เปรียบเทียบวิธี

	Futures Wheel	Trend Impact Analysis
สาขาวิชา	Futures Foresight Studies	Futures Foresight Studies
ตระกูล	Process / pipeline	Process / pipeline
ปีกำเนิด	1972	1972
ผู้ริเริ่ม	Jerome C. Glenn	Theodore J. Gordon (The Futures Group / Millennium Project)
ประเภท	Structured brainstorming pipeline for mapping consequences of change	Probabilistic trend-extrapolation pipeline perturbed by future events
แหล่งต้นตำรับ	Glenn, J. C., & Gordon, T. J. (Eds.). (2009). Futures Research Methodology, Version 3.0. The Millennium Project. ISBN: 9780981894119	Gordon, T. J., & Hayward, H. (1968). Initial experiments with the cross-impact matrix method of forecasting. Futures, 1(2), 100-116.
ชื่อเรียกอื่น	Futures Wheel Method, Implications Wheel, Consequence Wheel, Mind-Mapping the Future	TIA, Trend-Impact Forecasting, Probabilistic Trend Perturbation, Event-Adjusted Trend Extrapolation
ที่เกี่ยวข้อง	3	3
สรุป	The futures wheel is a structured brainstorming method for thinking through the consequences of a change. Created by Jerome Glenn in 1972 and now a staple of the foresight toolkit, it writes a trend, event, or decision in the center of a page and radiates outward: spokes lead to the direct, first-order consequences; from each of those, further spokes lead to second-order consequences; and the ripple continues to third and higher orders. The visual web that results makes the cascading implications of a change tangible, surfaces indirect effects that linear thinking misses, and reveals where consequences reinforce or contradict one another. Simple enough to run on a whiteboard yet powerful enough to anchor serious foresight work, it organizes intuition about how change propagates.	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.