Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Participatory Budgeting Designs for the Real World
Authors: Roy Fairstein, Gerdus Benadè, Kobi Gal
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an extensive empirical study in which 1 800 participants vote in four participatory budgeting elections in a controlled setting to evaluate the practical effects of the choice of voting format and aggregation rule. |
| Researcher Affiliation | Academia | 1 Ben-Gurion University of the Negev, Israel 2 Boston University, USA 3 University of Edinburgh, UK |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Our dataset and code will be made publicly available. The data can be found at https://github.com/rfire01/Participatory-Budgeting-Experiment |
| Open Datasets | Yes | Our dataset and code will be made publicly available. The data can be found at https://github.com/rfire01/Participatory-Budgeting-Experiment |
| Dataset Splits | No | The paper describes a user study and does not mention explicit training/validation/test dataset splits typically found in machine learning contexts. |
| Hardware Specification | No | No specific hardware details (such as GPU/CPU models, memory, or cloud instance types) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, used to replicate the experiment. |
| Experiment Setup | Yes | The user study consists of asking voters to vote using one of the six input formats above in one of four different participatory budgeting elections in a hypothetical city. We recruit roughly 75 different participants for each of the 24 configurations (four elections times six input formats) using Amazon Mechanical Turk, in total just over 1 800 participants. Participants were first presented with a written and video description of the PB voting task. They had to pass a simple quiz about the task in order to proceed. Next, participants carried out the voting task in their allocated PB configuration. Each participant was assigned a (random) location on the city map and shown the description and location of the projects. Participants were rewarded a fixed sum for participation and received a 75% bonus for passing the consistency questions. |