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 [1].
On the Distortion Value of Elections with Abstention
Authors: Masoud Seddighin, Mohammad Latifian, Mohammad Ghodsi
JAIR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this study, we wish to answer the following question: how does the distortion value change if we allow less motivated agents to abstain from the election? We consider an election with two candidates and suggest an abstention model, which is a general form of the abstention model proposed by Kirchg assner. Our results characterize the distortion value and provide a rather complete picture of the model. The paper primarily presents mathematical models, theorems (e.g., Theorem 3.1, Theorem 4.1), lemmas (e.g., Lemmas 3.3, 3.4, 3.5), and their proofs, which are characteristic of theoretical research. |
| Researcher Affiliation | Academia | All authors are affiliated with academic institutions: 'Institute for Research in Fundamental Sciences (IPM)' and 'Sharif University of Technology', as indicated by their affiliations and email domains (@gmail.com for one author is a personal email but the affiliation itself is academic, @ce.sharif.edu, @sharif.ir). |
| Pseudocode | No | The paper describes its methods using mathematical formulations, theorems, lemmas, and proofs. It does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements regarding the release of open-source code, nor does it provide links to any code repositories or mention code in supplementary materials. |
| Open Datasets | No | This paper is theoretical and focuses on mathematical models and proofs. It does not use or reference any publicly available datasets for empirical evaluation. The examples provided (e.g., Figure 1, Figure 12) are illustrative theoretical scenarios, not empirical datasets. |
| Dataset Splits | No | As this paper is theoretical and does not involve empirical experiments with datasets, there is no mention of training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical analysis rather than empirical experimentation. Therefore, no specific hardware details (like GPU/CPU models, memory amounts, or cluster specifications) used for running experiments are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and models. It does not specify any software dependencies with version numbers that would be required to replicate experimental results. |
| Experiment Setup | No | As the paper presents theoretical analysis and does not conduct empirical experiments, there are no details provided regarding experimental setup, hyperparameters, or training configurations. |