Portioning Using Ordinal Preferences: Fairness and Efficiency

Authors: Stéphane Airiau, Haris Aziz, Ioannis Caragiannis, Justin Kruger, Jérôme Lang, Dominik Peters

IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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.