Group Decision Making via Weighted Propositional Logic: Complexity and Islands of Tractability
Authors: Gianluigi Greco, Jerome Lang
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We focus on the complexity of finding optimal solutions, and we identify the tractability boarder between polynomial and NP-hard settings, along several parameters: the syntax of formulas, the allowed weights, as well as the number of agents, propositional symbols, and formulas per agent. The goal of the paper is to fill this gap, by depicting a clear picture of the complexity of collective decision making with weighted formulas under egalitarianism and by contrasting it with results that hold under utilitarianism. |
| Researcher Affiliation | Academia | Gianluigi Greco Universit a della Calabria, Italy ggreco@mat.unical.it J erˆome Lang Universit e Paris-Dauphine, France lang@lamsade.dauphine.fr |
| Pseudocode | No | The paper does not contain any structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper is theoretical and focuses on complexity analysis. It does not mention or provide any links to open-source code for the described methodology. |
| Open Datasets | No | This paper is theoretical and focuses on complexity analysis rather than empirical experiments. Therefore, it does not describe the use of any datasets for training or provide information about their public availability. |
| Dataset Splits | No | This is a theoretical paper that does not conduct empirical experiments with datasets. Therefore, no specific dataset split information (training, validation, or test) is provided. |
| Hardware Specification | No | This is a theoretical paper and does not describe any experimental hardware specifications or computational resources used for running experiments. |
| Software Dependencies | No | This is a theoretical paper and does not provide specific ancillary software details with version numbers (e.g., libraries, solvers) needed to replicate experiments. |
| Experiment Setup | No | This is a theoretical paper and does not involve empirical experiments with specific setup details like hyperparameters or training configurations. |