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..
Approximating Bribery in Scoring Rules
Authors: Orgad Keller, Avinatan Hassidim, Noam Hazon
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our algorithm is based on a randomized reduction from bribery to coalitional manipulation (UCM). To solve the UCM problem, we apply the Birkhoff-von Neumann (Bv N) decomposition to a fractional manipulation matrix. This allows us to limit the size of the possible ballot search space reducing it from exponential to polynomial, while still obtaining good approximation guarantees. |
| Researcher Affiliation | Academia | Orgad Keller Department of Computer Science Bar-Ilan University Israel EMAIL Avinatan Hassidim Department of Computer Science Bar-Ilan University Israel EMAIL Noam Hazon Department of Computer Science Ariel University Israel EMAIL |
| Pseudocode | Yes | Algorithm 1: RĪ±-UCM Algorithm. Algorithm 2: RĪ±-bribery Algorithm. |
| Open Source Code | No | The paper does not provide any statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets, therefore no information about dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving dataset splits for training or validation. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments or the hardware used for them. |
| Software Dependencies | No | The paper is theoretical and does not detail specific software dependencies with version numbers for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or system-level training settings. |