BiOWA for Preference Aggregation with Bipolar Scales: Application to Fair Optimization in Combinatorial Domains

Hugo Martin, Patrice Perny

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence

Main track. Pages 1822-1828.

https://doi.org/10.24963/ijcai.2019/252

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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.

Keywords

  • Knowledge Representation and Reasoning: Knowledge Representation and Decision ; Utility Theory
  • Heuristic Search and Game Playing: Combinatorial Search and Optimisation
  • Agent-based and Multi-agent Systems: Computational Social Choice
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