Approximating Bribery in Scoring Rules
Authors: Orgad Keller, Avinatan Hassidim, Noam Hazon
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 orgad.keller@gmail.com Avinatan Hassidim Department of Computer Science Bar-Ilan University Israel avinatan@cs.biu.ac.il Noam Hazon Department of Computer Science Ariel University Israel noamh@ariel.ac.il |
| Pseudocode | Yes | Algorithm 1: Rα-UCM Algorithm. Algorithm 2: Rα-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. |