Compara mètodes

	Trend Impact Analysis	Cross-Impact Matrix Method
Camp	Futures Foresight Studies	Futures Foresight Studies
Família	Process / pipeline	Process / pipeline
Any d'origen	1972	1968
Autor original	Theodore J. Gordon (The Futures Group / Millennium Project)	Theodore J. Gordon & H. Hayward
Tipus	Probabilistic trend-extrapolation pipeline perturbed by future events	Probabilistic cross-impact simulation pipeline for interdependent events
Font seminal	Gordon, T. J., & Hayward, H. (1968). Initial experiments with the cross-impact matrix method of forecasting. Futures, 1(2), 100-116.	Gordon, T. J., & Hayward, H. (1968). Initial experiments with the cross-impact matrix method of forecasting. Futures, 1(2), 100-116.
Àlies	TIA, Trend-Impact Forecasting, Probabilistic Trend Perturbation, Event-Adjusted Trend Extrapolation	Cross-Impact Matrix Forecasting, Conditional-Probability Cross-Impact, Gordon-Hayward Cross-Impact, Probabilistic Cross-Impact Simulation
Relacionats	3	3
Resum	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.	The cross-impact matrix method is a quantitative forecasting technique that asks how the occurrence of one future event changes the probability that other events will occur. Introduced by Theodore Gordon and H. Hayward in 1968, it begins with a set of forecast events and their initial probabilities and then captures the interactions among them in a matrix of conditional probabilities. Rather than forecasting each event in isolation, the method runs repeated Monte Carlo trials in which events occur or fail to occur and their cross-impacts propagate, updating the probabilities of the remaining events. The output is a revised, internally interactive set of event probabilities and a distribution over coherent futures, making explicit the web of mutual influence that simple independent forecasts ignore.