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In the same way than when looking at present events and products, your view into the future can be either informative or normative. Their difference is that in the former case you accept the future as it comes and in the latter case you wish to change it.
The informative or descriptive vista to future usually aims at finding out the most probable future. This view is conventional when you cannot affect the future. You just want to know it so that you can prepare yourself to the inevitable, like tomorrows weather. This approach has traditionally been called forecasting, or predicting. It tries to answer the question: "What happens to object A in time B if the evolution continues without interventions?"
Another, less common variant of descriptive approach is the utopia, a detailed narrative of a possible or "hypothetical" future which need not be the most probable one. The writers of utopias, beginning from Plato and Thomas More, usually leave the question of probability to the discretion of their public. The novels of Jules Verne have shown that fiction can sometimes emit constructive ideas to the developers of factual new products. A few large companies have lately found out that they do not need to rely on sci-fi writers in the creation of utopias. They have themselves started concept design projects which do not aim at creating real products but just generate ideas for imaginable original products in the future, to be used in strategic planning, for internal education of personal and for publicity. These visions often include utopias of potential future ways of living, where completely novel products of the company may find a market.
An utopia can also be written as a warning, an offending example to be avoided, in the style of the nightmare-like novel 1984 by George Orwell.
Normative approach to the future means that you think that you can affect the development. You perhaps know which kind of future you want, but you are not sure of the best method of obtaining it. Normative study of future tries to answer questions like:
Normative forecasting of the latter type above often continues as detailed planning of the future activity (see Developing an activity) or as Design of objects to be used in future.
The normative view necessarily involves evaluations, and it becomes necessary to define the people whose point of view shall be used in evaluation, cf. Preparing Design Theory or Relevant points of view in product development. Usually only the final state of the predicted or planned development is evaluated, in other words the internal procedures of normative forecasting seldom include subjective evaluations. These phases of normative forecasting thus resemble the informative approach, and same methods can be used in both types of forecasting.
Quite a number of alternative methods are used in forecasting, depending on the nature of the information that is available for underpinning the forecast. Generally two types of information are simultaneously needed as a basis of a forecast:
The general models that are needed in predicting have to be detected by previous research of the phenomenon or of other, similar phenomena, but this research need not always be done by the same scientist that produces the forecast. In effect, most predictions are made on the basis of general theory, i.e. using well known invariances that already long before have been discovered and published by other researchers.
The models that are used as a basis of forecasting are of different types, and accordingly there is no universal method of forecasting, as can be seen in the table below.
| The model used as the basis of forecasting | Method of forecasting |
|---|---|
| Expertise knowledge (tacit knowledge may also be used) | Delphi Method |
A model based on another
comparable system
|
Analogy Method |
Trend, the recent development in the system to be predicted, as defined by a series of observations, e.g.:
|
Extrapolation
- from the last observations, or - from all the findings - perhaps within limits |
| A statistical association between the variables to be predicted | Applying a Statistical Model |
An explanation for the phenomenon to be predicted:
|
Applying a Causal Model |
Every forecast is a theoretical composition in the same way as the general models that it is based on. However, the forecast exists not on the same "level of generality" as the universally valid theories exist. Instead, the connection between a forecast and the object that it describes is quite direct, in the same way as it is in case studies which describe just a few objects. In the diagram on the right it is called the "level of case studies".
All the above methods will be discussed below. In some cases it may be possible to combine some of the methods to improve the reliability of the prediction. Further, after the presentation of the methods there is a discussion on the available ways of assessing and describing uncertainty of forecasts.
The most primitive method of forecasting is guessing. The result may be rated acceptable if the person making the guess is an expert in the matter. An important thing to note is that guessing is the only method where we can make use of tacit knowledge that the specialist has not been able to express as exact words or numbers. Generally the best method for eliciting such a forecast from the expert is an unstructured interview. The method of interviewing allows you to inquire into the reasons and explanations for the presented forecast which you might also choose to criticize and thus try to reach an improved forecast. When interviewing an expert you may also learn something that you can later use if you prefer to construct your own forecasts with other methods.
Sometimes at least some of the experts live far away and they are thus difficult to interview. (Examples of potential sources of specialists can be found under the title Populations of Evaluators.) To consult such experts, you may resort to a questionnaire instead of an interview. If you wish to question several persons simultaneously, you may consider using the Delphi method.
