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..
Portioning Using Ordinal Preferences: Fairness and Efficiency
Authors: Stéphane Airiau, Haris Aziz, Ioannis Caragiannis, Justin Kruger, Jérôme Lang, Dominik Peters
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a family of rules for portioning, inspired by positional scoring rules. Rules in this family are given by a scoring vector (such as plurality or Borda) associating a positive value with each rank in a vote, and an aggregation function such as leximin or the Nash product. Our family contains well-studied rules, but most are new. We discuss computational and normative properties of our rules. We focus on fairness, and introduce the SD-core, a group fairness notion. Our Nash rules are in the SD-core, and the leximin rules satisfy individual fairness properties. Both are Pareto-efficient. |
| Researcher Affiliation | Academia | 1LAMSADE, CNRS, Universit e Paris-Dauphine, PSL University 2UNSW Sydney and Data61 CSIRO 3University of Patras 4University of Oxford |
| Pseudocode | Yes | Algorithm 1 Computing an s-leximin distribution |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for open-source code. |
| Open Datasets | No | The paper is theoretical and does not use or reference any datasets for empirical training. |
| Dataset Splits | No | The paper is theoretical and does not involve validation sets or data splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for any computations or analyses. |
| Software Dependencies | No | The paper mentions 'standard solvers' and 'linear programming' for computational aspects but does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |