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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Ceteris paribus majority for social ranking
Authors: Adrian Haret, Hossein Khani, Stefano Moretti, Meltem Öztürk
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of finding a social ranking over individuals or objects given a ranking over coalitions formed by them. We investigate the use of a ceteris paribus majority principle as a social ranking solution from classical axioms of social choice theory. Faced with a Condorcet-like paradox, we analyze the consequences of restricting the domain according to an adapted version of single-peakedness. We conclude with a discussion on different interpretations of incompleteness of the ranking over coalitions and its exploitation for defining new social rankings, providing a new rule as an example. |
| Researcher Affiliation | Academia | 1 TU Wien, Institut f ur Logic and Computation 192-02, Favoritenstraße 9-11, 1040 Wien, Austria 2 Universit e Paris-Dauphine, PSL Research University, CNRS, LAMSADE, Place du Mar echal de Lattre de Tassigny, F-75775 Paris cedex 16, France |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper; it provides illustrative examples but does not use datasets for training, validation, or testing. |
| Dataset Splits | No | This is a theoretical paper and does not involve training, validation, or test splits of data. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |