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 [1].
Not in My Backyard! Temporal Voting Over Public Chores
Authors: Edith Elkind, Tzeh Yuan Neoh, Nicholas Teh
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We investigate the computational complexity of optimizing utilitarian and egalitarian welfare. Our results show that while optimizing the former is computationally straightforward, minimizing the latter is computationally intractable, even in very restricted cases. Nevertheless, we identify several settings where this problem can be solved efficiently, either exactly or by an approximation algorithm. We also examine the effects of enforcing temporal fairness and its impact on social welfare, and analyze the competitive ratio of online algorithms. We then explore the strategic behavior of agents, providing insights into potential malfeasance in such decision-making environments. Finally, we discuss a range of fairness measures and their suitability for our setting. |
| Researcher Affiliation | Academia | 1Northwestern University, USA 2Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore 3Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore 4University of Oxford, UK |
| Pseudocode | No | The paper describes algorithms in text, such as the greedy algorithm for MIN-SUM in Section 3.1 and an approximation algorithm in Section 3.7, but it does not present them in structured pseudocode blocks or clearly labeled algorithm figures. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code, provide links to repositories, or mention code in supplementary materials for the described methodology. |
| Open Datasets | No | This paper is theoretical research focusing on computational complexity and algorithms for a temporal voting model. It does not involve empirical experiments using datasets, and therefore, no datasets are mentioned as being publicly available or open. |
| Dataset Splits | No | The paper is theoretical research and does not involve empirical evaluation on datasets. Therefore, there is no discussion of dataset splits for training, testing, or validation. |
| Hardware Specification | No | The paper presents theoretical work on computational complexity and algorithms. It does not describe any experiments that would require specific hardware for execution, and thus, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical, focusing on computational complexity and algorithm design. It does not describe implementation details or require specific software dependencies with version numbers for reproducibility of experiments. |
| Experiment Setup | No | The paper is theoretical in nature, focusing on algorithm design, computational complexity, and proofs. It does not describe any experimental setup, hyperparameters, or training configurations. |