Web Applications for Large-Scale Decision Support: Preference Elicitation, Modeling and Visualization

PhD student: Samuel Bohman, DSV

Opponent: Öystein Saebö, University of Agder, Norway

Main supervisor: Aron Larsson, DSV


This thesis addresses the lack of effective and efficient technology design in current e-participation research by investigating two approaches that yet have not been explored to any great extent in the literature: decision science and data visualization. It is concerned with the problem of how to combine techniques from these two fields to achieve decision support in the context of e-participation; from preference elicitation and modeling to data analysis, visualization and final recommendations, such that it can provide value to practitioners. 

The work was carried out in two separate research projects, but which shared a common research strategy: to develop, demonstrate, and evaluate e-participation technologies in real-life settings. The first project was a pilot designed to provide European universities with a web-based e-participation platform to empower students in the Bologna Process. Thirteen universities in Europe participated as end-users of the platform. Using a mixed methods research design, the results showed that ICT is poorly conceptualized in e-participation research and practice, typically conceived informally and simply as tools, independent of the political and social context within which they are developed and used. With regard to sociotechnical challenges in e-participation, the results confirm much of previous research that has underlined the prevalence of technological determinism, institutional resistance, privacy and trust issues, among many other factors. 

In the second project we developed a decision analytic framework for structuring, evaluating, and analyzing stakeholder conflicts in land-use planning. The Municipality of Upplands Väsby in Stockholm, Sweden, participated as a trial. Using agile design principles and methods we implemented the framework as a prototype spatial decision support system using the R programming language. Our prototype shows that a combination of decision science and data visualization has the potential to become a powerful tool in the hands of governments to enable members of society to identify where their differences really matter and where they are unimportant, thus providing structure and new insight to democratic debate. Furthermore, we believe it has the potential to alleviate some of the barriers and limitations associated with traditional methods of community engagement, including distance and time constraints, issues of scale, and high costs.

Link to full thesis