Summary
In Section 6.1, we define and illustrate four reference problematics — P.α, P.β, P.γ, P.δ. The objective of P.α is to aid the decision maker by the choice of a subset that is as small as possible so that a single action can eventually be chosen. This subset contains “best” actions (optima) or, perhaps, “satisfactory” actions (satisficing solutions). The result of P.α is a choice or a selection procedure.
The objective of P.β is to aid the decision maker by a sorting that leads to an assignment of each action to a category, where the categories are defined beforehand as a function of certain norms that deal with the ultimate fate of the actions that will be assigned to them. The result of P.β is a sorting or an assignment procedure.
The objective of P.γ is to aid the decision maker through a ranking that is obtained by placing all, or simply the “most attractive,” actions into equivalence classes that are completely or partially ordered according to preferences. P.γ results in a ranking or an ordering procedure.
The objective of P.δ is to aid the decision maker by developing a description of the actions and their consequences in appropriate terms. It results in a description or a cognitive procedure.
In Section 6.2 we discuss how the problematic chosen for a given phase of the analysis can correspond to one of the four reference problematics, to a sequence of two or more of the problematics, or to a mixed problematic. We illustrate the last two cases with new examples.
translator’s note: We considered translating this term as problem statements, problem types, or problem formulations, but felt that these could give the wrong impression. After discussion with several researchers in this field, we decided to remain close to the the original French word “problématique,” even if it seems like jargon and sounds somewhat awkward, but which we feel will avoid misunderstanding.
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© 1996 Springer Science+Business Media Dordrecht
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Roy, B. (1996). Problematics as Guides in Decision Aiding. In: Multicriteria Methodology for Decision Aiding. Nonconvex Optimization and Its Applications, vol 12. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2500-1_6
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DOI: https://doi.org/10.1007/978-1-4757-2500-1_6
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