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Persistent Forecasting of Disruptive Technologies
FIGURE 5-1 Probability of technology emergence.
Robust and dynamic structure,
Frames of reference for historical comparisons, and
Ease of use.
Persistence is one of the most important criteria to consider when designing a system for forecasting disruptive technology. Because most existing forecasts are developed over a short and finite time, they fail to incorporate signals that emerge after their creation and are therefore usable for only a short time. A key goal of a persistent system is to continuously improve the forecast based on new data, signals, and participant input. A persistent forecast can be built to serve many different customers, providing a continuously active and up-to-date forecast.
Openness and Breadth
No single group has the human, capital, or intellectual resources to imagine every possible disruptive scenario, capture every signal, or have access to all critical data. The persistent forecasting system must therefore be open to the widest possible participation. The more broad-based the system, the more likely it will be to generate many alternative futures and more extreme versions of the future, which often predict the most disruptive outcomes.
The committee believes that the entities running a persistent forecasting system need to be transparent and partner-friendly. This will build trust and yield compelling content and incentives that encourage broad and ongoing participation from a diverse (on every vector) group of participants.
The information derived from an open, persistent, crowd-sourced forecasting system can serve as a useful starting point for other classical approaches of forecasting, which in turn produce data and information to be fed back into the open system. Some forecasting methodologies can be employed to further investigate specific areas of interest and provide a deeper understanding of scenarios used to engage a targeted group for expert opinion. This approach uses both an iterative process, in which new ideas and forecasts are generated by crowds, and concept refinement, performed by experts. This feedback approach also exploits the strengths of other methodologies.