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.
Phragmén Rules for Degressive and Regressive Proportionality
Authors: Michał Jaworski, Piotr Skowron
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,663 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,733 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We will now analyze, through experiments, how voters satisfaction depends on using committee election rules implementing different types of proportionality. We ran 1000 simulations for each scenario. In Table 1 we give numerical values quantifying the voters satisfaction. From the experiments we conclude: |
| Researcher Affiliation | Academia | Michał Jaworski and Piotr Skowron University of Warsaw, Poland EMAIL |
| Pseudocode | No | The paper describes the rules and procedures in narrative text with examples but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide explicit statements or links to open-source code for the methodology described. |
| Open Datasets | No | The paper describes generating synthetic data from beta distributions rather than using a named, publicly available dataset with concrete access information: 'We draw the individuals independently at random from beta distributions, scaled into [-1, 1].' |
| Dataset Splits | No | The paper describes running simulations with generated data but does not specify train/validation/test dataset splits. It states: 'We ran 1000 simulations for each scenario.' |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | In the simulations we consider instances with n = 200 voters, m = 150 candidates and for the committee size k = 25. What is more, we set the acceptance radius τ = 0.2 and the parameters of the probability function pη to: τ = 30, δ = 120. |