Participatory Budget Project Selection Considering District Fairness: Two-Stage Large-Scale Multiattribute Group Decision Making Method

Participatory budgeting (PB) allows citizens with different backgrounds to participate directly in the decision-making process of budgeting and fund allocation. It can be considered as a multiattribute decision-making (MADM) process, which enhances the fairness, transparency, and effectiveness of decision-making. However, with the increasing number of participants and the complexity of the decision-making environment, how to effectively manage and optimize the selection of projects for large-scale PB with fairness has become a major challenge. To address this issue, this article proposes a two-stage MADM consensus method for large-scale participatory budget project selection problems. In the first stage, the ISODATA algorithm segments large participatory districts into subclusters, followed by an iterative algorithm based on group contribution theory to generate subcluster decision matrices. In the second stage, each subcluster is treated as a new participant, with district fairness considered to refine decision-making power through iterative adjustments, resulting in more manageable decisions. Finally, the iterative consensus-reaching algorithm is applied again to get the final group decision result. The applicability of the proposed methodology is verified through a case study in the Poland participant city project selection budget and comparative analysis. The result demonstrates that the methodology can integrate the preferences of different districts, facilitate budget consensus reaching, and improve the misrepresentations and fairness of decision-making.