An Overview of Forecasting Methodology
1993 © David S. Walonick, Ph.D.
All rights reserved.
Survival Statistics - an applied
statistics book for graduate students.
Most people view the world as consisting of a large
number of alternatives. Futures research evolved as a way
of examining the alternative futures and identifying the
most probable. Forecasting is designed to help decision
making and planning in the present.
Forecasts empower people because their use implies
that we can modify variables now to alter (or be prepared
for) the future. A prediction is an invitation to
introduce change into a system.
There are several assumptions about forecasting:
1. There is no way to state what the future will
be with complete certainty. Regardless of the methods
that we use there will always be an element of
uncertainty until the forecast horizon has come to
2. There will always be blind spots in forecasts.
We cannot, for example, forecast completely new
technologies for which there are no existing
3. Providing forecasts to policy-makers will help
them formulate social policy. The new social policy,
in turn, will affect the future, thus changing the
accuracy of the forecast.
Many scholars have proposed a variety of ways to
categorize forecasting methodologies. The following
classification is a modification of the schema developed
by Gordon over two decades ago:
Genius forecasting - This method is based on a
combination of intuition, insight, and luck. Psychics and
crystal ball readers are the most extreme case of genius
forecasting. Their forecasts are based exclusively on
intuition. Science fiction writers have sometimes
described new technologies with uncanny accuracy.
There are many examples where men and women have been
remarkable successful at predicting the future. There are
also many examples of wrong forecasts. The weakness in
genius forecasting is that its impossible to recognize a
good forecast until the forecast has come to pass.
Some psychic individuals are capable of producing
consistently accurate forecasts. Mainstream science
generally ignores this fact because the implications are
simply to difficult to accept. Our current understanding
of reality is not adequate to explain this phenomena.
Trend extrapolation - These methods examine
trends and cycles in historical data, and then use
mathematical techniques to extrapolate to the future. The
assumption of all these techniques is that the forces
responsible for creating the past, will continue to
operate in the future. This is often a valid assumption
when forecasting short term horizons, but it falls short
when creating medium and long term forecasts. The further
out we attempt to forecast, the less certain we become of
The stability of the environment is the key factor in
determining whether trend extrapolation is an appropriate
forecasting model. The concept of "developmental
inertia" embodies the idea that some items are
more easily changed than others. Clothing styles is an
example of an area that contains little inertia. It is
difficult to produce reliable mathematical forecasts for
clothing. Energy consumption, on the other hand, contains
substantial inertia and mathematical techniques work
well. The developmental inertia of new industries or new
technology cannot be determined because there is not yet
a history of data to draw from.
There are many mathematical models for forecasting
trends and cycles. Choosing an appropriate model for a
particular forecasting application depends on the
historical data. The study of the historical data is
called exploratory data analysis. Its purpose is to
identify the trends and cycles in the data so that
appropriate model can be chosen.
The most common mathematical models involve various
forms of weighted smoothing methods. Another type
of model is known as decomposition. This technique
mathematically separates the historical data into trend,
seasonal and random components. A process known as a
"turning point analysis" is used to produce
forecasts. ARIMA models such as adaptive filtering
and Box-Jenkins analysis constitute a third class of
mathematical model, while simple linear regression and
curve fitting is a fourth.
The common feature of these mathematical models is
that historical data is the only criteria for producing a
forecast. One might think then, that if two people use
the same model on the same data that the forecasts will
also be the same, but this is not necessarily the case.
Mathematical models involve smoothing constants,
coefficients and other parameters that must decided by
the forecaster. To a large degree, the choice of these
parameters determines the forecast.
It is vogue today to diminish the value of
mathematical extrapolation. Makridakis (one of the gurus
of quantitative forecasting) correctly points out that
judgmental forecasting is superior to mathematical
models, however, there are many forecasting applications
where computer generated forecasts are more feasible. For
example, large manufacturing companies often forecast
inventory levels for thousands of items each month. It
would simply not be feasible to use judgmental
forecasting in this kind of application.
Consensus methods - Forecasting complex systems
often involves seeking expert opinions from more than one
person. Each is an expert in his own discipline, and it
is through the synthesis of these opinions that a final
forecast is obtained.
One method of arriving at a consensus forecast would
be to put all the experts in a room and let them
"argue it out". This method falls short because
the situation is often controlled by those individuals
that have the best group interaction and persuasion
A better method is known as the Delphi technique. This
method seeks to rectify the problems of face-to-face
confrontation in the group, so the responses and
respondents remain anonymous. The classical technique
proceeds in well-defined sequence. In the first round,
the participants are asked to write their predictions.
Their responses are collated and a copy is given to each
of the participants. The participants are asked to
comment on extreme views and to defend or modify their
original opinion based on what the other participants
have written. Again, the answers are collated and fed
back to the participants. In the final round,
participants are asked to reassess their original opinion
in view of those presented by other participants.
The Delphi method general produces a rapid narrowing
of opinions. It provides more accurate forecasts than
group discussions. Furthermore, a face-to-face discussion
following the application of the Delphi method generally
Simulation methods - Simulation methods involve
using analogs to model complex systems. These analogs can
take on several forms. A mechanical analog might
be a wind tunnel for modeling aircraft performance. An
equation to predict an economic measure would be a mathematical
analog. A metaphorical analog could involve
using the growth of a bacteria colony to describe human
population growth. Game analogs are used where the
interactions of the players are symbolic of social
Mathematical analogs are of particular
importance to futures research. They have been extremely
successful in many forecasting applications, especially
in the physical sciences. In the social sciences however,
their accuracy is somewhat diminished. The extraordinary
complexity of social systems makes it difficult to
include all the relevant factors in any model.
Clarke reminds us of a potential danger in our
reliance on mathematical models. As he points out, these
techniques often begin with an initial set of
assumptions, and if these are incorrect, then the
forecasts will reflect and amplify these errors.
One of the most common mathematical analogs in
societal growth is the S-curve. The model is based on the
concept of the logistic or normal probability
distribution. All processes experience exponential growth
and reach an upper asymptopic limit. Modis has
hypothesized that chaos like states exist at the
beginning and end of the S-curve. The disadvantage of
this S-curve model is that it is difficult to know at any
point in time where you currently are on the curve, or
how close you are to the asymtopic limit. The advantage
of the model is that it forces planners to take a
long-term look at the future.
Another common mathematical analog involves the use of
multivariate statistical techniques. These techniques are
used to model complex systems involving relationships
between two or more variables. Multiple regression
analysis is the most common technique. Unlike trend
extrapolation models, which only look at the history of
the variable being forecast, multiple regression models
look at the relationship between the variable being
forecast and two or more other variables.
Multiple regression is the mathematical analog of a
systems approach, and it has become the primary
forecasting tool of economists and social scientists. The
object of multiple regression is to be able to understand
how a group of variables (working in unison) affect
The multiple regression problem of collinearity
mirrors the practical problems of a systems approach.
Paradoxically, strong correlations between predictor
variables create unstable forecasts, where a slight
change in one variable can have dramatic impact on
another variable. In a multiple regression (and systems)
approach, as the relationships between the components of
the system increase, our ability to predict any given
Gaming analogs are also important to futures
research. Gaming involves the creation of an artificial
environment or situation. Players (either real people or
computer players) are asked to act out an assigned role.
The "role" is essentially a set of rules that
is used during interactions with other players. While
gaming has not yet been proven as a forecasting
technique, it does serve two important functions. First,
by the act of designing the game, researchers learn to
define the parameters of the system they are studying.
Second, it teaches researchers about the relationships
between the components of the system.
Cross-impact matrix method - Relationships
often exist between events and developments that are not
revealed by univariate forecasting techniques. The
cross-impact matrix method recognizes that the occurrence
of an event can, in turn, effect the likelihoods of other
events. Probabilities are assigned to reflect the
likelihood of an event in the presence and absence of
other events. The resultant inter-correlational structure
can be used to examine the relationships of the
components to each other, and within the overall system.
