|
Table of contents
PrefaceOne of UNESCO's major missions is to
promote closer linkages between scientific knowledge and
policy-making.
The work of Mihajlo D. Mesarovic, David L. McGinnis and Dalton A. West,
that we have the pleasure of publishing in the MOST Policy Paper Series,
is an important contribution to the effort the Director-General of UNESCO,
Federico Mayor, qualified as "bridging the gap between science and
decision-makers". The authors propose a new paradigm, applied in this case
to the area of global change, but which can be adapted for application in
other important fields, such as population, migrations, employment, etc.:
It aims at replacing the input/ output paradigm, generally used in
research and policy analysis, by a paradigm the authors refer to as
cybernetic and reflexive. In their words, what is proposed is an
"integrated assessment, as a process of reasoning about the global future,
based on decision support methodologies in which an ensemble of models are
used and the human factor is "put inside the models", to represent
goal-seeking (adaptive) behaviour and account for non-measurable
aspects".
A prototype of an integrated assessment support system, named
GLOBESIGHT (a contraction of Global Foresight) has been developed, and is
used in various circumstances, and in particular the following UNESCO
activities: a series of "UNESCO Workshops on Bridging the Gap Between
Science and Decision-Making", as well as the "Globally-Oriented University
Education Consortium", that UNESCO has recently launched, with the aim of
fostering teaching and research on global issues through a network of
universities in different parts of the world, including the Case Western
Reserve University (USA), Bilkent University (Turkey) and the Technical
University of Catalunya (Spain). Others, such as the Trier University
(Germany), Jawaharlal Nehru University (India) and Autonomous University
of Mexico (UNAM) are about to join the network.
Also, in December 1995, the MOST programme and the Institute of World
Systems, Economies and Strategic Research of Bilkent University, jointly
organized an international symposium in Ankara, on "Methodological Issues,
Quantification Techniques, Decision-making and Governance in Social
Sciences", where GLOBESIGHT was presented and major epistemological and
methodical issues involved in this field were discussed. A book is in
preparation.
This Consortium, directed by M. Mesarovic, is developed within the
context of UNESCO's Management of Social Transformations Programme (MOST),
University Twinning Programme (UNITWIN) and Co-ordination Unit for the
Environmental Programmes - the latter being the International Geological
Correlation Programme (IGCP), International Hydrological Programme (IHP),
International Oceanographic Commission (IOC) and Man and the Biosphere
Programme (MAB). Currently, there are also efforts to disseminate a
simpler version of this paradigm and the GLOBESIGHT model, at the
secondary education level, particularly through the UNESCO Associated
Schools network, which involves schools throughout the world.
Ali Kazancigil
Director, Division of Social
Sciences, Research and Policy
Executive Secretary,
MOST Programme
UNESCO, Paris, April 1996
1 Cybernetic Paradigm for the Human Dimension
(1)
Understanding
the role which humankind plays in global change is a prerequisite for the
development of realistic and credible policies for mitigating change. That
role is customarily described in terms of the "human dimension". The very
term "dimension" itself indicates the inadequacy assigned to the human
role. One would not characterize the role of natural phenomena in
analogous terms, e.g., by talking about the "ocean dimension" or
"atmosphere dimension" of global change. Atmosphere, oceans, land, and
other natural systems are clearly subsystems which constitute a global
system through interaction. Similarly humankind is also a subsystem which,
like any of the natural subsystems, is a constituent part of the global
system. This is not recognized in research under the so-called "human
dimension", which focuses on two sets of indicators:
- the impact of anthropogenic activities on the environment, e.g., the
increase in greenhouse gases, and resulting changes in the atmosphere
and climate, etc.; and
- the impact of environmental change on humans, e.g., changes in
agricultural productivity under assumed change in the atmosphere, etc.
What is missing, however, is how these two categories of indicators
are related or how these two sets of indicators are connected, i.e.,
how the human system functions in time as the global change occurs.