In the Delphi method, the researcher directs identical questions to a group of experts, asking them to give their guesses on the future development of the specified topic. In the next step, the researcher makes a summary of all the replies he has received. He then sends the summary to the respondents and asks if any of them wants to revise his original response. If the respondents are amenable to the extra effort, they may be asked to justify their opinion, especially if it differs from that of the majority.
As it is difficult to make summaries of other than quantitative responses, the questions that are used in the Delphi process are usually quantitative, e.g. "What will the price of crude oil be in 20 years?" On the basis of this type of responses, the researcher will be able to calculate e.g. the means and the ranges. One advantage of the method is that you can readily use the range of the responses as a first measure of the reliability of the forecast.
Nothing prevents using qualitative or any other type of questioning if the nature of the object so requires. If you wish to do so, it is advisable to plan in advance the method of making the summary of responses, otherwise it will be difficult to arrange the second round of the questionnaire.
The Delphi procedure is normally repeated until the respondents are no longer willing to adjust their responses.
The Delphi method is not very reliable. Results of Delphi questionnaires are often later found to have predicted the real course of events remarkably badly. Wrong guesses are often made by renowned specialists and sometimes even by a majority of them, and the odd person who is later found to have predicted right would perhaps never have been elected to the Delphi group of experts. "If you had predicted the collapse of the Berlin wall one year before it happened, that would have proved that you are no expert in politics."
Most forecasting methods make use of some kind of a model that is assumed to portray the relations between the various aspects and attributes of the "system" whose development shall be predicted. Simplest and probably the oldest method of acquiring such a model is available when the system to be predicted belongs to a class of comparable systems whose members normally follow similar patterns of development which are known. For example, the life span of an animal follows usually the same pattern that is typical for the species. If you know this pattern, it may even be possible to forecast without having any explicit theoretical formulation of the pattern: you simply regard an earlier observed specimen of the pertinent class as a model of development. Already in Hippocrates' time physicians knew the typical process of many illnesses, and when observing the initial symptoms of such a process in a patient the physician could predict the progress of the disease.
If you wish to use the method of analogy, you thus have to find an earlier "foreign" system that is similar to the now existing "home" system whose future you want to forecast. The foreign system must have already undergone the phase of development that you are trying to predict for the home system. In other words, it must have reached a "later" or "more mature" stage in development than the home system.
Often it will be impossible to find a foreign system that would be absolutely identical to your home system. At least the environments of the systems differ. The systems are then just "analogous". An obvious difference is that the foreign system has been observed in the past and the home system is to be continued into the future. This is a difference that you cannot help, but many other divergences like for example dissimilarities between the sizes of the systems can be eliminated by making suitable corrections.
The process of forecasting through analogy is:
Typical instances of the analogy method are forecasts of national economies. The U.S.A. or another suitable "developed" country is taken as the foreign system, and this model is then applied to predict the national economy of a "less developed" country. Variables that are predicted in this way often relate to industrial production, to the Gross National Product, or to consumption like the number of cars and the amount of traffic.
The analogy method is not restricted to quantitative data. In fact, it can handle any format of description of a temporal development. An example of qualitative forecasting can be found in Oswald Spengler's (1880 - 1936) book Untergang des Abendlandes (1918, 1922) which explains the typical development of the ancient cultures of China, Egypt, Rome and a few others which have flourished in their time and then withered away. Spengler found that cultures are processes that share a common model of development. He then made the prediction that the Western culture which is still under development will follow the same pattern. In this part of his treatise, Spengler thus created an analogy between objects of the same category (i.e. between cultures).
Moreover, Spengler (and likewise Arnold J. Toynbee in the book A Study of History, 1935-39) drew the analogy further and asserted that the pattern of cultural development is also analogous to the succession of seasons, i.e. spring, summer, fall and winter, and even to the lives of plants and animals which include the phases of birth, maturing, bloom, decline/decadence and death. In other words, Spengler extended the analogy from one species of systems (cultures) to another (to seasons, or to animals).
Another example of an analogy between objects of different category is Alvin Toffler's book The third wave (1980), where an analogy to waves is used to describe and predict the evolution from agricultural to industrial society, and later to the information society.
Indeed, bold analogies between objects of different types (see examples of these) can sometimes generate fertile hypotheses for discussion, but if you just wish to make a plausible forecast it is usually safer to restrict the analogy to a single class of objects only. Think if you tried to predict the development of cars by making an analogy to computers? You might conclude that cars should soon run at 10.000 mph, while their weight dwindled to a few grams?
Even those analogies that keep to a restricted class of objects often suffer from various irregular factors that affect the home system differently than the foreign one. You can try to diminish their influence by using more than one foreign system, if available. In other words, you make parallel forecasts and combine the results. Still better, if you can find the general pattern that all the systems follow; if it is possible you can shift to the more reliable forecasting methods of Applying a Statistical Model or Applying a Causal Model.
One more weakness of the analogy method is that it is difficult to assess the uncertainty of its results.
Extrapolation is the most usual method of forecasting. It is based on the assumption that present development will continue in the same direction and with unvarying speed (or alternatively, with steadily growing or diminishing speed, i.e. a logarithmic extrapolation).
The basis of an extrapolation will be knowledge on the recent development of the phenomenon. You will need at least two (although usually you have more) sequential observations made at known points of time. The principle is shown in the figure on the right.
You will have the option of measuring the difference d as an absolute or as a proportional change. Absolute measurement means the same as even development, i.e. change in constant speed. Proportional measurement, e.g. "10% increase to the preceding observation" means that the pace of change is increasing (or decreasing). This alternative is sometimes called "logarithmic extrapolation", see figure below.
If you have more than two observations, you
have the option of choosing the number of observations that you will
base the extrapolation on. If you feel that the very last observations have
better predicting capacity than the earlier ones, you may prefer to
disregard the earlier observations. An alternative is to give more weight
to the later observations than to the earlier ones. If you decide to use a
large number of observations (in other words, you are extrapolating the
trend) you will probably wish to make the
calculations with a regression analysis
program if your data are quantitative.
In the examples above the observations that will be extrapolated are recorded as quantitative variables. In other words, a time series is extrapolated. In addition, a numerical forecast is often explained in qualitative verbal terms as well, to make it easier to comprehend. An example is the book Megatrends by Naisbitt (1982).
Nevertheless, nothing prevents to extrapolate trends that are described partially or entirely in qualitative terms. Moreover, it is often practical to describe existing products with the help of pictures and other icon models, and this mode of presentation is
serviceable even for extrapolations. In the book Industrial Design,
Raymond Loewy (1979) thus combined two directions of view: historical and
predictive. On page 74 of the book we find an "Evolution Chart of Design" which shows the development from 1900 to 1942. The last picture is Loewy's forecast which he created on the basis of the trend in the entire series, the main trend being here a gradual shift to more streamlined design.
The innate weakness of all extrapolation is that it only can comply with such processes or forces which already are in operation. It always ignores those new impacts that begin to apply only in the present or in the future. Often there will be gradually more and more such new impacts in the future, and in such a case extrapolation can give useful results only for a relatively short period.
Another weakness is that it is almost impossible to assess the probable error of an extrapolation. A rough notion of it can be obtained by studying the consistency and homogeneity of the series of the original observations.
| high
pressure |
fine
weather |
| low
pressure |
foul
weather |
A statistical model is based on a series of observations on the phenomenon, and it delineates the pattern of the association between the various factors or variables of the phenomenon that are of interest. This association needs not be an absolute one. A small number of exceptions to the general rule reduce, but do not entirely spoil, the predictive ability of the model.
The descriptive models that are used in
forecasting are often quantitative, but qualitative ones are used as well.
Indeed, any of several model languages
can be used. For example, verbal aphorisms were used as the
basis of weather forecasting already long before barometers: "Red sky
at night, sailor's delight; red sky in morning, sailors take warning."
And likewise: "Ring around the moon, brings a storm soon."
Today, the tendency goes in quantitative direction but note that in
the development and forecasting of products there are many aspects that
can be expressed only qualitatively or graphically.
The method of predicting on the basis of a descriptive model is
simple, as can be seen in the diagram on the right. If one of the variables
in the model is time, it is only necessary to insert any chosen
future time point in its place and then read the "value" of the desired
variable in the model. ("Value" is in quotes here because in qualitative
models its contents are not numerical.)
There is also another way of using the model, and it is feasible even when the model does not include time as variable. This method was already used in the case of the barometer, above.
| rising
pressure |
improving
weather |
| sinking
pressure |
worsening
weather |
In this method the focus is on the
direction of change of the variables or factors like in the table on
the right. We need a recent observation on the now prevailing direction of
change of the factor that is not being predicted (here: the air
pressure). From these data we can deduce the anticipated change of the
factor to be predicted (the weather) and finally its state at the end of the
predicted period.
Note that this method includes extrapolating the factor that is not
being predicted, which means assuming that its change will remain
constant during the span of forecast.
Forecasting on the basis of statistical models is feasible even when
you do not know the reason or explanation of the mathematical
association you have found in the historical data. The method might give
a right prediction even in such a case when your assumed explanation of
the existing statistical association is quite erroneous!
Famous historical examples of predicting on the basis of
mathematical models only were astronomical calculations in ancient
Mesopotamia, and those of Ptolemy the Greek. Most, perhaps all, of
these early scientists believed that the Earth was the centre of the
universe, and the sun, the moon and the planets were just moving
around it. Nevertheless, the mathematical models of these apparent
movements were accurate and yielded correct predictions of the rises
and sets of the sun and the moon, and even of their eclipses.
Quantitative descriptive models consist of variables and an expression which defines their relation to each other. This relation is sometimes called statistical association in order to emphasize that it originates from statistics i.e. a series of observations. For quantitative models this association is normally expressed as an equation, e.g. of the type y = ax + b. In the section on extrapolation we already discussed two types of associations: the linear and the logarithmic trends. Below, there are a few other usual types of relations:
There are great risks in forecasting without knowing the reasons for the statistical associations. For example, scientific forecasts of national economies are notorious for their low reliability, which, of course, is due to missing insights in the factual relationships of the variables of economy. Generally, you should always try to find out the rational explanation behind the statistical association that you are using as the basis of your forecast. It is always safer to forecast on the basis of a causal model (described later), than to forecast on the basis of statistical associations only.
The most accurate method of prediction, i.e., the causal
model, becomes possible if you have, through research, obtained a
model which not only describes (as in the previous section) the
development of the phenomenon to be predicted but also explains it. The explanation can be either causal, through motive or function or any other type of
reasoning. This means that we know the dynamic invariance of change in the process going
on. Usually (though not always) such invariance remains valid also in the
future and thus it gives good grounds for prediction. The weather, for
example, is no more predicted on the basis of air
pressure only; today we know the invariable structure of moving
cyclones (fig. on the right) which explains the changes both in air
pressure and in weather. Even the proverb about red
skies has now been given an explanation:
"Because the weather patterns in North America generally move from west to east, when clouds arrive overhead at sunrise the sky will appear red, signalling a storm "moving in". When the storm eventually passes, the sky will clear in the western sky. If sunset occurs simultaneously, the light will cast a red glow on the clouds above, now moving towards the east." (Cited from Gene Rempel and Mike Hanson.)The method of forecasting on the basis of a causal model does not much differ from the use of a statistical model. In the best case one of the variables in the model is time: then you just insert the right year into the model, and it immediately becomes the desired forecast.
The method that is discussed above may seem to pertain to quantitative models only; however the same principle can be applied when predicting on the basis of qualitative models which have explanatory power. Examples of them are found in History That Explains.
The causal model is
often so complicated that it is best managed by using a computer. Even
then, you will usually need an illustrative presentation of your model to
clarify your thinking and finally to be presented in the report. In such an
illustration you will need a notation system to describe the
various logical relations between the variables. The computer program
will often be able to print out the model, using its in-built notations. If you
can find no suitable ready made notation systems, you can devise one.
A famous example of a large causal model was fabricated by the
so-called Club of Rome in 1972. This model, published in the book
The Limits to Growth, consists of dozens of variables, including
the world population, birth rate, industrial and agricultural production, the
non renewable resources, and pollution. In the model, the levels,
or physical quantities which can be measured directly, were indicated
with rectangles
, rates that influence those levels with
valves
,
and auxiliary variables that influence the rate equations with circles
. Time delays
were indicated by sections within rectangles
. Real flows of people, goods,
money, etc. were shown by solid arrows
and causal
relationships with broken arrows
. Clouds
represent sources or "sinks" (exits of
material) that are not important to the model behaviour.
The Club of Rome started building their "World Model" by first constructing five sub-models. These concentrated on the five "basic quantities": population, capital, food, non-renewable resources remaining (measured as now remaining fractions of the 1900 reserves), and pollution. One of the sub-systems included the causal relations and feedback loops between population, capital, agriculture, and pollution (fig. on the right). Finally the researchers combined all the five sub-models and thus created the final World Model, part of which is illustrated below.
When using a causal model as the basis of your prediction, you should keep in mind that the model has normally been produced by studying a certain population, which means that the model is valid only in that context. You should not generalize too unscrupulously and assert that the model will also be valid in the future environment that you are forecasting.
If you nevertheless intend to do just that, you should carefully consider the following:
It is often advantageous to use one
method for the short time part of the forecast and another one for the
long-term period. For the near future linear extrapolation is often useful,
while it often happens that common sense, research, or other source of
general knowledge tells you that the evolution that you are forecasting is
subject to pre-set limits or laws which dictate not the nearest events but
rather a more distant future. You may, for example, be studying the
growth of a plant knowing that the steady growth will eventually reach an
end.
If that is the case, you may combine two forecasting methods: you
extrapolate just the nearest values, while basing the forecast of
the later values on a general law. Typical examples of such long
term developments are:
The s-curve is usual if the growth has natural limits, as is the case with plants and with the natural resources of the earth,There are not too many methods for predicting the
reliability of your predictions. One of the best is triangulation:
making parallel forecasts with different methods if it is possible. If
different methods lead to dissimilar forecasts, it gives an idea of the
range of the uncertainty.
Sensitivity analysis is another method which, however,
works only with numerical models. Most forecasting methods allow you to
calculate what the result will be if one of your starting assumptions or one
item in the input data is varied. Or, if you believe that you know the
probable error of one of your assumptions, you may use this
knowledge to calculate the probable error of the resulting forecast.
Once the researcher has developed for himself an approximation of the likelihood of the forecast, the next task is to disclose this likelihood to his public as well. Many usual methods of presenting the forecast (like diagrams) are very exact, indeed their very exactness often badly corresponds to the uncertainty of the forecast. Instead, the researcher should select such a presentation of the forecast which gives the right impression of the degree of uncertainty. There are, indeed, a number of methods which can be used to describe the probable error or likelihood of a forecast:
A fuzzy scale. For example, the Club of Rome
deliberately chose to omit the vertical scales of the variables and also
made the horizontal scale somewhat vague (consisting of just the values
1900 and 2100). This was because they wanted to indicate that the
numerical values were approximate (ibid., p. 123).Parallel scenarios are quite easy to fabricate if you have a mathematical model as basis for the
forecast: all that is needed is to feed in the model several alternative sets of data. For example, the above mentioned Club of Rome made a series
of scenarios by feeding different data into the single causal model of the pertinent relations, shown earlier.
Their "standard" scenario, shown above, assumes that all the variables follow their historical values from 1900 to 1970. Food, industrial
output, and population grow exponentially until the rapidly diminishing resource base forces a slowdown in industrial growth. Population growth is finally halted by a rise in the death rate due to decreased food and
medical services.
There is another scenario from the same book (fig. 36) on
the left. Here the assumed resource reserves were doubled, while all the
other assumptions were kept identical to the "standard" scenario.
Industrialization can now reach a higher level. The larger industrial plant
releases pollution at such a rate, however, that the absorption
mechanisms of the environment become saturated. Then pollution
causes an immediate increase in the death rate and a decline in food
production.
A third scenario from The Limits to Growth (fig. 44,
on the right) is identical to the "standard" one, except that the population
is assumed to stay constant after 1975. The industrial output continues to
grow exponentially until the depletion of non renewable resources brings
a sudden collapse of the industrial system.
Sites in the WWW on forecasting:
On the questions of reporting, a separate chapter is included.
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March 29, 2005. Original location:
http://www2.uiah.fi/projects/metodi
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