The advantage of this technique is that it forces
forecasters and policy-makers to look at the
relationships between system components, rather than
viewing any variable as working independently of the
Scenario - The scenario is a narrative forecast
that describes a potential course of events. Like the
cross-impact matrix method, it recognizes the
interrelationships of system components. The scenario
describes the impact on the other components and the
system as a whole. It is a "script" for
defining the particulars of an uncertain future.
Scenarios consider events such as new technology,
population shifts, and changing consumer preferences.
Scenarios are written as long-term predictions of the
future. A most likely scenario is usually written, along
with at least one optimistic and one pessimistic
scenario. The primary purpose of a scenario is to provoke
thinking of decision makers who can then posture
themselves for the fulfillment of the scenario(s). The
three scenarios force decision makers to ask: 1) Can we
survive the pessimistic scenario, 2) Are we happy with
the most likely scenario, and 3) Are we ready to take
advantage of the optimistic scenario?
Decision trees - Decision trees originally
evolved as graphical devices to help illustrate the
structural relationships between alternative choices.
These trees were originally presented as a series of
yes/no (dichotomous) choices. As our understanding of
feedback loops improved, decision trees became more
complex. Their structure became the foundation of
computer flow charts.
Computer technology has made it possible create very
complex decision trees consisting of many subsystems and
feedback loops. Decisions are no longer limited to
dichotomies; they now involve assigning probabilities to
the likelihood of any particular path.
Decision theory is based on the concept that an expected
value of a discrete variable can be calculated as the
average value for that variable. The expected value is
especially useful for decision makers because it
represents the most likely value based on the
probabilities of the distribution function. The
application of Bayes' theorem enables the modification of
initial probability estimates, so the decision tree
becomes refined as new evidence is introduced.
Utility theory is often used in conjunction with
decision theory to improve the decision making process.
It recognizes that dollar amounts are not the only
consideration in the decision process. Other factors,
such as risk, are also considered.
It seems clear that no forecasting technique is
appropriate for all situations. There is substantial
evidence to demonstrate that combining individual
forecasts produces gains in forecasting accuracy. There
is also evidence that adding quantitative forecasts to
qualitative forecasts reduces accuracy. Research has not
yet revealed the conditions or methods for the optimal
combinations of forecasts.
Judgmental forecasting usually involves combining
forecasts from more than one source. Informed forecasting
begins with a set of key assumptions and then uses a
combination of historical data and expert opinions.
Involved forecasting seeks the opinions of all those
directly affected by the forecast (e.g., the sales force
would be included in the forecasting process). These
techniques generally produce higher quality forecasts
than can be attained from a single source.
Combining forecasts provides us with a way to
compensate for deficiencies in a forecasting technique.
By selecting complementary methods, the shortcomings of
one technique can be offset by the advantages of another.
Difficulties in Forecasting Technology
Clarke describes our inability to forecast
technological futures as a failure of nerve. When a major
technological breakthrough does occur, it takes
conviction and courage to accept the implications of the
finding. Even when the truth is starring us in the face,
we often have difficulty accepting its implications.
Clark refers to this resistance to change as
cowardice, however, it may be much deeper. Cognitive
dissonance theory in psychology has helped us understand
that resistance to change is a natural human
characteristic. It is extremely difficult to venture
beyond our latitudes of acceptance in forecasting new
Clarke states that knowledge can sometimes clog the
wheels of imagination. He embodied this belief in his
"When a distinguished but elderly scientist
states that something is possible, he is almost
certainly right. When he states that something is
impossible, he is very probably wrong."
