BiOWA for Preference Aggregation with Bipolar Scales: Application to Fair Optimization in Combinatorial Domains
Abstract
We study the biOWA model for preference aggregation and multicriteria decision making from bipolar rating scales. A biOWA is an ordered doubly weighted averaging extending standard ordered weighted averaging (OWA) and allowing a finer control of the importance attached to positive and negative evaluations in the aggregation. After establishing some useful properties of biOWA to generate balanced Pareto-optimal solutions, we address fair biOWA-optimization problems in combinatorial domains. We first consider the use of biOWA in multi-winner elections for aggregating graded approval and disapproval judgements. Then we consider the use of biOWA for solving robust path problems with costs expressing gains and losses. A linearization of biOWA is proposed, allowing both problems to be solved by MIP. A path-ranking algorithm for biOWA optimization is also proposed. Numerical tests are provided to show the practical efficiency of our models.
Cite
Text
Martin and Perny. "BiOWA for Preference Aggregation with Bipolar Scales: Application to Fair Optimization in Combinatorial Domains." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/252Markdown
[Martin and Perny. "BiOWA for Preference Aggregation with Bipolar Scales: Application to Fair Optimization in Combinatorial Domains." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/martin2019ijcai-biowa/) doi:10.24963/IJCAI.2019/252BibTeX
@inproceedings{martin2019ijcai-biowa,
title = {{BiOWA for Preference Aggregation with Bipolar Scales: Application to Fair Optimization in Combinatorial Domains}},
author = {Martin, Hugo and Perny, Patrice},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {1822-1828},
doi = {10.24963/IJCAI.2019/252},
url = {https://mlanthology.org/ijcai/2019/martin2019ijcai-biowa/}
}