A probabilistic cross‐impact methodology for explorative scenario analysis

As one of the approaches to scenario analysis, cross-impact methods provide a structured approach building scenarios as combinations outcomes for selected uncertainty factors. Although they vary in their details, are similar that synthesize expert judgments about probabilistic or causal dependencies between pairs factors and seek focus attention on can be deemed consistent. Still, most do not associate probabilities with scenarios, which limits possibilities integrating them risk decision analysis. Motivated by this recognition, we develop method derives joint probability distribution over all possible from probabilistically interpreted statements. More specifically, our (i) admits broad range statements realizations factors, (ii) supports process eliciting such statements, (iii) synthesizes these solving series optimization models corresponding derived. The resulting used construct Bayesian networks, expands analyses carried out. We illustrate real case study impacts three-dimensional (3D)-printing Finnish Defense Forces. probabilities, associated network helped explore alternative futures gave insights into how Defence Forces could benefit 3D-printing.