手法を比較

	Normative Scenario Backcasting	Causal Layered Analysis
分野	Futures Foresight Studies	Futures Foresight Studies
系統	Process / pipeline	Process / pipeline
提唱年	1990	1998
提唱者	John B. Robinson	Sohail Inayatullah
種類	Goal-oriented pipeline constructing a milestone pathway backward from a desired future image	Layered deconstruction-and-reconstruction pipeline for futures and problem analysis
原典	Robinson, J. B. (1990). Futures under glass: A recipe for people who hate to predict. Futures, 22(8), 820-842.	Inayatullah, S. (1998). Causal layered analysis: Poststructuralism as method. Futures, 30(8), 815-829.
別名	Normative Backcasting, Scenario Backcasting, Goal-Oriented Backcasting, Desired-Future Backcasting	CLA, Causal Layered Analysis Method, Inayatullah CLA, Layered Futures Analysis
関連	4	3
概要	Normative scenario backcasting inverts the usual direction of futures work: instead of projecting forward from the present to ask what is likely, it starts from an explicit image of a desired future and works backward to construct the path of milestones, conditions, and actions that would lead there. John Robinson introduced the approach in 1990 as a method for people who 'hate to predict,' arguing that when the goal is to assess whether and how a normatively preferred future could be reached, forecasting the probable is the wrong tool. Backcasting instead asks what would have to happen, in what order, for the desired endpoint to come about. Distinct from generic policy backcasting, the normative scenario variant centres on first articulating a rich, value-laden scenario image of the endpoint and then deriving the pathway from it. As Bishop, Hines and Collins note in their survey of scenario techniques, this goal-driven logic makes backcasting a natural partner to scenario methods wherever the aim is not to anticipate the future but to deliberately work toward a chosen one.	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.