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The Clustering Method Applied in a Fair Division Process

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  • Mputu Losala Lomo, Denis-Robert

Abstract

In this paper, we intend to present our two processes for the equitable sharing of resources which use several variables, take into account the type of variables and the origin of the resource, involving in particular the notions of data transformation and of clustering used in Statistics. These processes are: PRRS (for Procédé de la Répartition des Ressources Sans réduction des inégalités, in English Process of the Distribution of Resources without reduction of inequalities) and PRRC (for Procédé de la Répartition des Ressourcesà partir des résultats de la Classification, in English Process of the Distribution of Resources from Clustering results). They come to solve the problem of injustice in a sharing of resources (in particular, a sum of money), the injustice due to 1) the use of a single variable (criterion) instead of several, 2) the direct use of homogeneous variables where the same unit of measurement is expressed differently for each variable, 3) the direct use of heterogeneous variables and 4) the lack of reduction of inequalities between individuals, in certain cases.

Suggested Citation

  • Mputu Losala Lomo, Denis-Robert, 2023. "The Clustering Method Applied in a Fair Division Process," MPRA Paper 122924, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:122924
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    References listed on IDEAS

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    7. Mputu Losala Lomo, Denis-Robert, 2022. "Application de la Classification Ascendante Hiérarchique à la Répartition des Ressources Budgétaires dans la Ville-Province de Kinshasa [Application of Hierarchical Ascendant Clustering to the Dist," MPRA Paper 113774, University Library of Munich, Germany.
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    More about this item

    Keywords

    Ascending Hierarchical Classification; Equitable sharing; Resource; Process; Reduction of inequalities.;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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