Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
On the Distortion Value of the Elections with Abstention
Authors: Mohammad Ghodsi, Mohamad Latifian, Masoud Seddighin1981-1988
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 14,604 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,222 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our results fully characterize the distortion value and provide a rather complete picture of the model. In Theorem 3.1 we state the main result of this section. The basic idea to prove Theorem 3.1 is as follows: we prove that for every election E, there exists an election instance E with the same expected winner, and D(E ) D(E). In Lemmas 3.3,3.4, and 3.5 we introduce three sorts of valid displacement which help us collect the voters. |
| Researcher Affiliation | Academia | Mohammad Ghodsi, Mohamad Latiο¬an, Masoud Seddighin Sharif University of Technology, Institute for Research in Fundamental Sciences (IPM) School of CS EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any sections explicitly labeled 'Pseudocode' or 'Algorithm', nor does it present structured code-like procedures. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a specific dataset. Therefore, it does not refer to a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, thus it does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on mathematical analysis and proofs, thus it does not mention specific hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe computational experiments or software implementations, so it does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical models and proofs. It does not describe an empirical experimental setup with hyperparameters or system-level training settings. |