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The Millennium Project Futures Research Methodology—V3.0
HEURISTIC MODELING
by
Sam Cole1
I. Background
II. An Outline of the Method––Interface and Equations
III. How the Method Is Used
IV. Cross-checking with History
V. Strengths and Weaknesses
VI. Possible Evolution of the Method
References
1 Sam Cole, Department of Urban and Regional Planning, University at Buffalo. This paper is based on the author‘s
contribution to Dator, J. 2002. Advancing Futures: Futures Studies in Higher Education. Praeger. London, and
―Global Issues and Futures: A Theory and Pedagogy for Heuristic Modeling‖. Futures, 40, 2008, 777-787.
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Acknowledgments
This chapter has benefited from peer review comments by Theodore J. Gordon, Senior Fellow,
The Millennium Project; Jose Cordeiro, Chair, and The Millennium Project Node in Venezuela;
and Jerome Glenn, director, The Millennium Project. Special thanks to Elizabeth Florescu and
Kawthar Nakayima for project support and John Young for final proofreading.
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History/Trends
Dialogue/Delphi
Heuristic Model
Future Scenario
I. BACKGROUND TO THE METHOD
The heart of the method described in this chapter is a rather simple computer simulation model
based on a matrix of interactions between so-called ―hard‖ and ―soft‖ variables. This model is set
within an interrogative framework shown in Figure 1 that involves other semi-quantitative and
discursive methods. Since most readers will be familiar with these contextual methods, the focus
here is on the core component––the heuristic model. Only the manner in which other methods
interact with the model will be explained here. As an illustration, an example based on the
―standard‖ economy-demography-environment––technology sub-systems considered in the COR
(Club of Rome) models and integrated assessment models is presented. The additional culture-
conflict-knowledge-society variables and their relationships emphasized in the heuristic model
are more novel, especially since these less easily measured variables are treated as equal partners
throughout the futures exercise.
The approach brings together various futures methods, and, like most other methods, has
adapted, or evolved from previous contributions, rather than being ―invented‖ from scratch. It
has been influenced by a variety of factors, personal, academic, and professional. As a
professional futurist and academic, I have had opportunity to construct or review many types of
models addressing a wide variety of problems. While I am convinced that useful results can be
obtained from quantitative models, it is clear that there are diminishing returns to modeling
effort. Moreover, there is a tendency to focus on the relatively few things that we can measure
and leave aside those that we cannot. Ironically, in the real world key decisions often rest on
highly questionable qualitative assertions, that are not easily incorporated into empirical models.
Figure 1. The Contextual Framework for Heuristic Modeling
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The objectives and elements of the method described here are quite similar to other now-
established heuristic methods, notably Delphi surveys, scenario building, simulation modeling, and
cross-impact analysis. In their various manifestations, these all seek insights into the variety of
possible outcomes from the interaction within a broad range of variables, adopting some element
of ―quantification‖. The core of the method is a simulation similar to KSIM and System
Dynamics, and especially to the Jay Forester World Dynamics model that fired up the Limits to
Growth debate. Indeed, the core of the method is a simulation model – designed to be
complementary to, and integrated with, a variety of other futures methods and to bridge between
qualitative scenarios and quantitative modeling, (such as World Futures: The Great Debate, and
Worlds Apart). The goal is more limited than most of these studies in that the model is as much a
device to provoke discussion and pose questions, originally in a classroom environment. In this
context, the key problem was to find a device that was cheap, straightforward, and with a fast
learning curve. The solution was to adopt minimalist equations, data input, and a graphical
interface.
The application used in this chapter, combines ―harder‖ economy-population-environment
variables, with ―softer‖ culture-conflict-knowledge related variables. The former draw on The
Club of Rome Limits to Growth studies and the Sussex critique of that study. The other
components were set out in a project for UNESCO on Cultural Diversity and Sustainable
Futures, and later work for the UN Commission on Culture and Development.
II. AN OUTLINE OF THE METHOD––INTERFACE AND EQUATIONS
The ―modeling‖ component of the heuristic method is a rather undemanding computer model,
whose naivety is somewhat compensated for by the way it is used and contextualized. The goal
was to present the whole model – inputs, outputs, and instructions via a single screen window,
shown in Figure 2
As indicated above, the overall approach employs a combination of futures methods. The
Dialogue/Delphi box in Figure 1 indicates some kind of group discussion designed to identify
and generate inputs to the model, to review results, and to modify assumptions and relationships.
This may involve several approaches depending on circumstances, face-to-face discussion,
survey, even a formal Delphi. The dialogue poses a set of questions, initially to identify variables
and relationships to be incorporated in the model, and also desirable outcomes to be explored.
This provokes, on the one hand, the challenge to find evidence for hypotheses in historical trends
or examples to provide ―data‖ for the model, and on the other hand, even greater challenge to
discover a set of policies that will bring the model output (future scenarios and trajectories) to
some preferred result.
The central feature is the simple computer model, whose interface and equations are now
explained. The interface shown in Figure 2 includes three tables for inputting data, three graphs
for viewing results, and three controls for setting time horizons and variability. Primary data are
entered into the Trend/Interaction Matrix (yellow table) using a mouse (left click selects and
raises entries, right click reduces entries). In this illustration, arbitrary amounts have been
entered for three variables (Items 1 to 3). This table is also used to name items, to increase the
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number of variables, and so on. Immediately as the data are entered or changed, the model
calculates their interactions and resulting trends up to a specified time horizon. This is shown in
the large chart. The smaller bar in the Figure 2 chart shows the mutual influence of variables on
each other at the time horizon. The data in the Trend matrix represent ―past and present trends‖.
The second Policy Matrix (blue table) is used to input ―policies‖ designed to bring these trends
to some more desirable outcome. The third Uncertainty Matrix (mauve table) is used to prescribe
levels of uncertainty and response lags associated with each variable. The matrix may be
expanded to accommodate many variables, and the tables adjust and scroll accordingly.
Figure 2. Model Interface and Simple Example
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The entries in the Interaction Matrix represent the share of year-to-year change (as a percentage)
in each variable that can be attributed to the other variables at the starting time period when all
variables have a value of unity. Thus, the level of variables is measured relative to their size in
this base year, say the year 2000 (i.e. the Millennium). In Figure 2, the entry 1.5 in the top-left
says that Item 1 (population, for example) has an autonomous growth of 1.5% due to itself. This
positive growth is offset by the negative influence of -2% from Item 2. The situation of Item 2 is
the reverse with negative (-1%) autonomous growth, but an initial stronger influence (1.5%)
from Item 1. Item 3 has no autonomous growth but a net negative influence from Items 1 and 2.
The result shown in the main chart is that Items 1 and 3 show decline while Item 2 grows until
the negative influence of other items overwhelms this trend.
Growth of Item 1 = Contribution from Item 1 to its own growth
+Contribution from Item 2 +Contribution from Item 3
To calculate the trends in each item we update from year-to-year so for each variable,
Next year level = this year level + this year growth
An additional term representing ―uncertainty‖ is added to show, depending on how data are
generated, the level of systematic, statistical, empirical, and theoretical ambiguity in the figures.
Thus, the model is merely a set of linear equations directly or indirectly linking each variable
with all others. From year-to-year (or whatever time step is used) the change in a variable
depends on the current level of other variables. The net change in each item is measured as its
annual rate of change and the interactions are measured in terms of contributions to annual
growth (or decline) in the level of each item. The direction of change is positive if increasing the
contributor (the row variable) is expected to increase the level of the column variable. The
formal relationships of the model are given by Equations 1 to 3 below. The full model then
becomes:
L1(next year) = L1 + (I11 x L1) + (I21 x L2) + (I31 x L3) + E(L1) (1)
L2(next year) = L2 + (I12 x L1) + (I22 x L2) + (I32 x L3) + E(L2) (2)
L3(next year) = L3 + (I13 x L1) + (I23 x L2) + (I33 x L3) + E(L3) (3)
where L1 et al. are the current levels, I21 et al are the entries to the table, and E(L1) are the
uncertainties in the levels. The initial lags are one period: when these are changed, levels become
a weighted average of previous levels. As explained above, in the cross-impact matrix, the base
year levels are all set to 1 (unity). Although the model is linear and so generates geometric
(approximately exponential) growth trajectories, combinations of relationships induce reversals
of direction as well as pace. Up to a point, therefore, non-linear complex relationships may be
deconstructed into a sequence of equations via intervening variables. These various restrictions
simplify data entry, graph scales, and so on, and allow the interface––input, output, and
instructions – all to be accommodated within a single screen window. The programmed model
makes use of some of the features in Visual Basic; for example, selecting matrix items also
brings various information (―Hints‖) to the screen as shown in Figure 3.
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Figure 3. Information and Hints
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Before using the model in a futures exercise, participants are recommended to input exploratory
data into the model in order to become somewhat proficient with its components, and to better
understand basic forecasting issues (for example, what a 2% per annum compound growth rate
over 50 years implies).
III. HOW THE METHOD IS USED
The principal goal of the exercise is to provoke dialogue amongst a group of ―participants‖.
Developed for class-room use in a course on Global Issues and Futures (GIFS), the participants
were Masters-level planning students. The idea was to encourage discussion about global issues,
provide a nexus for reviewing readings and information, including materials on the Millennium
Project‘s website. Readers will recognize that the questions posed by and to the students are
similar to those that have appeared in the Millennium Project outlook surveys. Indeed, each year
these surveys have been used to illustrate a variety of methodological and content issues. In this
sense the students provided a ―panel of experts‖ to mimic real-world gurus who advise
governments, or contribute to the Millennium Project activities.
There are obviously many ways in which this part of the exercise might be managed, face-to-
face or through the Internet, through formal sample surveys, or through ad-hoc groups. In the
GIFS case, the course began with seminars and guest lectures, films, and readings, and
exploration of issues on the Web, culminating with group activities and presentations. The final
product was a ―scenario‖ devised by the group with individual students being responsible for
agreed topics and activities. A given student might choose to research the historic relationship
between technical change and economic growth, or how different futurists and forecasters view
trends in the next century, or the relevant policy prescriptions for change.
In general, the scenario-building exercise requires participants to quantify perceptions of past,
present, and future developments. First, we try to confirm the historical trend and to establish the
base scenario forecast, analyzing the prevailing situation. From this we propose alternatives,
review policies, and explore strategies that are intended to demonstrate preferred futures. In the
context of such a seminar, constructing a scenario typically involves identifying and assessing
the range of issues, variables, and outcomes. The overall task typically includes a number of
activities, each of which generally involves a sequence of steps.
For this model, the leading question is what items should be included in the interaction matrix?
Why is a given topic important: what is the issue? Different worldviews highlight or play down
the importance of particular variables. In the GIFS example, participants identified a number of
issues that they considered important. For the classroom exercise the selection was boiled down
to conflict, culture, education, poverty, technology, economy, population, and environment. Each
of these items and their mutual inter-relationships then were examined in more detail by the
class. Some discussion concerned what to do about missing but implicitly important items, and
how or whether to sub-divide the world? In the example, the world is considered as a whole,
Limits to Growth style. The tradeoffs between clarity and complexity, simplicity and over-
simplification, etc., as in real-world policy-making, are resolved by pressure of time. The goal,
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nonetheless, is to represent ideas about important variables in a consistent and poignant fashion,
in order to address the relationships between them in a systematic manner.
Given the selection of the cornerstone variables, the next step is to clarify their meaning, and
how each might be measured. For example, conflict might be defined as the number of deaths
from wars, institutional violence, crime, etc., but could also include psychological stress or a
propensity to violence, including the size of the military or arms races. Similarly, environment
may be conceptualized as an abundance of ecological diversity depleted by demographic and
economic advance, but with an intrinsic capacity for regeneration, or simply as a remaining level
of mineral resources. Throughout, participants are pushed to conceptualize sub-systems and their
interrelationships. For example, culture, education, and technology together provide a
―knowledge sub-system‖ with formal and informal, traditional and modern components. Other
effects might be treated as indirect, for example, the spin-off innovations from conflict and
conflict preparedness are a contribution of conflict to technology growth that in turn contributes
to economy growth. Similarly, culture and environment have historically been major sources of
innovation, or economic growth as a stimulus to education and technology.
The diagonal entries in the matrix are the internal changes within the sub-system (as defined
earlier). This includes all processes that are not to be made explicit. These entries are typically
omitted from cross-impact analyses although, as self-reinforcing feedbacks, they make a large
dynamic contribution. Discussion of such issues raises questions such as those in Table 1.
Table 1. Critical Questions: Self-Impacts (diagonal entries)
With additional variables, greater care is needed when identifying and deconstructing causal
sequences, for example, a simple demographic-economic model might take net population
growth to decline as the economy expands, but if an education sector is included there may be
Item Examples of Questions to be Confronted
Conflict: How important are arms races, domino effects, peace movements -
does this lead to positive or negative reinforcement of conflict?
Culture: Do cultures reinforce each other, does increasing diversity lead to
more diversity?
Education: To what extent does education involve a self-reinforcing cycle of
reproduction?
Poverty: Does poverty reinforce itself - to what extent is this a direct effect
rather than an indirect effect via demography, education, economy
etc.?
Technology: Does a high level of technology increase the rate of innovation and
diffusion?
Environment: Does the environment have a restorative Gaia-like regenerative
capability?
Economy: How important are the residual effects of investment, trade etc. on
growth?
Population: What is the contribution to population growth of births and deaths?
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several direct and indirect interactions. Education and economy are mutually reinforcing, but
might have opposite effects on population growth (via reduced birth and infant mortality rates
and increased life expectancy). Beyond this, it is evident that issues are mutually defined - for
example, making technology as an explicit variable means that its effect must be discounted
from the internal workings of the economy (i.e. make it as independent as possible). Although
there may be considerable overlap and ambiguity, the goal is discuss the distinctive contributions
that each variable makes to every other.
In order to enter into the interaction matrix participants must consider, for example, whether the
levels so defined, are increasing or decreasing, fast or slow? What evidence is impressionistic,
anecdotal, or assertive, and what is empirical? Is the situation with respect to each issue
worsening or improving? How do we decide whether a particular change is to be considered
favorable"? In order to make projections information must be translated into current rates of
change (i.e. percent per year), the strength of mutual interactions, and so on, which forces the
further question of how big is "big", or how to compare contributions in quantitative terms.
Table 2. Critical Questions for Causal Relationships: Cross-Impacts of Conflict
Here, the thinking draws on previous work in scaling life satisfaction surveys, and similar work.
The approach is to treat the values in the matrix as if they were responses to an opinion survey,
in which, for example, respondents are asked to assess their ―satisfaction‖ with various aspects
of their lives on a scale of 1 to 5. In the social sciences these are then taken to have a cardinal
meaning as well as an ordinal meaning and the responses become data for a variety of statistical
techniques, most commonly linear regression. A response ―very satisfied‖ is scored 5, compared
to ―somewhat satisfied‖ scoring 4, and so on: a dubious procedure at best. The same assumption
is made in Delphi and cross-impact analysis. Comparably dicey assumptions are made about
aggregation of dissimilar attributes – combining the proverbial apples with oranges. However
irrational this may seem, humans have survived this practice to date.
