مقایسهٔ روش‌ها

	Science Fiction Prototyping	Causal Layered Analysis
حوزه	Futures Foresight Studies	Futures Foresight Studies
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	2011	1998
پدیدآور	Brian David Johnson (Intel)	Sohail Inayatullah
نوع	Narrative-prototyping pipeline for technology futures	Layered deconstruction-and-reconstruction pipeline for futures and problem analysis
منبع بنیادین	Johnson, B. D. (2011). Science Fiction Prototyping: Designing the Future with Science Fiction. Morgan & Claypool. ISBN: 9781608456550	Inayatullah, S. (1998). Causal layered analysis: Poststructuralism as method. Futures, 30(8), 815-829.
نام‌های دیگر	SF Prototyping, SFP, Fiction-Based Prototyping, Design Fiction Prototyping	CLA, Causal Layered Analysis Method, Inayatullah CLA, Layered Futures Analysis
مرتبط	3	3
خلاصه	Science Fiction Prototyping (SFP) is a method, formalized by Intel futurist Brian David Johnson, for using short works of science fiction as design tools. The core idea is that a fictional narrative grounded in a real, specified science or technology can act as a 'prototype' — a way to test the human, social, and ethical implications of an innovation before it is built, and to feed what is learned back into the actual engineering and design process. Rather than treating fiction as mere entertainment or untethered speculation, SFP imposes a discipline: every story must start from a concrete scientific grounding, develop a believable world, introduce the technology, follow its consequences honestly, and end with a reflection that loops back to the science. Johnson's 2011 monograph lays out the steps and uses examples drawn from his work shaping product visions at Intel.	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.