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].
The Complexity Landscape of Outcome Determination in Judgment Aggregation
Authors: Ulle Endriss, Ronald de Haan, Jérôme Lang, Marija Slavkovik
JAIR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a comprehensive analysis of the computational complexity of the outcome determination problem for the most important aggregation rules proposed in the literature on logic-based judgment aggregation. ... Our analysis applies to several different variants of the basic framework of judgment aggregation that have been discussed in the literature, as well as to a new framework that encompasses all existing such frameworks in terms of expressive power and representational succinctness. ... These findings provide important insights into the mechanics of judgment aggregation and can offer guidance for the development of practical algorithms. In particular, they indicate what existing tools for combinatorial optimisation, such as Answer Set Programming or SAT solvers, can potentially be used to implement judgment aggregation solvers (see also Section 5). |
| Researcher Affiliation | Academia | Ulle Endriss EMAIL Ronald de Haan EMAIL Institute for Logic, Language and Computation University of Amsterdam, The Netherlands Jérôme Lang EMAIL CNRS, LAMSADE, PSL, Paris-Dauphine University, France Marija Slavkovik EMAIL Department of Information Science and Media Studies University of Bergen, Norway |
| Pseudocode | No | The paper describes algorithms and problem reductions but does not contain any explicitly labeled pseudocode or algorithm blocks. It focuses on theoretical proofs and complexity analysis. |
| Open Source Code | No | The paper does not provide any concrete access information for source code. It discusses complexity results and potential implementations using existing tools like Answer Set Programming or SAT solvers, but does not release specific code for the described methodology. The closest mention is in the conclusion: "Research along these lines has been initiated very recently, encoding the outcome determination problem for various judgment aggregation rules into the automated reasoning framework of Answer Set Programming (De Haan & Slavkovik, 2019)." This refers to another paper about encoding, not the release of code for *this* paper's work. |
| Open Datasets | No | The paper is theoretical in nature and does not describe or use any datasets in its analysis or proofs. Therefore, no information about open datasets is provided. |
| Dataset Splits | No | The paper is theoretical and focuses on computational complexity analysis. It does not describe experiments that would require dataset splits. |
| Hardware Specification | No | The paper is a theoretical work on computational complexity and does not describe any experimental setup that would involve specific hardware for running experiments. |
| Software Dependencies | No | The paper is theoretical, analyzing computational complexity, and does not mention specific software or library versions used for implementation or experimentation. It does mention potential tools like "Answer Set Programming or SAT solvers" but without specific versions or as dependencies for its own methodology. |
| Experiment Setup | No | The paper is a theoretical work on computational complexity and does not describe any experimental setup, hyperparameters, or training configurations. |