Participatory Budgeting Designs for the Real World
Authors: Roy Fairstein, Gerdus Benadè, Kobi Gal
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | 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. |