手法を比較

	MICMAC Structural Analysis	SMIC Prob-Expert
分野	Futures Foresight Studies	Futures Foresight Studies
系統	Process / pipeline	Process / pipeline
提唱年	2006	2006
提唱者	Michel Godet with Jean-Claude Duperrin (LIPSOR)	Michel Godet (LIPSOR)
種類	Matrix-based pipeline for classifying system variables by influence and dependence	Probabilistic cross-impact pipeline for ranking scenario combinations
原典	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
別名	MICMAC, Structural Analysis, Cross-Impact Matrix Multiplication Applied to a Classification, Matrice d'Impacts Croises	SMIC, Systeme et Matrices d'Impacts Croises, SMIC-PROB-EXPERT, Probabilistic Cross-Impact Method
関連	3	3
概要	MICMAC — Matrice d'Impacts Croises Multiplication Appliquee a un Classement, or Cross-Impact Matrix Multiplication Applied to a Classification — is the structural-analysis tool at the front of Michel Godet's la prospective method. Developed by Godet with Jean-Claude Duperrin, it starts from a square matrix in which experts record the direct influence of each system variable on every other, then raises that matrix to successive powers to uncover the indirect influences that propagate along chains of variables. Summing the rows and columns of the iterated matrix yields each variable's overall influence and dependence, and plotting variables on the influence-dependence plane sorts them into driving (key) variables, dependent (result) variables, relay variables, and autonomous variables. The purpose is not prediction but diagnosis: to reveal which hidden variables truly drive the system, so that later scenario work focuses on the factors that matter.	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.