Variable Top Row Entries FIRST COLUMN ENTRIES Culture Does conflict polarize culture
or destroys marginal cultures?
Does diversity of culture lead to
conflict or help mediate it?
Education Does conflict change the level
of education or only its
content?
Does diversity of culture lead to
conflict or help mediate it?
Poverty Does conflict creates poverty? Does poverty promote conflict?
Technology Does conflict stimulate
technological change?
Does technology exacerbate conflict?
Environment Does conflict destroy
environment?
Does and abundance of environment
reduce the level of conflict?
Economy Does conflict destroy
productive capacity?
Does a rising economy reduce
conflict, and vice versa?
Population Does conflict deplete
population?
Do population pressures increase
conflict?
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The net growth rate of each variable is the sum of positive and negative contributions. For some
variables (such as economy and demography) there may be reliable quantitative estimates on
current and historic growth rates – after all, the diagonal elements are the foci of the mainstream
academic disciplines. In contrast, there is less research on the cross-interactions between these
fields. Nonetheless, setting up the remainder of the table requires that all other relationships are
hypothesized using the knowledge of cross-disciplines such as economic anthropology, policy
institutes, as well as inspired intuition based on anecdotal and partial information. Participants
are encouraged to seek justification for each of their suggestions in economic and social theory,
and in futures studies and the corresponding empirical studies. In the Age of the Internet, the
Millennium Project, United Nations agencies, and so many private organizations, there are
multiple sources of information.
The other entries represent the cross-impacts between the selected variables, for example, how
environment factors or levels of conflict affect population or economic growth? This
morphological analysis poses a good many tricky questions e.g. can we separate the investment,
trade, etc. effects from environment, technology, and human resource endowments. Table 2
shows typical questions to be addressed between conflict and the other variables. Even
seemingly banal questions such as the impact of conflict on population have extremely complex
answers. Other variables prompt similar debate. Indeed, the value of the interaction matrix, like
cross-impact analysis, is that it compels participants to address such questions.
Participants typically differ in their readiness to move from tentative verbal answers to such
questions to more quantitative empirical responses. Nonetheless, with due encouragement and
varying degrees of skepticism, it is possible to reach a modicum of agreement. One important
catalyst in negotiating this ―agreement‖ is to allow a fairly wide range of disagreement, as
measured by the extent of uncertainty in growth rates in any given year, for example. An
alternative is for dissenters to construct separate versions representing their own observations
and resulting projections. One way to assess the plausibility of these ―data‖ is to run the model in
reverse (backwards from 2000) and check whether it forecasts-backwards a reasonable view of
history. This procedure will be illustrated below.
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Figure 4. Projections: Present into the Future
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Given sufficient agreement about the impact table, or alternatives, the next step is to ―run‖ the
model. An attractive feature of the model is that charts and tables update immediately, so
feedback is continuous. With the parameters shown in the trend matrix in Figure 4, the model
projects a somewhat depressing future. Levels of culture and environment are forecast to decline
increasingly rapidly! This outcome is also shown in the main chart in Figure 4. Population,
poverty, and conflict increase, and then decline as culture and environment are depleted. Despite
this, the economy, technology, and education continue to rise steadily. Projecting further into the
future, population and conflict too disappear, yet economy and technology continue to grow
exponentially. Ironically, the environmental and demographic forecasts fit with the Limits to
Growth‘s most pessimistic scenarios yet the economy and technology trends are quite contrary.
The future summarized in Table 3 - a world with no people but massive technology and
economic activity resembles more the Millennium 3000 scenario, The End of Humanity and the
Rise of Phoenix, with a future dominated by robot civilizations, where technology has finally
totally substituted for nature.
Most people would not be happy with this particular vision of the future. Obviously, we cannot
take the projections too literally. In any case, what does it mean to have ―no more nature‖, or ―no
more culture‖? Some futurists expect that our descendents, robot or humanoid, will develop a
rich diversity of cultures, and bio-engineering and terra-forming will provide us with a new
nature.
Table 3. Prospects for the Future
It is actually quite difficult to find an attractive future vision compatible with such consensus
projections, especially with linear equations (but this leads to the question of whether modelers
simply adopt formulae that disguise such problems). In the GIFS exercise so long as students let
present trends continue, the future remained unacceptable. An implication, not one that futurists
will find surprising, is that some dramatic paradigm shift is demanded. The greatest challenge for
participants therefore is to find plausible policies that will deliver an acceptable future. To do
this the data in the model are adjusted using the ―policy matrix‖ shown in Figure 4 to change the
projections. To arrive at this table, some rules first had to be established, not least to use
resources wisely, if not optimally. Policies have to be realistic, strategies that obviously demand
great resources, must be traded-off against others, some strategies are likely to be politically and
functionally incompatible with others. For the GIFS exercise, the goal was to invent a strategy
Item Next 50 Years Next Century
Conflict Rising Declining
Culture All diversity lost No more culture
Education Increasing Increasing
Poverty Steadily increasing Slowly decreasing
Technology Increasingly rapid growth Much less than present
Environment Totally depleted No more nature
Economy Rapid growth Many-fold increase
Population Declining level Totally collapsed
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that reduced the level of poverty and loss of culture and environmental resources by reallocating
economic and educational resources. The policy and outcome are summarized in Table 4.
Table 4. Strategic Choices for a Preferred Future
Item Policy (Explicit or Implied) Outcome for Item
Conflict No direct policy Steady then declining
Culture Education for diversity Steady
Education Changed emphasis Increasing
Poverty Steadily increasing Steady, then decreasing
Technology Changed emphasis Reduced rate of increase
Environment Less damaging technology Decline then recovering
Economy Less growth-oriented Reduced rate of increase
Population Education of women Slower rate of increase
Figure 5. Policy Changes from the Past to the Future
With these changes, the rate of growth of the economy and technology declines but the loss of
culture and environment is reversed. The new less fatalistic trends are shown in Figure 5.
(To obtain this, the policy modifiers shown in the Policy Matrix in Figure 4 are added to the
entries in the Trend Matrix). A skeptic might argue that ―solution‖ and outcome are obvious, but
again what matters here are the processes discussed and the relative magnitudes of the required
changes. Again, since the model is not a strict ―accounting‖ framework the allocation of
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resources is at best notional, but gives a sense of the tradeoffs inherent in such prescriptions: in
effect, a ―bartering‖ process takes place between participants and policies. The policy changes
are neither marginal i.e. likely to come about through the normal adjustment processes of an
open society, nor do they appear so dramatic as to require a wholesale shift in human values and
lifestyles.
IV. CROSS-CHECKING WITH HISTORY
For some variables there are some reasonably current or historical data that may be use as a
starting point. At least with respect to the net trends of population and economy, time series are
available from the United Nations and other institutions, and used to assess trend and some of
the inter-linkages. For the GIFS, world population estimates are available for millennia and
probably are reasonably reliable for the last century. For the world economy, reliable data may
be available for less than half a century, but economic historians have made estimates for several
centuries. Most other data are even more contentious or less readily available: data such as
environment might be based on resource abundance or surviving species; conflict might be based
on number of wars, crime, terrorism etc.; technology might be based on number of patents;
culture on the number of language groups, art works, etc. Variables are indexed to the present, so
the initial input might simply be the perception that things are getting ―much worse‖ or ―slightly
better.‖ These assessments too, then have to be translated into quantitative terms such as ―so
many percent per annum.‖
In setting up the model it is useful to reach agreement on these net trends first, and then consider
how the various contributions factor in. Data on the various contributions are less readily
available, but the literature (economics, demography, anthropology, environmental sciences etc.)
and corresponding international agencies provide clues. Thus for at least some of the cross-
impacts, there may be statistical evaluations in the literature. In a Delphi survey, it surely must
be an assumption that experts are familiar with these works and are able to synthesize the
findings to conclude whether a given interaction makes a ―major‖ or ―marginal‖ contribution,
etc. Experiments show that simply to rank these categories does not provide sufficient precision
since feedback models can be especially sensitive to small parameter variations, verging on
critical behavior. Cardinal scales cannot be separated from the underlying model (for example, if
the model is linear, then the scaling may be non-linear, and vice versa, i.e. they are model-
dependent.) For similar reasons, the statistically estimated parameters from econometric or
demographic studies offer only a guide to the parameters of a more inclusive model. Indeed,
given ambiguities in definition, uncertainties in available data, limitations of estimation
techniques, and so on, the synthesis of information is by human induction and trial and error.
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Table 5. Assessment of Past Trends
Although the model as a whole is not estimated in a formal statistical manner, it is nonetheless
sensible to assess whether it is at least plausible. One avenue for this is to test whether the model
replicates ―known‖ history, by running it backwards from the present to the past. Given that
most of the information in the trend matrix in Figure 2 is based on the recent times,
―backcasting‖ provides a straightforward test. This is based on the perceptions of the past as
summarized in Table 5. The corresponding backcast from the present to 1900 is given in Figure
6. With ―fine tuning‖ of the inputs the model, using smaller increments to matrix entries, will run
backwards to before the last millennium. The backcast for the parameters shown has levels of
population, economy, technology, and education unambiguously increasing through time, and
the level of environmental resources falling, as perhaps most historians would accept. Past trends
in conflict, culture, and poverty are probably more contentious, simply because we think we
know more about them.
Backcasting also provides a way of deconstructing competing explanations; several worldviews
may provide plausible visions of the future but, like the original Limits to Growth models, they
can provide nonsensical pasts, that expose inconsistencies in the assumptions. When participants
introduced their agreed understandings of the ―conservative‖ and ―environmentalist‖ worldviews
into the GIFS model, the former offered a far better fit with their perceptions of the past. In
contrast, the environmentalist view, which better matched the popular sentiment of the group and
their policy prescriptions for the future, would not project back plausibly more than a few
decades. To help understand this, the model was used to show how influences on each variable
change through time. Up to the beginning of the 20th
century there was a systemic relationship
between variables so that the interactions between variables provided a set of positive and
negative feedbacks that constrained them to a more-or-less mutually balanced path. Approaching
the mid-20th century this scheme appears to break down as fewer variables, especially the
economy and technology, begin to dominate the behavior of all others, undermining the former
balance. This tendency is increasingly pronounced as we project further into the future.
Item Past 50 Years Last Century
Conflict Variable Similar to present
Culture Declining diversity More than present
Education Increasing Less than present
Poverty Increasing More than present
Technology Increasingly rapid growth Much less than present
Environment Increasingly depleted More than present
Economy Rapid growth Much less than present
Population Declining growth Much more than present
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Figure 6. Backcasting from the Present into the Past
Achieving a plausible backcast may involve a good deal of tweaking of the parameters and is
certainly more challenging than projecting a plausible future. Nonetheless, there has been much
misunderstanding about backcasting as a technical exercise. It has been argued, for example, that
it contravenes the laws of thermodynamics, or that time does not run backwards. In the real
world this may be true. In a time-step model the real issue is simply how well our assumed
algebraic relationships and data fit with our understanding of the past. The only formal
requirement is that the model can retrace the backcast in a forwards direction. Backcasting,
projection, and policy experiment, demonstrate the sensitivity of outcomes to relatively small
parameter changes, and the importance of recognizing and accounting for variability and
uncertainty in futures studies.
Throughout any futures exercise, the need to be aware of the extent and implications of
uncertainty is paramount. For futurists, a high degree of uncertainty translates into the need to
prepare for several futures, even though we may have an option on the most desirable. As a
practical matter, an understanding of uncertainty is arguably at least as important as the base
forecast. For planners such uncertainty translates into the need to devise robust strategies. The
sensitivity of the model to parameters and policies, also suggests how difficult it will always be
to arrive at sustainable policies. Figure 7 shows typical results when the group‘s assessment of
uncertainty in each variable is introduced. By running the model many times with random
fluctuations determined by candid estimates of uncertainty we can calculate high and low
projections or a distribution of projections for individual variables. The wide uncertainty for the
poverty variable shown in Figure 8 arises indirectly from fluctuations in other items (shown in
the bar chart), suggesting the need to revisit the selected strategy. Together, the assumptions
about past, present and future inputted into the model, (whether as the variables adopted,
strengths of their relations, degree of uncertainty, reactions trends, or proposed alternatives)
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reflect the collective paradigm or worldview of the participants. This will be discussed further
below.
Figure 7. Uncertainty in Future Trends and Probability Profiles
Figure 8. Probability Profiles for Poverty
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V. STRENGTHS AND WEAKNESSES
The strengths and weaknesses of the heuristic model might be assessed as
i) a meaningful set of equations,
ii) as a futures method, or
iii) as a device designed to stimulate thinking about i) and ii), which it primarily is.
As a set of equations offering reliable predictions, the model has obvious limitations. The core
model is a simple-minded set of linear equations mixing apples and oranges and riddled with
potential points of criticism – some valid, others ideological, others nit-picking. Some of the
objections, such as the use of linear rather than non-linear relationships, average rather than
marginal coefficients, the lack of strict accounting mechanisms, and so on can be fixed. In some
respects, even the version described here has advantages over some methods in that it integrates
lags and periodic disturbances and shows how multipliers build up through time. Methods such
as SMIC and cross-impact, in contrast, deal only with first-round effects. The heuristic model
even mimics complex system type-results in the sensitivity and variety of outcomes.
Beyond its present form, the model might be elaborated in several directions through the
equations used, for example introducing a menu of possible relationships, or a ―look-up table‖ as
with System Dynamics. Alternatively, the model might be estimated econometrically. More
interestingly, world modeling can become a place where mathematics, economics, physics, and
philosophy intersect, for example, the debate between the Sussex and MIT authors about
backcasting with the Limits models. I was involved in a similarly virulent debate as to why the
world economy, modeled as a fully-closed input-output model does not explode or collapse in
response to the smallest disturbance! Strangely, the answer seems to have been provided by
Archimedes (287-212 BC) when he famously remarked, "Give me a lever long enough and a
place to stand and I will move the earth."
As a discourse-provoking device, the approach has been modestly successful, at least as a way of
encouraging participants to appreciate the importance of taking a systemic approach to
forecasting, and to show just how unpredictable and contested the future is. With students, the
approach has served to advance their analytic skills. However, in that case the model was
programmed as an Excel spreadsheet, building on their existing skills to set up, and comprehend,
a ―mini-model‖ from scratch each semester. Prior to adopting this approach, I had used Barry
Hughes‘ International Futures (IFs) software, also in use by the Millennium Project.
Unfortunately, this far more detailed and sophisticated model required more participant know-
how and inputs than a 2-year planning program allows. Moreover, as a fully integrated package,
it did not allow the flexibility required for the class. Another more ―open‖ alternative, such as
STELLA, was expensive and did not deal well with the matrices needed to address the social and
economic structures and issues prescribed by the class. An early online version of the model,
described below, was also less successful in this setting.
