방법 비교

	MACTOR Actor Strategy Analysis	SMIC Prob-Expert
분야	Futures Foresight Studies	Futures Foresight Studies
계열	Process / pipeline	Process / pipeline
기원 연도	2006	2006
창시자	Michel Godet (LIPSOR)	Michel Godet (LIPSOR)
유형	Matrix-based pipeline for mapping actor power, positions, and alliances	Probabilistic cross-impact pipeline for ranking scenario combinations
원전	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
별칭	MACTOR, Actor Strategy Analysis, Matrix of Alliances and Conflicts, Methode des Acteurs	SMIC, Systeme et Matrices d'Impacts Croises, SMIC-PROB-EXPERT, Probabilistic Cross-Impact Method
관련	3	3
요약	MACTOR — Matrix of Alliances and Conflicts: Tactics, Objectives, and Recommendations — is the actor-analysis method in Michel Godet's la prospective toolkit, designed to study the strategy game among the players who shape a system's future. Where structural analysis with MICMAC maps variables, MACTOR maps actors: it builds a matrix of the direct means of action each actor can exert on the others, from which it derives competitive-strength coefficients (the Ri ratios) that gauge each actor's power, and a second matrix recording where each actor stands, for or against, on the contested objectives at stake. By weighting actors' positions by their power and comparing them objective by objective, MACTOR computes the convergences and divergences among actors, revealing potential alliances, latent conflicts, and the balance of power. The result is a strategic diagnosis that informs scenario construction by exposing which futures the actor field would support or resist.	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.