方法对比

	La Prospective Morphological Scenarios	SMIC Prob-Expert
领域	Futures Foresight Studies	Futures Foresight Studies
方法族	Process / pipeline	Process / pipeline
起源年份	2006	2006
提出者	Michel Godet (LIPSOR, Conservatoire national des arts et metiers)	Michel Godet (LIPSOR)
类型	Combinatorial scenario-construction pipeline within La Prospective	Probabilistic cross-impact pipeline for ranking scenario combinations
开创性文献	Godet, M. (2006). Creating Futures: Scenario Planning as a Strategic Management Tool (2nd ed.). Economica. ISBN: 9782717852448	Godet, M. (2006). Creating Futures: Scenario Planning as a Strategic Management Tool (2nd ed.). Economica. ISBN: 9782717852448
别名	Godet Morphological Scenarios, Prospective Scenario Building, French School Scenario Method, Morphologie des scenarios	SMIC, Systeme et Matrices d'Impacts Croises, SMIC-PROB-EXPERT, Probabilistic Cross-Impact Method
相关	4	3
摘要	Within the French school of la prospective developed by Michel Godet, morphological scenario construction is the integrating stage that turns the outputs of structural and actor analysis into a small set of coherent images of the future. Building on Fritz Zwicky's morphological method, Godet decomposes the studied system into a set of dimensions or components, attaches to each a few mutually exclusive hypotheses about how it might evolve, and treats the Cartesian product of these hypotheses as the morphological space of all conceivable futures. Because that space is combinatorially large, the method's analytical work lies in reducing it: pruning combinations that are internally incoherent, implausible, or incompatible with the strategies of key actors, until a handful of contrasted, self-consistent scenarios remain. Distinct from general morphological analysis, this is the scenario-building application that consumes the variables identified by MICMAC and the actor positions mapped by MACTOR.	SMIC Prob-Expert — from the French Systeme et Matrices d'Impacts Croises, Systems and Matrices of Cross-Impacts — is the probabilistic cross-impact method in Michel Godet's la prospective toolkit. It takes a small set of fundamental hypotheses about the future and asks experts for both the simple probability that each hypothesis comes true and the conditional probabilities linking the hypotheses to one another. Because experts' raw estimates are rarely mutually consistent, SMIC's core is a quadratic optimisation that adjusts them minimally into a coherent joint probability distribution over the 2^n possible combinations of the hypotheses. Each combination is an image of the future — a scenario — and the corrected, or net, probabilities rank these images from most to least likely. The method thereby turns scattered expert opinion into a probabilistically weighted set of scenarios, identifying the few core futures that concentrate most of the probability mass.