As a futures method, the heuristic model is a component of a more synthetic approach that stands
alongside similar approaches such as like simulation modeling, cross-impact, and others. As with
these methods, while the modeling exercise involves quantification, the aim is not so much to
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produce precise forecasts as to help understand the issues and possible outcomes. The goal for
the method was to combine approaches from two kinds of futurists (practical forecasters who
focus on ―probable‖ futures tied to the extrapolation of present trends, and visionaries who seek
―desirable‖ futures that are likely to be achieved only beyond the trend) into a fairly simple
―open‖ system providing projections for an arbitrary selection of interacting variables. As with
many futures methods, the primary benefit of the heuristic method is that it obliges us to
organize information and to formalize connections between complex issues, and to deal with
many variables simultaneously. In the sense that the main goal of the heuristic model itself is to
provoke dialogue, this component might be assessed as the ―core‖ of the approach with the
model itself as primarily a facilitator or accounting device.
Again, it is useful to consider the method in relation to other models, formal and informal, in
teaching, and futures studies. With regard to the first, one might adapt the model solution; for
example, some of the earlier objections about the minimalist method of solution or choice of
variables may be addressed by using different types of modeling. Similarly, disturbances can be
introduced as a concatenation of random shocks, or non-linear-relationships can be
deconstructed into a concatenation of linear ones. In principle too, the number of variables may
be greatly expanded. The output of the model may be linked to a GIS (Geographic Information
System). There could be, for example, a menu of functions, closures, and solutions within the
model. However, at this point, it might be better to use an established model such as IFs, or a
functional toolbox such as Mathematica. Again, for classroom and possibly other uses,
simplicity and a short learning curve are of the essence.
Since the model was first developed, there have been remarkable changes in computing capacity,
software, and on-line access to data. The current program is rather primitive (essentially an old-
fangled Basic program translated to Visual Basic) and does not make the best use of features in
contemporary software Some years ago, the model was initiated via an interactive Web site and
an online questionnaire. The average responses to questions such as those shown earlier in
Tables 1 and 2, and the range of responses then became the inputs to the model with the resulting
projections displayed. Although, again this proved less effective for teaching purposes than face-
to-face discussion, it nonetheless has potential for situations where participants cannot come
together, or in conjunction with video conferencing, for example, and other efforts by public
agencies and more confident panelists to create information networks and dialogues within
communities.
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VI. POSSIBLE EVOLUTION OF THE METHOD
This last remark leads to possibilities for the future of such methods. Since its inception the
Millennium Project has appeared as an embryonic global negotiating machine distributed across
cyberspace feeding information to, and eliciting choices from, individuals, institutions, and
enterprises. There are many developments that will considerably enhance the possibility of
‗sketching out the future‘ and directly integrating simple models such as that described above
with futurists‘ insights and scenarios. The Internet provides the opportunity to implement this
approach. Written, spoken, and visual scenarios surely will soon feed directly with these models
that in turn will be able to ‗learn‘ our paradigms and preferences, and the ways that each of us
experiences and analyzes the world. At least from the perspective of this author, worldwide
Delphi surveys, not least those developed by the Millennium Project, cry out for a way of
structuring and integrating and synthesizing and making findings consistent, or simply revealing
and bringing together our varying perceptions and their implications.
One such approach based on textual analysis of journal articles was explored in a 2008 paper
titled ―The Zeitgeist of Futures?‖ The spirit of this analysis in relation to the heuristic model is to
capture topics, mood, and meaning of a particular worldview as expressed through discernable
relationships between variables and their changes over time through ―data-mining‖ of journal
articles. The prime assumption is that the attention paid to a topic such as ―globalization‖ in an
article (i.e. its word count) is an indicator - sensitive to the intellectual and cultural climate of the
era that ―something is happening‖ that might be a harbinger of the future. The count gives some
idea of what core variables might be inputted into the heuristic model to represent a particular
paradigm or Zeitgeist.
Table 6. Frequency of Topics
Topic Time Horizon Geographic Scope Survival Direction Disposition
technology 11
5
2000 85 world 68 human 43 develop 101 challenge 23
economy 98 century 48 global 59 health 18 sustain 63 problem 20
environment 60 post- 33 international 29 conflict 13 change 50 uncertainty 17
society 47 history 32 space 26 peace 8 growth 22 issue 14
culture 32 long-term 15 nation 21 wealth 3 limit 18 crisis 10
energy 23 millennium 8 local 8 poverty 3 globalization 16 dilemma 5
resource 16 21st century 3 cyberspace 4 universal 3 transform 14
agriculture 10 20th century 0 universe 3 progress 10
population 6 decline 6
Table 6 (based on articles in Futures from 1968 to 2007) showed that topics such as humanity
and development are frequent, but generic and all-encompassing. Related issues show distinctive
trends, for example, conflict took off in the mid-1970s but was overtaken by peace beginning in
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the mid-1980s. Some of the variation over the years is clear-cut and easy to explain; in other
cases quite enigmatic. An example of the former would be the steady trend in development as an
all-encompassing expression of desired direction, or the popularity of sustainability in the late
1980s (following the United Nations conferences). Likewise, with geographic scope and scale:
the world as a topic has been steady, but by 2000 lost out to global and globalization. Less easy
to explain is why century (as in end-of-century) should be popular around 1980, Millennium
around 1990, and 21st Century invisible until 2000? Unraveling the Zeitgeist also sheds light on
another perennial question for futurists to debate on whether our efforts have any substantive
impact on the future, or whether they are simply responding to events, or whether, by
commenting on events we provide prescriptions for the future, and so avoid the very
circumstance we foresee.
