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..
Bounding the Inefficiency of Compromise
Authors: Ioannis Caragiannis, Panagiotis Kanellopoulos, Alexandros A. Voudouris
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formulate simple games that capture this behavior and quantify the inefficiency of equilibria using the well-known notion of the price of anarchy. Our results indicate that compromise comes at a cost that strongly depends on the neighborhood size.Technical contribution. We study questions related to the existence, computational complexity, and quality of equilibria in k-COF games. We show that there exist simple 1-COF games that do not admit pure Nash equilibria and, furthermore, that even in games where equilibria exist, their quality may be suboptimal, i.e., the price of stability (defined in [Anshelevich et al., 2008]) is strictly greater than 1. |
| Researcher Affiliation | Academia | Ioannis Caragiannis University of Patras EMAIL Panagiotis Kanellopoulos CTI & University of Patras EMAIL Alexandros A. Voudouris University of Patras EMAIL |
| Pseudocode | No | The paper describes algorithms conceptually (e.g., 'standard algorithms for computing shortest or longest paths in directed acyclic graphs') but does not provide any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use any datasets, thus no information regarding public dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, therefore it does not discuss training/validation/test splits. |
| Hardware Specification | No | The paper focuses on theoretical contributions and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper focuses on theoretical contributions 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 settings. |