1. Introduction

A participatory budget (PB) is a group decision-making process where citizens distribute public resources among a set of proposed projects. PB is highly beneficial for multiple parties because: it enables people to shape the local budget, municipalities obtain clear information about social priorities, it helps to integrate local communities and motivates them to cooperate, it educates citizens about costs, and it constrains local investments. All of these benefits have helped PB to grow in terms of the number of processes and budget limits. The present study investigated Polish PBs. Based on this study, we can describe a typical PB in Poland according to four steps: (1) a city announces the PB; (2) citizens propose projects; (3) the city verifies the proposals and formulates the final ballots; and, finally, (4) the citizens vote for projects. We found that major Polish cities included more than 100 projects in their ballots and people only had to choose 3-7, so the winners were usually selected by majority rule. However, this method causes high dispersion of the votes among multiple alternatives, where large numbers of people may vote for less popular projects and the process is completed without any project winning. Despite those issues majority rule has great advantage – it is easy to understand and scale. Complicated decision support systems could solve money distribution problem but people would lost trust to the system. We see our solution as a recommendation system that helps people with information overload during the voting. According to Malhotra [1], negative effects start with 10 or more options while in PB we have around 100 options. Recommendation system helps people to get familiar with potentially interesting projects instead of scanning all titles. Final solution should rank projects by different criteria: category, potential beneficiaries, location and cost. Final decision belongs to the participant. In order to build such a system for PB, an algorithm is essential for ranking projects, which was the focus of the present study. Thus, we propose automated comparisons of PB projects using the “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) method. The ranking of PB projects is a specific problem because multi-criteria comparisons are based on non-quantitative criteria, i.e., nominal and fuzzy criteria such as topic, location, and beneficiaries. The TOPSIS method minimizes the distance to the ideal alternative while maximizing the distance to the worst. In a fuzzy extension of TOPSIS, the ratings of alternatives and the weights of the criteria are fuzzy numbers or linguistic variables. The major modification of the TOPSIS method required for PB is that the objective perfect solution does not exist among the maximum and minimum values for the criteria. Thus, the subjective choice is the ideal solution for the decision maker and the negative ideal solution is the most dissimilar solution. The remainder of this paper is organized as follows. First, we briefly describe the PBs. Next, we present an overview of DSS systems and fuzzy TOPSIS with preliminary definitions. In Section 4, we describe the application of the modified TOPSIS method to PB projects. We then present examples based on the Poznan PB project set. In the final section, we discuss the results obtained.



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