Another intriguing possibility for the conceptual structure of the model is to extend the equations
to a version of the discrete logistic model. The appeal of this particular revision is that the
discrete logistic equation is remarkably simple, quite in contrast to the rich variety of trajectories
it generates: equilibrium, overshoot, period-doubling, and chaos. The prime relevance of this
model is that it captures the relationship between growth potential and carrying capacity, futures
studies‘ perennial Neo-Malthusians versus Cornucopians debate, as well as generating
tumultuous trajectories reminiscent of our contemporary world. The model has been used to
explore variability and chaos across a wide range of disciplines (biology, engineering,
meteorology) and has been hypothesized as a core explanation for social and economic systems
exhibiting chaotic growth trajectories. Most studies have concluded that the growth potential in
social systems is empirically insufficient to promote chaos, but (in this author‘s opinion) this
view stems from a misinterpretation of the model. Moreover, the key to understanding this
model is the failure of the defective forecasting and decision-making procedure simulated within
the logistic equation. This, together with the new dynamic of the globalizing economy with
mobility of capital and demand, plus the agglomeration and levering effects around points of
accumulation and production, easily brings socio-economic systems into the realm of chaos, and
possibly offers explanation and pointers for the post-Millennium system.
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REFERENCES
Anderson, P., K. Arrow, and D. Pines. 1988. The Economy as an Evolving Complex System.
Addison_Wesley, New York.
Cole, S. C. Freeman, K. Pavitt, and M. Jahoda, 1973 Thinking about the Future: A Critique of
the Limits to Growth. Futures (Special Issue). Also Chatto and Windus, London, amd Models of
Doom, Universe. New York.
Dator, J. 2002. Advancing Futures: Futures Studies in Higher Education. Praeger Studies on the
21st Century. Praeger. London.
Forester, J. 1971, World Dynamics, Wright Allen Press, Mass; and Meadows, D. et al. 1972,
The Limits to Growth, Universe Books, New York.
Godet, M. 1974. SMIC: A New Cross Impact Method. Futures. Futures 8:4. 336–349.
Freeman and Jahoda. 1977. World Futures: The Great Debate; Martin Robertson, London, and
Universe Books, New York.
Gordon, T. and H. Hayward. 1968. Initial Experiments with the Cross-Impact Method of
Forecasting. Futures. 1:2. 100-116.
Helmer, O. and N. Rescher. 1959. On the Epistemology of the Inexact Sciences," Management
Sciences, 6,1.
Hughes, B. 1996. International Futures: Choices in the Creation of a New World Order.
Westview. Boulder.
Kane, J. 1972. A Primer for a New Cross-Impact Language—KSIM 1972. Technological
Forecasting and Social Change 4. 192.
Leontief, W. 1951. The Structure of the American Economy, Harvard University Press,
Cambridge. 2nd Edition.
Sackman, M. 1974. Delphi Assessment: Expert Opinion, Forecasting, and Group Opinion. Rand
Report. R1283 PR. Santa Monica.
UNU. 2003. Forum for Globally Integrated Environmental Assessment Modeling. United
Nations University. Tokyo.
Futures-related publications by this author that have contributed to the approach
1971. Model Dependent Scale Values for Attitude Questionnaire Items. Socio Economic
Planning Sciences. 5: 395 405.
1973. Backcasting with World Models. Nature. May. 243:5402, 63-65.
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1977. Global Models and the International Economic Order; Pergamon Press, New York.
1985. Worlds Apart: Technology and North-South Relations in the Global Economy.
Wheatsheaf. Rowman and Allenheld. Brighton.
1987. World Economy Forecasting and the International Agencies International Studies
Quarterly. Dec.
1990. Cultural Diversity and Sustainable Futures. Futures. 22.10:1044-1058
1995. Contending Voices: Futures, Culture, and Development. Futures. 27.5: 473-481.
2001. Dare to Dream – Bringing Futures Studies into Planning. Journal of the American
Planning Association. Fall 2001.
2004 Neo-Malthusians and Cornucopians: Beyond Chenoweth and Feitelson. Futures: August.
2004.
2008 The Zeitgeist of Futures? Symposium. Futures 40.10: 893–926
2009 A Logistic Tourism Model: Resort Cycles, Globalization, and Chaos. (forthcoming)
Annals of Tourism Research, June 2008.
Downloadable version of the Model http://www.acsu.buffalo.edu/~samcole/heuristic.htm