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction (SBP), Maryland, MD, April 2014.

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The 6th International Conference on Social Informatics (SocInfo 2014) will be held in Barcelona, Spain, from November 10th to November 13th. SocInfo is an interdisciplinary venue for researchers from Computer Science, Informatics, Social Sciences and Management Sciences to share ideas and opinions, and present original research work on studying the interplay between socially-centric platforms and social phenomena. The ultimate goal of Social Informatics is to create better understanding of socially-centric platforms not just as a technology, but also as a set of social phenomena. To that end, they are inviting interdisciplinary papers, on applying information technology in the study of social phenomena, on applying social concepts in the design of information systems, on applying methods from the social sciences in the study of social computing and information systems, on applying computational algorithms to facilitate the study of social systems and human social dynamics, and on designing information and communication technologies that consider social context.

This year’s special purpose of the conference is to to bridge the gap between the social sciences and computer science. SocInfo aspires to a conference that is equally attractive to computer scientists and social scientists by putting emphasis on the methodology needed in the field of computational social science to reach long-term research objectives.

The event will also offer tutorials, workshops and keynote talks that will be tailored to address the collaboration between the two research cultures in an era when social interactions are ubiquitous and span offline, online and augmented reality worlds.

Visit their official site for information on the Calls for Papers and Workshops, research areas and more.

Important dates

Full paper submission: August 8, 2014 (23:59 Hawaii Standard Time)
Notification of acceptance: October 3, 2014
Submission of final version: October 10, 2014
Conference dates: November 10-13, 2014

Collective Intelligence 2014 is an interdisciplinary conference seeking to bring together researchers from a variety of fields relevant to understanding and designing collective intelligence of many types.

The conference will take place at MIT and consist of:

• Invited talks from prominent researchers in different areas related to collective intelligence such as engineering, psychology, management, political science, information science, and sociology
• Oral presentations
• Poster/Demo sessions
• “Ignite” sessions in which practitioners (e.g. policy makers) connect with researchers around collective-intelligence-based solutions to real-world problems

IMPORTANT DATES

• Extended abstract submission deadline:  January 15, 2014
• Notification of acceptance / rejection:  February 15, 2014
• Conference dates:  June 10-12, 2014

Also see Collective Intelligence 2012.

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.

Download: http://bit.ly/1p8SJOP

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.

Download: http://bit.ly/1SnngGh

Abstract. To date, on-demand provisioning models of human-based services in the cloud are mainly used to deal with simple human tasks solvable by individual compute units (ICU). In this paper, we propose a framework allowing the provisioning of a group of people as an execution service unit, a so-called Social Compute Unit (SCU), by utilizing clouds of ICUs. Our model allows service consumers to specify quality requirements, which contain constraints and objectives with respect to skills, connectedness, response time, and cost. We propose a solution model for tackling the problem in quality-aware SCUs provisioning and employ some metaheuristic techniques to solve the problem. A prototype of the framework is implemented, and experiments using data from simulated
clouds and consumers are conducted to evaluate the model.

Keywords: human-based service, social compute unit, quality of service, service cloud management.

doi: http://dx.doi.org/10.1007/978-3-642-45005-1_22

Citation: Muhammad Zuhri Catur Candra, Hong-Linh Truong, Schahram Dustdar, “Provisioning Quality-Aware Social Compute Units in the Cloud”, the 11th International Conference on Service Oriented Computing. Berlin, Germany, 2-5 December, 2013.

Download: http://bit.ly/1XCWOXo

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.

Download: http://bit.ly/1QfQVcO