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..
Convergence of Opinion Diffusion is PSPACE-Complete
Authors: Dmitry Chistikov, Grzegorz Lisowski, Mike Paterson, Paolo Turrini7103-7110
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the algorithmic properties of the fixed-point behaviour of such networks, showing that the problem of establishing whether individuals converge to stable opinions is PSPACE-complete.Our contribution is two-fold: firstly, we present some classes of networks which are guaranteed to converge, and secondly we show that the problem of establishing whether a network converges is PSPACE-complete even for the simplest of such protocols, closing a gap in the literature. |
| Researcher Affiliation | Academia | Dmitry Chistikov,1 Grzegorz Lisowski,1 Mike Paterson,1 Paolo Turrini1 1Department of Computer Science, University of Warwick, United Kingdom EMAIL |
| Pseudocode | No | The paper includes figures illustrating network gadgets (e.g., AND, NOT, NOP gates) and constructions, but it does not present any formal pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention or provide access to any open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments using datasets. Thus, it does not mention any publicly available or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data. Therefore, it does not specify any training/test/validation dataset splits. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup or the specific hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any software implementations or their dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on complexity proofs rather than empirical experimentation. Therefore, it does not describe any experimental setup details, hyperparameters, or training settings. |