The Limits
of Quantitative
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with trend extrapolation as an approach to future
prediction. But we dealt essentially with problems
in single-issue projection. Is it not perhaps possible
to consider the simultaneous interaction of many
variables, perhaps with a complex statistical model
backed by computer power, and so achieve a trend
projection we can rely on? In this chapter we con-
sider where this is possible and useful, and where
it is not.
Statistical Analysis and Quantitative Modeling
Our lives and decisions are dominated by statistics. For better or
worse, quantitative analysis has become the authoritative form of
knowledge. In fact, this is a relatively new phenomenon in the his-
tory of human thought. It is only since the nineteenth century that
statistical analysis has come to stand at the core of the way we
think about the world, and alternative forms of investigation—judg-
ment, experience, and intuition—have been pushed into the back-
ground. But the pendulum has swung so far that, in our era, not
only have quantitative approaches become central to how we in-
vestigate complex situations, but also unless something is numer-
ically studied, it is almost “not knowledge.” Economics, once an
arena of social analysis, has become a field of turbo-math, while
management academics produce papers that more closely resem-
ble particle physics than anything real managers actually do. This
pattern is repeated across much of psychology and the rest of the
social sciences.
Ready access to computer power, allowing us to do more with
numbers, has greatly facilitated this shift. So it should come as no
surprise that many people look to sta-
tistical analysis or quantitative meth-
ods, and particularly computer-driven
projective modeling, to solve the
conundrum of predicting the future.
Software developers and entrepre-
neurs have taken up the challenge to
develop computer-driven forecasting
methodologies with alacrity. Future-
oriented number-crunching software programs, with such names
as Autocast, ForecastX, Forecast Pro, and SmartForecasts, are often
lavishly advertised to corporations and other institutions that have
So it should come as no sur-
prise that many people look
to statistical analysis or
quantitative methods, and
particularly computer-driven
projective modeling, to solve
the conundrum of predicting
the future.
an interest in anticipating and evaluating change. Forecast Pro’s
advertising text is typical of the kind: “With Forecast Pro, you pro-
vide the historic data for the items you are forecasting and Fore-
cast Pro does the rest. The built-in expert selection mode analyzes
your data, selects the appropriate forecasting technique and calcu-
lates the forecasts using proven statistical methods.”
All computer-driven quantitative modeling generates future pro-
jections through predictive algorithms, based on mathematical re-
lationships between variables derived from analysis of past data.
Patterns and associations among variables and multiple relation-
ships of cause and effect (causal and dependent variables) may be
derived, for example, by regression techniques, to determine what
mix of causal influences are at work on any perceived outcome
variable, and the degree of influence of each on the outcome. (A
slump in sales may be attributed to many factors: less advertising,
placement of outlets, lower consumer spending, competitive influ-
ences, distributor discounts, product options, changes in house-
hold income, etc. A regression can help determine what mix of
causal factors in what proportion has caused this over time.)
Modeling techniques, including econometrics, set the deduced
coefficients between variables into complex algorithms that math-
ematically define the relationships between variables and set deci-
sion points about which variables affect which others, under what
conditions, and by how much. With this, the modeler can project
into future time, allowing us to watch the future evolving on a
computer screen.
The most common form of quantitative projection is based on a
“time-series analysis,” where a sequence of data points measured
at successive and preferably uniform time intervals has been col-
lected. Using the standard measure of time as a base, analysts cre-
ate a mathematical curve that approximates the data evolution to the

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