方法对比

	Manoa Alternative Futures Method	Trend Impact Analysis
领域	Futures Foresight Studies	Futures Foresight Studies
方法族	Process / pipeline	Process / pipeline
起源年份	2009	1972
提出者	Jim Dator (Hawai'i Research Center for Futures Studies, University of Hawai'i at Manoa)	Theodore J. Gordon (The Futures Group / Millennium Project)
类型	Generative pipeline for producing alternative images of the future	Probabilistic trend-extrapolation pipeline perturbed by future events
开创性文献	Dator, J. (2009). Alternative futures at the Manoa School. Journal of Futures Studies, 14(2), 1-18.	Gordon, T. J., & Hayward, H. (1968). Initial experiments with the cross-impact matrix method of forecasting. Futures, 1(2), 100-116.
别名	Manoa School Method, Four Generic Futures, Dator Alternative Futures, Hawai'i Alternative Futures	TIA, Trend-Impact Forecasting, Probabilistic Trend Perturbation, Event-Adjusted Trend Extrapolation
相关	4	3
摘要	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.	Trend impact analysis (TIA) is a forecasting method that marries quantitative extrapolation with expert judgment about disruptive future events. Developed by Theodore Gordon and colleagues at The Futures Group in the early 1970s and later codified in the Millennium Project's Futures Research Methodology, it starts from a 'surprise-free' baseline produced by fitting and projecting a historical time series. It then asks which unprecedented events — events with no historical analog that ordinary extrapolation cannot anticipate — could deflect that trend, and with what probability, magnitude, and timing. Through Monte Carlo simulation those probabilistic impacts perturb the baseline, yielding not a single line but a probability envelope that shows how the trend might bend if the unexpected occurs.