Fair Lotteries for Participatory Budgeting
Authors: Haris Aziz, Xinhang Lu, Mashbat Suzuki, Jeremy Vollen, Toby Walsh
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | considering that PB generalizes classical voting and committee selection problems in social choice theory, it also poses interesting axiomatic and algorithmic research challenges (Aziz and Shah 2021; Rey and Maly 2023). A major effort underway in computational social choice is to design meaningful axioms that capture elusive properties such as fairness and representation, and to design computationally efficient algorithms that satisfy such axioms. One of our main contributions is a randomized algorithm which simultaneously satisfies ex-ante Strong UFS, expost full justified representation (FJR) and ex-post BB1 for PB with binary utilities. |
| Researcher Affiliation | Academia | UNSW Sydney, Australia {haris.aziz, xinhang.lu, mashbat.suzuki, j.vollen, t.walsh}@unsw.edu.au |
| Pseudocode | Yes | Algorithm 1: BW-GCR-PB: Strong UFS and FJR, Algorithm 2: BW-MES-PB: Strong UFS and EJR |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not use or reference any specific datasets, thus no information about public availability or access is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation on datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers, as it focuses on theoretical algorithms rather than implementation details for empirical studies. |
| Experiment Setup | No | The paper is theoretical and does not include details about experimental setup, hyperparameters, or training configurations. |