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 Convergence of Iterative Voting: How Restrictive Should Restricted Dynamics Be?
Authors: Svetlana Obraztsova, Evangelos Markakis, Maria Polukarov, Zinovi Rabinovich, Nicholas Jennings
AAAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,313 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,007 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 | We study convergence properties of iterative voting procedures. To this end, we provide two general conditions on the dynamics based on iterative myopic improvements, each of which is suf๏ฌcient for convergence. |
| Researcher Affiliation | Collaboration | Svetlana Obraztsova National Technical University of Athens, Greece Tel-Aviv University, Israel; Evangelos Markakis Athens University of Economics & Business Greece; Maria Polukarov University of Southampton United Kingdom; Zinovi Rabinovich Mobileye Vision Technologies Ltd. Israel; Nicholas R. Jennings University of Southampton United Kingdom |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for training or experimentation. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not contain specific experimental setup details such as hyperparameters or training configurations. |