Сравнение на методи

	Causal Layered Analysis	Manoa Alternative Futures Method
Област	Futures Foresight Studies	Futures Foresight Studies
Семейство	Process / pipeline	Process / pipeline
Година на възникване	1998	2009
Създател	Sohail Inayatullah	Jim Dator (Hawai'i Research Center for Futures Studies, University of Hawai'i at Manoa)
Тип	Layered deconstruction-and-reconstruction pipeline for futures and problem analysis	Generative pipeline for producing alternative images of the future
Основополагащ източник	Inayatullah, S. (1998). Causal layered analysis: Poststructuralism as method. Futures, 30(8), 815-829.	Dator, J. (2009). Alternative futures at the Manoa School. Journal of Futures Studies, 14(2), 1-18.
Други названия	CLA, Causal Layered Analysis Method, Inayatullah CLA, Layered Futures Analysis	Manoa School Method, Four Generic Futures, Dator Alternative Futures, Hawai'i Alternative Futures
Свързани	3	4
Резюме	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.	The Manoa Alternative Futures Method is the signature technique of the Hawai'i Research Center for Futures Studies, developed by Jim Dator at the University of Hawai'i at Manoa. Its founding axiom is that 'the future' cannot be predicted, only alternative futures can be imagined, so the purpose of foresight is not a single forecast but a set of qualitatively different images broad enough to bound the space of what might plausibly happen. Dator's central empirical claim, distilled from decades of futures work, is that the enormous variety of credible long-range scenarios collapses into four generic images: continued growth, collapse, discipline, and transformation. The method seeds these four archetypes with emerging issues — weak signals not yet visible as trends — to stretch participants' images of the future and produce a usable, divergent scenario set.