Evaluation of Project Performance in Participatory Budgeting
Authors: Niclas Boehmer, Piotr Faliszewski, Łukasz Janeczko, Dominik Peters, Grzegorz Pierczyński, Šimon Schierreich, Piotr Skowron, Stanisław Szufa
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Second, we compute our measures for projects from real-life PB instances from Pabulib [Faliszewski et al., 2023] and analyze their behavior. As a side result, we obtain some striking examples of nonmonotonicity of proportionality-oriented PB rules. We also demonstrate the usefulness of our measures by giving a detailed analysis of the Wieliczka election. |
| Researcher Affiliation | Academia | Niclas Boehmer1 , Piotr Faliszewski2 , Łukasz Janeczko2 , Dominik Peters3 , Grzegorz Pierczy nski2,4 , ˇSimon Schierreich5 , Piotr Skowron4 and Stanisław Szufa2,3 1Harvard University 2AGH University, Krak ow 3CNRS, LAMSADE, Universit e Paris Dauphine PSL 4University of Warsaw 5Czech Technical University in Prague |
| Pseudocode | No | The paper describes algorithmic procedures in text and provides a 'Proof sketch (PHRAGMÉN)' section outlining steps for an algorithm, but it does not include a clearly labeled pseudocode or algorithm block/figure. |
| Open Source Code | Yes | The code for our experiments is available at github.com/Project-PRAGMA/ Project-Performance-IJCAI-2024. |
| Open Datasets | Yes | We conduct our experiments on all 551 PB instances with approval votes from Pabulib [Faliszewski et al., 2023] |
| Dataset Splits | No | The paper analyzes real-world participatory budgeting instances but does not describe any specific training, validation, or test dataset splits or their sizes, as it does not involve training a machine learning model in the conventional sense. |
| Hardware Specification | Yes | We ran our experiments on 10 threads of an Intel(R) Xeon(R) Gold 6338 CPU @ 2.00GHz core. |
| Software Dependencies | No | The paper mentions using 'Gurobi' for experiments but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | Yes | To compute the measures, we use the algorithms described in Section 3. For the adding-voters and sampling algorithms, we increase the approval score of the designated project by 1% in each step (repeating each step 100 times for the sampling algorithms). |