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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Price of Fairness in Budget Division and Probabilistic Social Choice
Authors: Marcin Michorzewski, Dominik Peters, Piotr Skowron2184-2191
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we extend our approach beyond the worst-case analysis. In a series of computer simulations we assess the average efficiency and egalitarian fairness of randomized rules assuming that voters preferences come from certain distributions. |
| Researcher Affiliation | Academia | 1University of Warsaw, 2Carnegie Mellon University |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to open-source code for the methodology described. |
| Open Datasets | No | The paper describes data generation models (Euclidean Model, Impartial Culture, Mallow’s Model) but does not provide access to a specific publicly available or open dataset. |
| Dataset Splits | No | The paper describes drawing instances for simulations but does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For each configuration we draw 500 instances, and for each instance I and each rule f we calculate the normalized welfare sw(I, f(I)). |