Input datasets, source codes, and outcomes related to "Beikverdi et al. (2023): A Bi-level Model for District-fairness Participatory Budgeting: Decomposition Methods and Application" (submitted)
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Templates, datasets, source codes, and outcomes for the submitted article: "A Bi-level Model for District-fairness Participatory Budgeting: Decomposition Methods and Application" by Majid Beikverdi (m.beikverdi@gmail.com), Nasim Ghanbar Tehrani, Kamran Shahanaghi - Department of Industrial Engineering, Kharazmi University, Tehran, Iran - Department of Industrial Engineering, University of Science & Technology, Tehran, Iran
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Topics: The impact of bi-level programming and decomposition approaches on participatory budgeting Methods: - Bi-level programming - Benders decomposition algorithm - Meta-heuristic (PSO & GSA) Findings: Modeling leader-follower interactions and applying decomposition approaches effectively increase the utility obtained for all districts and reduce the computation volume. District-fairness axioms can be used in this approach to move towards social justice. Conclusion: The participation of citizens in the redistribution of urban resources alone is not enough to eliminate social exclusion. The existing social inequalities resulting from the previous behaviors of resource allocation should be considered, and technical constraints should be defined to direct resources towards less privileged districts and prevent the elite capture. Neglecting the mentioned issues may lead to the reproduction of social inequalities through long-term participatory mechanisms. Accordingly, analyzing leader-follower interactions using bi-level programming and decomposition approaches helps increase social justice in participatory budgeting programs. Help: Need help with reproducing research results? - M.beikverdi@gmail.com