Social polarization in the metropolitan area of Marseille. Modelling uncertain knowledge with probabilistic and possibilistic networks

Abstract

A Bayesian Network and a Possibilistic Network are used to produce trend scenarios of social polarization in the metropolitan area of Marseille (France). Both scenarios are based on uncertain knowledge of relationships among variables and produce uncertain evaluations of future social polarization. We show that probabilistic models should not be used just to infer most probable outcomes, as these would give a fallacious impression of certain knowledge. The possibilistic model produces more uncertainty-laden results which are coherent with model uncertainties and respect elicited values of possibilities. Results of the two models converge when probability values are “degraded”.