A Discrepancy-Based Framework to Compare Robustness Between Multi-attribute Evaluations

Abstract : Multi-objective evaluation is a necessary aspect when managing complex systems, as the intrinsic complexity of a system is generally closely linked to the potential number of optimization objectives. However, an evaluation makes no sense without its robustness being given (in the sense of its reliability). Statistical robustness computation methods are highly dependent of underlying statistical models. We propose a formulation of a model-independent framework in the case of integrated aggregated indicators (Multi-attribute evaluation), that allows to define a relative measure of robustness taking into account data structure and indicator values. We implement and apply it to a synthetic case of urban systems based on Paris districts geography, and to real data for evaluation of income segregation for Greater Paris metropolitan area. First numerical results show the potentialities of this new method. Furthermore , its relative independence to system type and system model may position it as an alternative to classical statistical robustness methods.
Type de document :
Communication dans un congrès
CSD&M 2016, Dec 2016, Paris, France. Springer International Publishing, pp.141 - 154, 2016, Complex Systems Design & Management. 〈http://www.2016.csdm.fr/〉. 〈10.1007/978-3-319-49103-5_11〉
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https://halshs.archives-ouvertes.fr/halshs-01424253
Contributeur : Juste Raimbault <>
Soumis le : lundi 2 janvier 2017 - 09:28:53
Dernière modification le : vendredi 4 janvier 2019 - 17:33:22
Document(s) archivé(s) le : mardi 4 avril 2017 - 00:23:15

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Juste Raimbault. A Discrepancy-Based Framework to Compare Robustness Between Multi-attribute Evaluations. CSD&M 2016, Dec 2016, Paris, France. Springer International Publishing, pp.141 - 154, 2016, Complex Systems Design & Management. 〈http://www.2016.csdm.fr/〉. 〈10.1007/978-3-319-49103-5_11〉. 〈halshs-01424253〉

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