Nearly all futurists describe the past as
unchangeable, consisting as a collection of knowable
facts. We generally perceive the existence of only one
past. When two people give conflicting stories of the
past, we tend to believe that one of them must be lying
This widely accepted view of the past might not be
correct. Historians often interject their own beliefs and
biases when they write about the past. Facts become
distorted and altered over time. It may be that past is a
reflection of our current conceptual reference. In the
most extreme viewpoint, the concept of time itself comes
The future, on the other hand, is filled will
uncertainty. Facts give way to opinions. As de Jouvenel
points out, the facts of the past provide the raw
materials from which the mind makes estimates of the
future. All forecasts are opinions of the future (some
more carefully formulated than others). The act of making
a forecast is the expression of an opinion. The future,
as described by de Jouvenel, consists of a range of
possible future phenomena or events. These futuribles are
those things that might happen.
Defining a Useful Forecast
Science fiction novelist Frederik Pohl has suggested
that the "only time a forecast has any real utility
is when it is not totally reliable". He proposes a
thought experiment where a Gypsy fortune teller predicts
that we will be run over and killed when we leave the tea
room. If we know that the Gypsy's predictions are one
hundred percent accurate, then Pohl states that the
fortune is useless, because we would be unable to alter
the forecast. In other words, predictions only become
useful when they are not completely reliable.
The apparent paradox created by Pohl's thought
experiment is only a function of the particular
situation. The paradox exists only when 1) we want the
future to be different than the prediction, and 2) when
we believe that there is no way for us to adapt to or
affect the forthcoming changes. Pohl's thought experiment
actually doesn't even meet that criteria, since one could
present a convincing argument that it is more desirable
to spend the rest of our lives confined to the comfort of
a tea room than to leave and meet certain death.
Obviously, our new life would be difficult to accept and
adapt to, but it could be done. Prisoners do it all the
A forecast can be one hundred percent accurate and
still be useful. For example, suppose our Gypsy had told
us that after leaving her tea room we would safely return
home. Again, since we know that her forecasts are
completely accurate, we would receive emotional comfort
from her predictions. In a more tangible example, suppose
the prediction is that our manufacturing company will
receive twice as many orders for widgets as we had
anticipated. Since the forecast is one hundred percent
accurate, we would be wise to order more raw materials
and increase our production staff to meet the coming
Pohl is wrong. The goal of forecasting is to be as
accurate as possible. In the case of business demand
forecasting, it is naive to suggest that an accurate
forecast is useless. On the contrary, a more accurate
forecast enables us to plan the use our resources in a
more ecological fashion. We can minimize waste by
adapting to our expectations of the future.
It is sometimes useful in thought experiments to look
at the situation from the opposite perspective. Suppose
we know that our Gypsy is always wrong in her
predictions. Her accuracy is guaranteed to be zero. Note
that this is different than random forecasts, where she
might hit the mark once in a while. The Gypsy
sighs with relief and says that there is no fatal
accident in store for us today. According to Pohl's
reasoning, this should provide the most useful forecast
because it has the least accuracy. It's obvious, though,
that this fortune is as useless as the one where she is
completely accurate. Leaving the Gypsy's tea-room is not
something we would want to do.
If we view accuracy as a continuum, it may be that the
antonym of accuracy is randomness (instead of
inaccuracy). In this case, Pohl's theory would suggest
that random forecasts are more useful than accurate
forecasts. In demand forecasting, the degree of over- and
under-utilization of our resources is proportional to the
difference between the observed and predicted values.
Random forecasts are entirely unacceptable for this type
Pohl's thought experiment is very important because it
forces us to look at the theoretical foundations of
forecasting. First, Pohl's experiment may not be valid
because it violates a basic assumption of forecasting
(i.e., we cannot predict the future with one hundred
percent accuracy). Second, the usefulness of a forecast
does not always seem to be related to its accuracy. Both
extremes (completely accurate and completely inaccurate)
can produce useful or useless forecasts.
