Innovative Applications of O.R.
Project rankings for participatory budget based on the fuzzy TOPSIS method

https://doi.org/10.1016/j.ejor.2016.12.044Get rights and content

Highlights

  • TOPSIS method for ranking of participatory budget projects is proposed.

  • Inexact and vague projects descriptions are modeled as fuzzy variables.

  • PIS selection is modified due to relative preferences of the individual participants.

  • Distance measure for category classification is proposed.

  • An example of the ranking is presented.

Abstract

In this study, a fuzzy technique is proposed for order preference based on the similarity to an ideal solution for the personalized ranking of projects in a participatory budget (PB). A PB is a group decision-making process where citizens distribute public resources among a set of city investment proposals. The dynamic growth in the popularity of PB during the last 10 years has been due to a significant increase in the number of projects submitted and the demonstrable weakness of the traditional majority vote. The rationality of decision-makers is restricted by the large number of possible options from which voters can choose only a few within a limited amount of time, and thus there is no opportunity to review all of the projects. Appropriate decision support tools can assist with the selection of the best outcome and help to address the growth of PB processes. 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. The “Technique for Order Preference by Similarity to Ideal Solution” (TOPSIS) method aims to minimize 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 required to the TOPSIS method for PB is that the perfect objective solution does not exists 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. This study describes the application of fuzzy TOPSIS with a modification for PB based on an empirical example from a Poznań PB project (Poland).

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 (1984), 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 Poznań PB project set. In the final section, we discuss the results obtained.

Section snippets

Development

PB has its origins in Latin America but it has recently become widespread. Nelson Dias (2014) identified five stages of PB growth: trial period (local experiments in Brazil, 1989–1997); Brazilian PB (140 municipalities adopted PB, 1997–2000); Latin American and European expansion (2000–2007); national and international PB networks (2007–2008); and “jumping off the scale” (after 2008). At present, we are in the last stage where PB has become part of more complex participatory systems. The

Multi-criteria decision analysis based on the TOPSIS method

One of the most widely used multi-criteria decision analysis methods is the TOPSIS method, which was proposed by Hwang and Yoon in 1981, and extended by Yoon in 1987, as well as by lai Hwang, jou Lai, and yun Liu in 1993. In the TOPSIS method, the optimal alternative is nearest to the positive ideal solution (PIS) and farthest from the negative ideal solution (NIS). A comparison of different methods for the multiple criteria decision problem can be found in Zanakis, Solomon, Wishart, and

TOPSIS for ranking PB projects

In this section, we present a modified fuzzy TOPSIS method for ranking PB projects. The main components for decision making are as follows.

  • The goal is to rank participatory budget projects according to participant first choice (PIS).

  • Decision-makers are city residents and temporarily resident citizens such as students.

  • Alternatives are different projects that could be implemented. The proposals are submitted by citizens and described in a rather general manner.

  • Criteria It is difficult to

Examples from the Poznań project set

We tested the performance of the algorithm based on examples from recent PB projects in Poznań (PO2016). In this process, people proposed 267 (120 citywide and 147 district) projects. The vote count was (total) 73 136, which comprised 52 997 (72.46%) electronic and 20 139 (27.54%) paper votes. The number of valid votes was 66 124 (90.41%). The budget was 15 million PLN6 and it was divided as follows: 5 million PLN for citywide projects and 2 million PLN ×

Summary

The importance of PBs has increased significantly in the last 10 years. However, the sudden and dynamic growth of PBs has highlighted the need for DSSs in this area. The key problem with PBs is that the ranking methods used for projects do no employ quantitative assessment criteria. In this study, we proposed a modified fuzzy TOPSIS method for PBs, which we illustrated using real-world data from Poznań. The application of TOPSIS to PBs required some major changes to the algorithm, i.e., the

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      Citation Excerpt :

      In order to extend the comparative analysis and make the discussion broader to the decision-making science, the results were compared with a multicriteria method that is not based on prospect theory, namely the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method (Hwang & Yoon, 1981). This method was selected since it is one of the most widely used MCDM method (Walczak & Rutkowska, 2017) and also because it is recognized in the literature as having high prediction capacity in comparative studies with others MCDM methods (Caterino, Iervolino, Manfredi & Cosenza, 2009; Kolios et al., 2016; Leoneti, 2016; Thor, Ding & Kamaruddin, 2013; Widianta, Rizaldi, Setyohadi & Riskiawan, 2018; Yeh, 2002). Following the introduction, the second section of the present paper details the original TODIM and TOPSIS methods and their variations.

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