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].
Reasoning about Consensus when Opinions Diffuse through Majority Dynamics
Authors: Vincenzo Auletta, Diodato Ferraioli, Gianluigi Greco
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our goal is to analyze these questions under the lens of algorithm design and computational complexity, by focusing on the setting where agents (e.g., the members of the department) hold binary opinions (e.g., restaurant vs pizzeria), where at each time instant precisely one agent can change her opinion (asynchronous model), and where social relationships are encoded as a graph. |
| Researcher Affiliation | Academia | 1 University of Salerno, Italy 2 University of Calabria, Italy |
| Pseudocode | Yes | Algorithm 1 Solving CONSENSUS[1/2] on input G = (N, E) |
| Open Source Code | No | The paper does not mention providing open-source code for its methodology. |
| Open Datasets | No | This paper is theoretical and does not use or describe datasets. |
| Dataset Splits | No | This paper is theoretical and does not involve data splitting for training, validation, or testing. |
| Hardware Specification | No | This paper is theoretical and does not describe experiments run on specific hardware. |
| Software Dependencies | No | This paper is theoretical and does not describe software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |