Participatory Budgeting with Project Groups

Authors: Pallavi Jain, Krzysztof Sornat, Nimrod Talmon, Meirav Zehavi

IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We study the computational complexity of identifying project bundles that maximize voter satisfaction while respecting all budget limits. We show that the problem is generally intractable and describe efficient exact algorithms for several special cases, including instances with only few groups and instances where the group structure is close to being hierarchical, as well as efficient approximation algorithms.
Researcher Affiliation Academia 1Indian Institute of Technology Jodhpur, India 2MIT CSAIL, USA 3Ben-Gurion University, Israel
Pseudocode No The paper describes algorithmic ideas and procedures in prose, particularly within the proofs (e.g., Lemma 11, Theorem 22), but it does not include formally structured pseudocode blocks or labeled algorithm listings.
Open Source Code No The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available.
Open Datasets No This paper is theoretical and focuses on computational complexity and algorithm design; it does not involve the use of datasets for training or evaluation.
Dataset Splits No This paper is theoretical and focuses on computational complexity and algorithm design; it does not involve the use of datasets with training/test/validation splits.
Hardware Specification No The paper is theoretical and focuses on computational complexity and algorithm design; it does not describe empirical experiments that would require specifying hardware used for computation.
Software Dependencies No The paper is theoretical and focuses on computational complexity and algorithm design; it does not provide specific software dependencies with version numbers required to replicate any experiments.
Experiment Setup No The paper is theoretical and focuses on computational complexity and algorithm design; it does not describe empirical experiments with specific setup details or hyperparameters.