Version [2.0] - [PySensMCDA: A novel tool for sensitivity analysis in multi-criteria problems]

Résumé fourni par la source

In this work, we present an update of the PySensMCDA library that extends its scope from perturbation-based sensitivity analysis to inverse analysis of the weight space. Four submodules are introduced. The sampling submodule generates weight vectors under absent or partial preference information and decision matrices with uncertain criteria values. The smaa submodule implements the stochastic multicriteria acceptability analysis family, complemented by an exact linear-programming route. The eors submodule provides exhaustive objective ranking together with local sensitivity diagnostics. The robustness submodule summarises any collection of rankings through stability coefficients, decision entropy and pairwise winning indices. A shared calling convention makes every analysis usable with any MCDA method, including user-defined ones. The update also adds six visualizations, an example notebook and 371 tests.