방법 비교

	Futures Wheel	Causal Layered Analysis
분야	Futures Foresight Studies	Futures Foresight Studies
계열	Process / pipeline	Process / pipeline
기원 연도	1972	1998
창시자	Jerome C. Glenn	Sohail Inayatullah
유형	Structured brainstorming pipeline for mapping consequences of change	Layered deconstruction-and-reconstruction pipeline for futures and problem analysis
원전	Glenn, J. C., & Gordon, T. J. (Eds.). (2009). Futures Research Methodology, Version 3.0. The Millennium Project. ISBN: 9780981894119	Inayatullah, S. (1998). Causal layered analysis: Poststructuralism as method. Futures, 30(8), 815-829.
별칭	Futures Wheel Method, Implications Wheel, Consequence Wheel, Mind-Mapping the Future	CLA, Causal Layered Analysis Method, Inayatullah CLA, Layered Futures Analysis
관련	3	3
요약	The futures wheel is a structured brainstorming method for thinking through the consequences of a change. Created by Jerome Glenn in 1972 and now a staple of the foresight toolkit, it writes a trend, event, or decision in the center of a page and radiates outward: spokes lead to the direct, first-order consequences; from each of those, further spokes lead to second-order consequences; and the ripple continues to third and higher orders. The visual web that results makes the cascading implications of a change tangible, surfaces indirect effects that linear thinking misses, and reveals where consequences reinforce or contradict one another. Simple enough to run on a whiteboard yet powerful enough to anchor serious foresight work, it organizes intuition about how change propagates.	Causal layered analysis (CLA) is a critical futures method developed by Sohail Inayatullah and set out in his 1998 paper 'Causal layered analysis: Poststructuralism as method.' Rather than forecasting, its aim is to open up the space of possible futures by reading an issue at four levels of depth. The surface 'litany' of headlines and accepted trends sits atop systemic causes, which rest in turn on the worldviews and discourses that legitimate them, all anchored in deep myths and metaphors. By moving down through these layers to expose the assumptions and narratives beneath a problem — and then reconstructing upward from a transformed deep story — CLA produces futures that differ not merely in detail but in their underlying logic.