Abstract
Aggregating the preference of multiple experts is a very old problem which remains without an absolute solution. This assertion is supported by the Arrow’s theorem: there is no aggregation method that simultaneously satisfies three fairness criteria (non-dictatorship, independence of irrelevant alternatives and Pareto efficiency). However, it is possible to find a solution having minimal distance to the consensus, although it involves a NP-hard problem even for only a few experts. This paper presents a model based on Ant Colony Optimization for facing this problem when input data are incomplete. It means that our model should build a complete ordering from partial rankings. Besides, we introduce a measure to determine the distance between items. It provides a more complete picture of the aggregated solution. In order to illustrate our contributions we use a real problem concerning Employer Branding issues in Belgium.
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Nápoles, G., Dikopoulou, Z., Papageorgiou, E., Bello, R., Vanhoof, K. (2015). Aggregation of Partial Rankings - An Approach Based on the Kemeny Ranking Problem. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9095. Springer, Cham. https://doi.org/10.1007/978-3-319-19222-2_29
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DOI: https://doi.org/10.1007/978-3-319-19222-2_29
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