Heuristic Modeling - The Millennium Project€¦ · Heuristic Model Future Scenario I. BACKGROUND...

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.


Heuristic Modeling ii

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.


Heuristic Modeling 1

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



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



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



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)



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.



Figure 3. Information and Hints



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,



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?



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?



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.



Figure 4. Projections: Present into the Future



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



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



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.



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



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)



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



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



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.



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



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.



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.



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

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