手法を比較

	Gompertz Substitution Forecasting	Trend Impact Analysis
分野	Futures Foresight Studies	Futures Foresight Studies
系統	Process / pipeline	Process / pipeline
提唱年	1971	1972
提唱者	Benjamin Gompertz (curve); growth-curve technology forecasters (Lenz, Martino) and the Fisher-Pry tradition	Theodore J. Gordon (The Futures Group / Millennium Project)
種類	Growth-curve diffusion pipeline for technology adoption and substitution	Probabilistic trend-extrapolation pipeline perturbed by future events
原典	Fisher, J. C., & Pry, R. H. (1971). A simple substitution model of technological change. Technological Forecasting and Social Change, 3, 75-88.	Gordon, T. J., & Hayward, H. (1968). Initial experiments with the cross-impact matrix method of forecasting. Futures, 1(2), 100-116.
別名	Gompertz Diffusion Forecasting, Gompertz Growth-Curve Forecasting, Asymmetric S-Curve Technology Forecasting, Gompertz Adoption Model	TIA, Trend-Impact Forecasting, Probabilistic Trend Perturbation, Event-Adjusted Trend Extrapolation
関連	3	3
概要	Gompertz substitution forecasting projects the adoption, diffusion, or substitution of a technology by fitting the asymmetric Gompertz growth curve to historical data and extrapolating it toward a saturation ceiling. Like the symmetric logistic used in the Fisher-Pry substitution model, the Gompertz curve captures the characteristic S-shape of technological change — slow initial uptake, rapid mid-life growth, and tapering as the market saturates — but unlike the logistic it is asymmetric, reaching its fastest growth early, at roughly 37 percent of the ceiling rather than at the midpoint. This makes it a natural choice when a new technology accelerates quickly and then approaches its limit gradually. Within the futures and foresight toolkit catalogued by Glenn and Gordon, growth-curve forecasting of this kind is a core quantitative method for anticipating when a technology will mature and when a successor is likely to displace it.	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.