The usefulness of a forecast is not something
that lends itself readily to quantification along any
specific dimension (such as accuracy). It involves
complex relationships between many things, including the
type of information being forecast, our confidence in the
accuracy of the forecast, the magnitude of our
dissatisfaction with the forecast, and the versatility of
ways that we can adapt to or modify the forecast. In
other words, the usefulness of a forecast is an
application sensitive construct. Each forecasting
situation must be evaluated individually regarding its
One of the first rules of doing research is to
consider how the results will be used. It is important to
consider who the readers of the final report will be
during the initial planning stages of a project. It is
wasteful to expend resources on research that has little
or no use. The same rule applies to forecasting. We must
strive to develop forecasts that are of maximum
usefulness to planners. This means that each situation
must be evaluated individually as to the methodology and
type of forecasts that are most appropriate to the
Do Forecasts Create the Future
A paradox exists in preparing a forecast. If a
forecast results in an adaptive change, then the accuracy
of the forecast might be modified by that change. Suppose
the forecast is that our business will experience a ten
percent drop in sales next month. We adapt by increasing
our promotion effort to compensate for the predicted
loss. This action, in turn, could affect our sales, thus
changing the accuracy of the original forecast.
Many futurists (de Jouvenel, Dublin, Pohl, and others)
have expressed the idea that the way we contemplate the
future is an expression of our desire to create that
future. Physicist Dennis Gabor, discoverer of holography,
claimed that the future is invented, not predicted. The
implication is that the future is an expression of our
present thoughts. The idea that we create our own reality
is not a new concept. It is easy to imagine how thoughts
might translate into actions that affect the future.
Biblical records speak of faith as the force that
could move mountains. Recent research in quantum
mechanics suggests that this may be more than just a
philosophical concept. At a quantum level, matter itself
might simply be a manifestation of thought. Electrons and
other subatomic particles seem to exist only when
physicists are looking for them, otherwise, they exist
only as energy.
An incredible discovery was made at the University of
Paris in 1982. A team of researchers lead by Alain Aspect
found that under certain conditions, electrons could
instantaneously communicate with each other across long
distances. The results of this experiment have been
confirmed by many other researchers, although the
implications are exceedingly hard to accept. Three
explanations are possible: 1) information can be
transferred at speeds exceeding the speed of light, 2)
the passage of time is an illusion, 3) the distance
between the electrons is an illusion. All three
explanations rock our perception of reality.
David Bohm has explained Aspect's experiment by
hypothesizing a holographic universe in which reality is
essentially a projection of some deeper dimension that we
are not able to comprehend. Instantaneous communication
is possible because the distance between the particles is
an illusion. Neurophysiologist Karl Pribram has also
theorized about the holographic nature of reality. His
theory is based on a study of the way that the brain
recalls memory patterns, but the implications are the
same. Reality is a phantasm.
If reality is an illusion, then the future is also an
The phenomena of being able to see the future is known
as precognition. Most people believe that (to some
degree) they can predict the future. Fortune-tellers,
however, believe they can view the future. There is a
major difference. We predict the future based on
knowledge, intuition and logic. Precognitive persons
claim to "see" the future. Knowledge and logic
are not involved.
Throughout history, there have been many reports of
gifted psychics with precognitive powers. Through some
unknown mechanism, these people are able predict things
that will happen in the future. If we admit that even a
single person in history has possessed this capability,
then we must accept the fact that our concept of reality
needs dramatic alteration. Time itself may not exist as
we currently perceive it. Forecasting may be a method of
Forecasting can, and often does, contribute to the
creation of the future, but it is clear that other
factors are also operating. A holographic theory would
stress the interconnectedness of all elements in the
system. At some level, everything contributes to the
creation of the future. The degree to which a forecast
can shape the future (or our perception of the future)
has yet to be determined experimentally and
Sometimes forecasts become part of a creative process,
and sometimes they don't. When two people make mutually
exclusive forecasts, both of them cannot be true. At
least one forecast is wrong. Does one person's forecast
create the future, and the other does not? The mechanisms
involved in the construction of the future are not well
understood on an individual or social level.
Modis believes that the media provides the mechanism
by which social forecasts take on a creative context. In
this theory, extensive media coverage acts as a
resonating cavity for public opinion, and creates a
"cultural epidemic" that modifies social
Dublin points out that the "future has become so
integral to the fabric of modern consciousness that few
people feel compelled to question it...". Because of
the power of a prediction to affect the future, he goes
on to state that prophesy is usually a self-interest
quest for power.
The Ethics of Forecasting
Are predictions of the future a form of propaganda,
designed to evoke a particular set of behaviors? Dublin
states that the desire for control is implicit in all
forecasts. Decisions made today are based on forecasts,
which may or may not come to pass. The forecast is a way
to control today's decisions.
Dublin is correct. The purpose of forecasting is to
control the present. In fact, one of the assumptions of
forecasting is that the forecasts will be used by
policy-makers to make decisions. It is therefore
important to discuss the ethics of forecasting. Since
forecasts can and often do take on a creative role, what
right do we have to make forecasts that involve other
Nearly everyone would agree that we have the right to
create our own future. Goal setting is a form of personal
forecasting. It is one way to organize and invent our
personal future. Each person has the right to create
their own future. On the other hand, a social forecast
might alter the course of an entire society. Such power
can only be accompanied by equivalent responsibility.
There are no clear rules involving the ethics of
forecasting. In Future Shock, Toffler discussed
the importance of value impact forecasting, the idea that
social forecasting must involve physical, cultural and
societal values. It is doubtful that forecasters can
leave their own personal biases out of the forecasting
process. Even the most mathematically rigorous techniques
involve judgmental inputs that can dramatically alter the
Many futurists have pointed out our obligation to
create socially desirable futures. Unfortunately, a
socially desirable future for one person might be another
person's nightmare. For example, modern ecological theory
says that we should think of our planet in terms of
sustainable futures. The finite supply of natural
resources forces us to reconsider the desirability of
unlimited growth. An optimistic forecast is that we
achieve and maintain an ecologically balanced future.
That same forecast, the idea of zero growth, is a
catastrophic nightmare for the corporate and financial
institutions of the free world. Our Keynesian system of
profit depends on continual growth for the well-being of
individuals, groups, and institutions.
Desirable futures is a subjective concept. It
can only be understood relative to other information. The
ethics of forecasting certainly involves the obligation
to create desirable futures for the person(s) that might
be affected by the forecast. If a goal of forecasting is
to create desirable futures, then the forecaster must ask
the ethical question of "desirable for whom?".
To embrace the idea of liberty is to recognize that
each person has the right to create their own future.
Forecasters can promote libertarian beliefs by empowering
people that might be affected by the forecast. Involving
these people in the forecasting process, gives them the
power to become co-creators in their futures.
Dublin, M. 1989. Futurehype: The Tyranny of Prophecy.
New York: Plume.
Hanke, J. & Reitsch, A. 1992. Business
Forecasting: Fourth Edition. New York: Simon &
Millett, S. & Honton, E. 1991. A Manager's Guide
to Technology Forecasting and Strategy Analysis Methods.
New York: Battelle Press.
Modis, T. 1992. Predictions: Society's Telltale
Signature Reveals the Past and Forecasts the Future. New
York: Simon & Schuster.
Talbot, M. 1991. The Holographic Universe. New York:
Toffler, A. et al. 1972. The Futurists. New York:
Bibliography Related To Future Studies
J. Armstrong, "Combining Forecasts: The End of
the Beginning or the Beginning of the End?" International
Journal of Forecasting, Vol. 5, No. 4 (1989), p. 585.
The author states that research from over 200
studies demonstrates that combining individual
forecasts produces consistent (but modest) gains in
forecasting accuracy. However, the research does not
yet indicate the conditions or methods for the
optimal combination of forecasts.
L. Chimerine, "The Changing Role of Economists in
Planning," The Journal of Business Forecasting,
Spring (1988) p. 2.
