A Discrepancy-based Framework to Compare Robustness between Multi-Attribute Evaluations

TL;DR

This paper introduces a model-independent framework for assessing the robustness of multi-attribute evaluations in complex systems, demonstrated through urban data analysis, offering a potentially more reliable alternative to traditional statistical methods.

Contribution

It presents a novel, model-independent approach to measure robustness in multi-attribute evaluations, applicable across different system types and data structures.

Findings

Effective robustness measure applied to urban systems and income segregation data

Potential independence from specific system models and types

First numerical results demonstrate promising capabilities

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 model may position it as an alternative to classical statistical robustness methods.