This requires:
- a proper representation of the process of interaction between
humankind as a system and the natural system; and
- explicit recognition of the specific and unique character of human
functioning as a system.
The first aspect - the relationship of humankind with nature - is best
understood in terms of the reflexivity concept amply advocated by George
Soros (2). Simply
put, humanity is changing the environment while simultaneously
being changed by it. It is a continuous feedback relationship. Humans are
not outside observers of global change but rather are on the inside of the
system being changed. This imposes a fundamental uncertainty (a limit to
complete, objective knowledge or predictability), a "bias" in Soros'
terminology. The human dimension view, illustrated in Fig. 1 has to be
replaced with the reflexivity view illustrated in Fig. 2. The human
impact and the impact on humans cannot be considered separately but as
clearly related (connected) in real time. Understanding this reflexive,
feedback configuration of the global change system is central to
understanding the human role in global change. Currently, human dimension
research focuses on the study of historical and present data - economic,
demographic, land use, social indicators, etc. - and also on
anthropological studies of the self-sustaining existence of tribes
(present or past) in isolated parts of the world. While this research is
certainly of interest and instructive, it is not adequate to address the
predicament facing global society in the 21st century. Fig. 3 compares the
view of the global change system by the Earth Systems Science Committee (3) and the view based
on the reflexivity concept.
Figure 1
Figure 2
Figure 3
The second aspect - proper representation of the specific character
of humankind and the role it plays in global change - needs a paradigm
different from the input/output or state transition paradigm used thus far
in the study of global change. In the state transition paradigm the system
is assumed to be fully describable in terms of the state of the system at
a given time and the system transformation (mapping, transfer functions)
of that state to another state as well as the input between two instances
in time. This paradigm originated in physical sciences. To convey the true
nature of such a paradigm we refer to it as the "Newtonian mechanics"
paradigm. It assumes that only lack of data and knowledge prevents us from
being able to fully predict the future; there is no room for uncertainty
or indeterminism. The state transition (input/output, stimuli/response)
view can be useful under limited circumstances in the representation of
humankind as a subsystem but erroneous if overextended. Using this
paradigm, models (economic, energy, integrated, etc.) are developed in
terms of differential (or difference) equations with or without
equilibrium processes. It has been observed that the problem with such
models is not that their predictions are wrong, but that they are right
most of the time except when the predictions are really needed. If
the time horizon is short and "business as usual" prevails the prediction
using input/output paradigms does not go far wide of the mark. It is when
the change is sufficiently large and the consequences are felt over a
sufficiently long period of time that the input/output paradigm breaks
down.
An alternative to state transition is the goal-seeking (or
decision-making) paradigm. It has its origin in biology and the study of
human behavior rather than in physical phenomena. More concisely, the
functioning of the system in the goal-seeking paradigm is represented by
two items:
- goal(s) of the system; and
- the processes which the system possesses to pursue these goals and
to respond to the influences from the environment.
The state transition and goal-seeking paradigms can be more explicitly
and precisely contrasted in terms of mathematical general systems theory
(4).
In the framework of state transition, input/output representation of a
system is considered as consisting of a pair of transformations (mapping,
transfer functions), namely, state transition, S1, and output
function, S2.
where Xtt1 is the set of inputs in the time interval between t and t1,
Yt1 is the set of outputs at t1, Zt and Zt1 are states at the times t and
t1, respectively, and ƒ (the Cartesian product) simply indicates that the
variables before the arrow (inputs) are causes for the change in the
variables after the arrow (outputs). The equations simply state that given
a state of the system z  Zt and an input x xtt1
Xtt1, the next state zt1 Zt1 is fully predictable
(Eq. (1)) resulting in a predictable output yt1 Yt1 (Eq. (2)). To
understand the system one has to have data on Z and X and the knowledge of
S1 and S2.