The author stresses the need for additional
emphasis on cost-effective short-term planning, and
suggests that economists need to spend more time in
determining how risk and uncertainty can be
incorporated into the planning process. Changing
demographics, interest rates, world competition, and
tax changes will contribute to increased volatility
R. Clemen, "Combining Forecasts: A Review and
Annotated Bibliography," International Journal of
Forecasting, Vol. 5, No. 4 (1989), p. 559.
The author argues that forecast accuracy can be
substantially improved through the combination of
multiple individual forecasts. This paper is a review
and annotated bibliography of the literature that
supports this contention.
D. George, "Forecasting, Planning and Strategy
for the 21st Century", Futurics: A Quarterly
Journal of Futures Research, Vol. 16, Nos. 3 & 4,
(1992), p. 56.
The article is a summary and review of Spyros
Makridakis's book, which deals with the paradoxes and
challenges of forecasting. It points out the ways
that we deal with uncertainty, including 1) looking
for "quick fix" solutions, 2) limiting our
information to that which supports our ideas, values
and beliefs, 3) believing that other predictions of
the future are better than our own, 4) relying on the
past to predict the future, and 5) excessive reliance
on quantification. The importance of tailoring a
forecast to the forecast horizon was emphasized.
D. Georgoff and R. Murdick, "Manager's Guide to
Forecasting," Harvard Business Review, Vol. 1
(January-February 1986), p. 110.
The authors discuss several forecasting methods
including judgmental forecasting. The article is an
overview of the most often used business forecasting
methods, and suggestions regarding their most
E. Joseph, "Chaos Forecasting Insights," Future
Trends Newsletter, Vol. 24, No. 2, (1993), p. 1.
The article is a primer on chaos theory. It covers
the concepts of prediction, incremental change and
forecasting, complexity growth, disorder, edge
transformation, non-causal mechanisms, initial
conditions, butterfly effect, dissipation,
bifurcations, new order development, focused
coherence, evolutionary change, and strange
attractors. Chaos forecasting methods are in a state
of rapid evolution.
E. Joseph, "Chaos Driven Futures," Future
Trends Newsletter, Vol. 24, No. 1, (1993), p. 1.
The author discusses what we have learned by
examining computer models of chaos. Initial stages of
system evolution involve increasingly complex and
seemingly random developments. As chaos reaches its
maximum, the system adapts to change by
disintegration, bifurication, or self-organization
into coherent patterns. Chaos is characterized by
continuous change, disorder and adaptation.
Reinforcers and inhibitors act on initial conditions
to set in motion forecastable events. The edge of
chaos is where change becomes initialized.
E. Joseph, II, "Quality Approaches to Long-Range
Forecasts," Futurics: A Quarterly Journal of
Futures Research, Vol. 16, Nos. 3 & 4, (1992), p.
This paper explores several different approaches
to forecasting future business sales. The assumption
is that the choice of the process used to create the
forecast affects the quality of the forecast. The
author discusses six forecasting methods with respect
to their quality (accuracy), and the time and effort
required to produce the forecast. These include
spreadsheet forecasting, group meeting, trend
analysis, informed forecasting, involved forecaster,
single plan forecaster, outside opinion forecaster,
multiple scenarios forecaster, and cause and effect
R. Kidder, "Ethics: A Matter of Survival," The
Futurist, March-April, (1992), p. 10.
This paper describes the ethics problems that are
being encountered as a result of our improving
technology. The author believes that we have seen a
decline in standards because of our increases in
tolerance. He notes the decline in educational
standards and points out that students today have
questionable ethics and trust only their gut
instincts. The author started the Institute for
Global Ethics to address these concerns.
G. Land and B. Jarman, "Future Pull: The Power of
Vision and Purpose," The Futurist, July-August,
(1992), p. 25.
The authors argue that a compelling vision of the
future can pull individuals and organization to their
desired future. They suggest a few principles to
follow: know the vision and purpose, commit to
achieve your vision, experience abundance as nature's
natural state, and make the world a better place by
living according to shared values.
