Abstract
The complexity of democratic public decision-making has augmented with the surge of volumes of information. One response is using digital tools in decision-making processes to enable more flexibility than the conventional yes or no responses. We proposed a solution for a large-scale democratic decision-making process using soft computing and fuzzy logic, based on systemic consensus and action design research. Our integrative fuzzy decision-making process was designed to allow the consideration of all the arguments, the deliberation of all the alternatives, and the assessment of each alternative using comparative linguistic expressions. The method was used to resolve a conflict-generating traffic problem in Geuensee, a municipality in the Swiss canton of Luzern. The citizens voted on two dimensions of resistance and support about each of the proposed alternatives. The results were computed using fuzzy membership functions and a fuzzy logic table, evaluated with different computational variants. The output was a ranking of the best options, as assessed by the decision-makers. We found that this method for smart participation of citizens was accepted and generated involvement, leading to an effective outcome for the decision-makers. In the last section, we discuss evaluation and ethical considerations.
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Acknowledgements
The authors are grateful to the people involved in the Unterdorf St. project. Further thanks go to Mathias Schmid, Dominik Huber, Maurizio Hofstetter and the two HSLU teams (Fabio Stecher, Daniele Muheim, Nicole Zimmermann and Corinne Spoering) coordinated by Emmenegger, B., HSLU.
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Emmenegger, B., Bigea, G., Portmann, E. (2023). Applied Large-Scale Group Decision Making Using Systemic Consensus and Fuzzy Method of Comparative Linguistic Expressions. In: Massanet, S., Montes, S., Ruiz-Aguilera, D., González-Hidalgo, M. (eds) Fuzzy Logic and Technology, and Aggregation Operators. EUSFLAT AGOP 2023 2023. Lecture Notes in Computer Science, vol 14069. Springer, Cham. https://doi.org/10.1007/978-3-031-39965-7_11
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