Participatory Budgeting: Data, Tools and Analysis
Authors: Piotr Faliszewski, Jarosław Flis, Dominik Peters, Grzegorz Pierczyński, Piotr Skowron, Dariusz Stolicki, Stanisław Szufa, Nimrod Talmon
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper we take a step towards understanding how various selection rules for participatory budgeting operate in practice. We do this by releasing and analysing data from over 650 PB elections, mainly conducted in Poland.1 Our contribution is the following: We apply our tools on the collected data and perform an extensive analysis that compares different voting rules for PB. We defer the detailed description of the Pabulib data format as well as additional figures with the results of our analysis to the full version of the paper [Faliszewski et al., 2023]. In our first set of simulations, we compare the three different approaches to making the Method of Equal Shares exhaustive. In our second set of simulations we compare the Add1U variant of Equal Shares with the Utilitarian Greedy rule. |
| Researcher Affiliation | Academia | Piotr Faliszewski1 , Jarosław Flis2 , Dominik Peters3 , Grzegorz Pierczy nski4 , Piotr Skowron4 , Dariusz Stolicki2,5 , Stanisław Szufa1 and Nimrod Talmon6 1AGH University 2Jagiellonian University in Krakow 3CNRS, LAMSADE Universit e Paris Dauphine PSL 4University of Warsaw 5Center for Quantitative Political Science 6Ben-Gurion University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | PABUTOOLS. This is a Python library providing a parser of Pabulib files and implementations of selected rules for participatory budgeting. The library is accessible via Py PI (https://pypi.org/project/pabutools/). |
| Open Datasets | Yes | PABULIB. This is a library of participatory budgeting data (PArticipatory BUdgeting LIBrary), and can be accessed via the following URL: http://pabulib.org. |
| Dataset Splits | No | The paper describes the data sources and how they are aggregated (e.g., citywide vs. districtwise schemes) but does not provide specific details on how the dataset is split into training, validation, or test sets for model development or evaluation, as this is an analysis paper, not a model training paper. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments or analyses. |
| Software Dependencies | No | The paper mentions 'PABUTOOLS. This is a Python library' but does not specify the version of Python or any other software dependencies with their version numbers required for replication. |
| Experiment Setup | No | The paper describes the types of data used, the rules compared (Utilitarian Greedy, Method of Equal Shares with completions), and the metrics applied. However, it does not provide specific experimental setup details such as hyperparameters, specific simulation parameters, or system-level training configurations typically found in model-based experimental setups. |