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.
Rank Maximal Equal Contribution: A Probabilistic Social Choice Function
Authors: Haris Aziz, Pang Luo, Christine Rizkallah
AAAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,646 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,818 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 | An exhaustive experiment shows that RMEC is SD-eļ¬cient for every proļ¬le with 4 agents and 4 alternatives. Further experiments show that RMEC is SD-eļ¬cient for almost all the proļ¬les with n, m ā¤ 8. In the experiment, we generated proļ¬les uniformly at random for speciļ¬ed numbers of agents and alternatives so that each preference is equiprobable, and examined whether the corresponding RMEC lottery is SD-eļ¬cient. The results are shown in Table 2. |
| Researcher Affiliation | Collaboration | Haris Aziz Data61, CSIRO and UNSW Sydney, Australia Pang Luo Data61, CSIRO and UNSW Sydney, Australia Christine Rizkallah University of Pennsylvania Philadelphia, United States |
| Pseudocode | Yes | Algorithm 1: The Rank Maximal Equal Contribution rule |
| Open Source Code | No | The paper does not provide concrete access to source code (e.g., a specific repository link or an explicit statement of code release) for the methodology described. |
| Open Datasets | No | In the experiment, we generated proļ¬les uniformly at random for speciļ¬ed numbers of agents and alternatives so that each preference is equiprobable. The paper does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper specifies generating profiles uniformly at random and testing them. However, it does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiments. |
| Experiment Setup | No | The paper mentions generating 10,000 or 1,000 profiles for experiments and specifying numbers of agents and alternatives (n, m up to 8). However, it does not provide specific experimental setup details such as concrete hyperparameter values or training configurations relevant to machine learning models. |