방법 비교

	Cross-Impact Balance Analysis	Cross-Impact Matrix Method
분야	Futures Foresight Studies	Futures Foresight Studies
계열	Process / pipeline	Process / pipeline
기원 연도	2006	1968
창시자	Wolfgang Weimer-Jehle	Theodore J. Gordon & H. Hayward
유형	Semi-quantitative scenario-construction pipeline	Probabilistic cross-impact simulation pipeline for interdependent events
원전	Weimer-Jehle, W. (2006). Cross-impact balances: A system-theoretical approach to cross-impact analysis. Technological Forecasting and Social Change, 73(4), 334-361.	Gordon, T. J., & Hayward, H. (1968). Initial experiments with the cross-impact matrix method of forecasting. Futures, 1(2), 100-116.
별칭	CIB Analysis, Cross-Impact Balances, Balance Algorithm Scenario Analysis, Qualitative Systems Analysis (Weimer-Jehle)	Cross-Impact Matrix Forecasting, Conditional-Probability Cross-Impact, Gordon-Hayward Cross-Impact, Probabilistic Cross-Impact Simulation
관련	4	3
요약	Cross-Impact Balance (CIB) analysis is a semi-quantitative foresight method that turns a panel of qualitative expert judgments into a small set of internally consistent scenarios. Introduced by Wolfgang Weimer-Jehle in 2006, CIB describes a system as a set of descriptors, each of which can take one of several discrete future states, and asks experts to judge, pairwise, how strongly each state promotes or restricts every other state. These judgments form a cross-impact matrix; a balance algorithm then searches the combinatorial space of state combinations for configurations in which every descriptor's chosen state is the one most strongly supported by all the others. These self-consistent combinations are the scenarios. CIB has become a standard tool for building qualitative socio-technical scenarios, including the shared socio-economic pathways used in climate research.	The cross-impact matrix method is a quantitative forecasting technique that asks how the occurrence of one future event changes the probability that other events will occur. Introduced by Theodore Gordon and H. Hayward in 1968, it begins with a set of forecast events and their initial probabilities and then captures the interactions among them in a matrix of conditional probabilities. Rather than forecasting each event in isolation, the method runs repeated Monte Carlo trials in which events occur or fail to occur and their cross-impacts propagate, updating the probabilities of the remaining events. The output is a revised, internally interactive set of event probabilities and a distribution over coherent futures, making explicit the web of mutual influence that simple independent forecasts ignore.