方法对比

	Science Fiction Prototyping	Manoa Alternative Futures Method
领域	Futures Foresight Studies	Futures Foresight Studies
方法族	Process / pipeline	Process / pipeline
起源年份	2011	2009
提出者	Brian David Johnson (Intel)	Jim Dator (Hawai'i Research Center for Futures Studies, University of Hawai'i at Manoa)
类型	Narrative-prototyping pipeline for technology futures	Generative pipeline for producing alternative images of the future
开创性文献	Johnson, B. D. (2011). Science Fiction Prototyping: Designing the Future with Science Fiction. Morgan & Claypool. ISBN: 9781608456550	Dator, J. (2009). Alternative futures at the Manoa School. Journal of Futures Studies, 14(2), 1-18.
别名	SF Prototyping, SFP, Fiction-Based Prototyping, Design Fiction Prototyping	Manoa School Method, Four Generic Futures, Dator Alternative Futures, Hawai'i Alternative Futures
相关	3	4
摘要	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.	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.