Incomplete Preferences in Single-Peaked Electorates
Authors: Zack Fitzsimmons, Martin Lackner
JAIR 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented our consecutive ones-based algorithm for the recognition of possibly single-peaked preferences for weak orders (and the extensions to the construction for single-plateaued and Black single-peaked preferences). The code is written in Python 3.7 and is available at https://github.com/zmf6921/incompletesp. We ran the algorithm on all currently available profiles of weak orders available on Pref Lib.org (Mattei & Walsh, 2013); these are in total 379 toc-instances6. We found that none of these instances are possibly single-peaked. ... On a more positive note, even large instances with > 1,000 candidates and instances with > 25,000 distinct votes could be solved in seconds (the maximum running time was 20.2 seconds, the average was 0.8 seconds, the median 0.02 seconds, processed on a 4GHz Intel i7 with 16 GB of RAM). |
| Researcher Affiliation | Academia | Zack Fitzsimmons EMAIL College of the Holy Cross Department of Mathematics and Computer Science One College Street, Worcester, MA 01610, USA Martin Lackner EMAIL TU Wien Databases and Artificial Intelligence Group Favoritenstraße 9-11, 1040 Vienna, Austria |
| Pseudocode | Yes | Algorithm 1: The Guided Algorithm |
| Open Source Code | Yes | We implemented our consecutive ones-based algorithm for the recognition of possibly single-peaked preferences for weak orders (and the extensions to the construction for single-plateaued and Black single-peaked preferences). The code is written in Python 3.7 and is available at https://github.com/zmf6921/incompletesp. |
| Open Datasets | Yes | We ran the algorithm on all currently available profiles of weak orders available on Pref Lib.org (Mattei & Walsh, 2013); these are in total 379 toc-instances6. |
| Dataset Splits | No | The paper runs algorithms on existing profiles from Pref Lib.org but does not describe any training/test/validation splits as it does not involve training models in the traditional sense. |
| Hardware Specification | Yes | On a more positive note, even large instances with > 1,000 candidates and instances with > 25,000 distinct votes could be solved in seconds (the maximum running time was 20.2 seconds, the average was 0.8 seconds, the median 0.02 seconds, processed on a 4GHz Intel i7 with 16 GB of RAM). |
| Software Dependencies | No | The paper mentions 'Python 3.7' for its code. It also mentions using 'a SAT solver' but does not specify its version number, nor does it list versions for other libraries or dependencies. |
| Experiment Setup | No | The paper describes the implementation and performance metrics of algorithms for recognizing single-peaked preferences but does not provide specific experimental setup details such as hyperparameters, training configurations, or initialization settings, as it is not training a machine learning model. |