E. Mahmoud, "Combining Forecasts: Some Managerial
Issues," International Journal of Forecasting,
Vol. 5, No. 4 (1989) p. 599.
The author discusses our lack of knowledge
regarding the way that managers combine forecasts.
The areas especially worthy of further study are how
managers adjust quantitative forecasts, the use of
expert computer systems, and the additional cost of
producing combined forecasts.
S. Makridakis, "The Art and Science of
Forecasting," International Journal of
Forecasting, Vol. 2 (1986), p. 45.
The author discusses judgmental forecasting as an
important component for business forecasting.
Including a quantitative component reduces
forecasting accuracy. Including a judgmental
component increases the cost of forecasts. Many
executives are more comfortable using their own
judgment for forecasting.
M. Mautner, "Human Values and Technological
Advances," The Futurist, July-August. (1992),
The author, a physical chemist in New Zealand,
describes the results of a questionnaire that he
presented during a lecture in future biochemistry.
Most students expressed strong reservations against
extreme technological changes. In fact, many extreme
technological changes were not endorsed even when
their only result was beneficial to humanity. Mautner
argues that when science and spirit come into
conflict, the common sense of the majority is the
most prudent survival strategy.
T. Modis and A. Debecker, "Chaoslike States Can
Be Expected Before and After Logistic Growth," Technological
Forecasting and Social Change, Vol. 41, No. 2 (1992).
The authors describe the instability that exists
on both ends of the S-growth curve. They point out
that most chaos studies are performed after a niche
is filled. They argue that the beginning of the
growth curve also has characteristics of chaos,
however, it is more difficult to recognize because
negative excursions have no meaning. The size and
steepness of the precursors can be used to predict
the steepness of the growth that will follow.
B. Nanus, "Visionary Leadership: How to Re-Vision
the Future," The Futurist, September-October,
(1992) p. 20.
The article discusses the need for a changing
vision in a changing world. Seven suggestions are
presented for the "prudent visionary". 1)
Involve others in the creation of your vision. 2)
Make sure the vision is attainable. 3) Reduce the
possibility of unpleasant surprises. 4) Be prepared
for the organization's resistance to change. 5) Focus
on doing the right thing, instead of the bottom line.
6) Be flexible and patient while creating the vision.
7) Implement change when things are going well,
rather than waiting until there is a crisis.
F. Pohl, "The Uses of the Future," The
Futurist, March-April, (1993), p. 9.
The author, a science fiction writer, argues that
the more accurate a prediction is, the less useful it
might be. A prediction that is made with complete
certainty offers no way to impact or modify the
future. The paradox, of course, is that the future is
changeable only to the degree that our forecasts are
unreliable. The point is made that each individual
can impact the future and the futurists need to take
G. Swenson, "Fuzzy Logic: A Tutorial &
Trends," Futurics: A Quarterly Journal of Futures
Research, Vol. 16, Nos. 3 & 4, (1992), p. 45.
The author describes the evolution of fuzzy logic
since its introduction in 1965. Unlike binary data,
fuzzy logic is way of processing imprecise data. It
accounts for variability by reducing the input data
to a few membership sets, and then considers the data
from the perspective of how well it belongs to the
set. This process, known as fuzzification, is
followed by a rules matrix, and finally
defuzzification, where the control outputs are based
on the intermediary decisions.
R. Theobald, "Leadership in the New Era," The
Futurist, May-June, (1991), p. 59.
In this short excerpt from his book Turning the
Century, the author argues that there are two
rules of human behavior: 1) People make decisions
that are in their own self-interest, and 2) people
change only when they see the possibility of more
satisfying choices. Theobald states that we need
leaders who will break our social problems into
manageable chunks for the purpose of better public
perception. He states that the way to introduce
social change is to make people recognize that it is
in their own self-interest to alter their behavior.
Using the example of new smoking regulations, it
would appear that he believes in a majority-rule