Futures studies, foresight, or futurology
is the science, art and practice of postulating possible,
probable, and preferable futures and the worldviews
and myths that underlie them. Futures studies (colloquially
called "futures" by many of the field's practitioners)
seeks to understand what is likely to continue, what
is likely to change, and what is novel. Part of the
discipline thus seeks a systematic and pattern-based
understanding of past and present, and to determine
the likelihood of future events and trends.
Primarily, a technological forecast deals with the
characteristics of technology, such as levels of technical
performance, like speed of a military aircraft, the
power in watts of a particular future engine, the accuracy
or precision of a measuring instrument, the number of
transistors in a chip in the year 2015, etc. The forecast
does not have to state how these characteristics will
be achieved.
Secondly, technological forecasting usually deals with
only useful machines, procedures or techniques. This
is to exclude from the domain of technological forecasting
those commodities, services or techniques intended for
luxury or amusement.
Methods of technology forecasting
Commonly adopted methods of technology forecasting
include the Delphi method, forecast by analogy, growth
curves and extrapolation. Normative methods of technology
forecasting — like the relevance trees, morphological
models, and mission flow diagrams — are also commonly
used.
Combining forecasts
Studies of past forecasts have shown that one of the
most frequent reasons why a forecast goes wrong is that
the forecaster ignores related fields.
A given technical approach may fail to achieve the
level of capability forecast for it, because it is superseded
by another technical approach which the forecaster ignored.
Another problem is that of inconsistency between forecasts.
Because of these problems, it is often necessary to
combine forecasts of different technologies. Therefore
rather than to try to select the one method which is
most appropriate, it may be better to try to combine
the forecasts obtained by different methods.
If this is done, the strengths of one method may help
compensate for the weaknesses of another.
Reasons for combining forecasts
The primary reason for combining forecasts of the same
technology is to attempt to offset the weaknesses of
one forecasting method with the strengths of another.
In addition, the use of more than one forecasting method
often gives the forecaster more insight into the processes
at work which are responsible for the growth of the
technology being forecast.
Trend curve and growth curves
A frequently used combination is that of growth curves
and a trend curve for some technology. Here we see a
succession of growth curves, each describing the level
of functional capability achieved by a specific technical
approach.
An overall trend curve is also shown, fitted to those
items of historical data which represent the currently
superior approach.
The use of growth curves and a trend curve in combination
allows the forecaster to draw some conclusions about
the future growth of a technology which might not be
possible, were either method used alone.
With growth curves alone, the forecaster could not
say anything about the time at which a given technical
approach is likely to be supplanted by a successor approach.
With the trend curve alone, the forecaster could not
say anything about the ability of a specific technical
approach to meet the projected trend, or about the need
to look for a successor approach. Thus the need for
combining forecasts.
Identification of consistent deviations
Another frequently used combination of forecasts is
that of the trend curve and one or more analogies.
We customarily consider the scatter of data points
about a trend curve to be due to random influences which
we can neither control nor even measure. However, consistent
deviations may represent something other than just random
influences.
Where such consistent deviations are identified, we
may have an opportunity to apply an analogy. Typical
events which bring about deviations from a trend are
wars and depressions. Thus the purpose of combining
analogies with a trend forecast is to predict deviations
from the trend deviations which are associated with
or caused by external events or influences.
As with other uses of analogy, it is important to determine
the extent to which the analogy between the event used
as the basis for the forecast, and the historical model
event, satisfies the criteria for a valid analogy.
Forecasts of different technologies
Combining forecasts of different technologies may be
even more important than combining the forecasts of
the same technology.
One reason for this is the fact that technologies may
interact or be interrelated in some fashion. Another
reason for this is that of consistency in an overall
picture or scenario. One of the simplest examples of
interacting trends is the projection to absurdity, i.e.
simply projecting the given data indefinitely without
getting any specific result. For instance, if one simply
projects recent rates of growth of world population,
one arrives at some fantastic conclusions about the
density of population in a particular place by various
dates in the next millennium.
Some other trends which can confidently be expected
to not continue indefinitely are:
1. Annual production of scientific papers.
2. Number of automobiles per capita.
3. Kilowatt hours of electricity generated annually.
Another instance of interacting trends was in the case
of the number of scientists in the U.S. growing faster
than the overall population. Since 1940s through the
1960s, science as an activity in the United States grew
exponentially. The number of dollars spent on R&D
was growing faster than the GNP (in the 1960s).
If projected indefinitely, these two curves would give
the result that eventually every person in the U.S.
would be working as a scientist and the entire GNP would
be devoted to R&D alone, which are however absurd
conclusions. Thus it is clear that the scientific discipline
of technology forecasting is not mere trend extrapolation
but also involves combining forecasts.
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