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

Giovanni Fusco, Cristina Cao, Didier Dubois, Henri Prade, Floriane Scarella, Andrea Tettamanzi


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”.

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