The goal-seeking paradigm requires more items. The following are needed
for representation of the system in the most general case:
- A range of alternative actions (decisions), M, available to the
system in response to what is happening or is expected to happen in the
system's environment.
- A range of uncertainties, U, which the system envisions as possibly
affecting the success of the selected decision. The uncertainties can be
due to two sources:
- uncertainty as to what might happen in the
environment, i.e., the external input from a range of anticipated inputs;
and
- uncertainty due to an incomplete or inaccurate view
(representation, image) of what the outcome of the decision will be even
if the external input is correctly anticipated. This represents the bias
on the part of the goal-seeker as to how the overall system functions. For
example, if the first kind of uncertainty is resolved in the sense that
the environmental input is exactly as expected, the outcome can still be
uncertain because of the lack of knowledge on the part of the decision
system as to how the environment is going to react to the decision.
- A range of consequences (outputs) following implementation of the
system's decision, Y.
- An evaluation set ("performance scale"), V, used by the system to
compare the results of alternative actions; i.e., given the outcomes of
the two decisions, which of the two is preferable.
- The decision system's view of the environment; i.e., what is the
system's understanding of the environment. In other words, what output
(consequence), y from Y, the system expects after a decision, m out of M,
is implemented and the environmental influence, u in U, is correctly
anticipated. In reality, it is seldom, if ever, a complete and accurate
reflection of the reality. In general terms, the corresponding mapping, P,
is given by
P : M  U -> Y
- An evaluation mapping, G, used to compare the outcomes of the
decisions using the preference scale, V, and taking into account the
"extent or cost of the effort", i.e., m in M. This is also a mapping
G : Y M -> V
for any given m in M resulting in y in Y. The mapping, G, assigns a
value, v in V. The role of G is to determine the system's preference for a
pair (mi, yi) over another pair (mj, yj).
- The tolerance function (relation) which indicates the degree of
satisfaction with the outcome if a given uncertainty, u in U, comes to
pass
T : U -> V
For example, if the conditions are of full certainty, the best (i.e.,
optimal decision) can be identified. If, however, there are several events
which are anticipated (i.e., U has more than one element) the performance
of the system, as evaluated by G, can be allowed to deteriorate for some u
in U, but it must stay within a tolerance limit which will ensure
"survival of the system".
- Using the above items the functioning of the system is defined by the
statement:
Find a decision (^m) in M so that the outcome is acceptable (e.g.,
within the tolerance limits) for any possible occurance of the uncertainty
u in U, i.e., find (^m) in M such that
G(P (^m, u), ^m) T(u,^m)
for all u in U.
This formulation is intimately related with what Herbert Simon
introduced as satisfactory (bounded rationality) human behavior (5) in contrast to the
"economic man" (i.e., optimizing) view which dominates economic theory.
An important role in this formulation is explicit recognition of
uncertainty and the concept of tolerance (acceptability, survival). The
performance can deteriorate for extreme occurrances in the environment but
it can still be acceptable or satisfactory (the outcome being within
tolerance limits) if "survival" of the system is assured regardless of
what occurs within the range of anticipated occurances.
Several remarks are helpful in clarifying the contrast between the two
paradigms:
- The input/output paradigm is far easier to model and should be
legitimately used whenever it does not result in a large distortion of
reality. However, if the behavior of the system is truly purposive, i.e.,
goal-seeking, this might not be possible. An illustration of this can be
found in the computer programmes for theorem proving, chess playing and
the likes. These programmes are not developed in terms of state
transitions but rather in terms of the so-called end-means, i.e., in terms
of goals (ends) and processes (means) to pursue these goals.
- The need for a new, human-based paradigm is recognized even in
well-established fields such as economics. Kenneth Arrow (6) has recently
observed "...the very notion of what constitutes an economic theory may
well change. Some economists have maintained that biological evolution is
a more appropriate paradigm for economics than equilibrium models
analogous to mechanics."
Formalization of the goal-seeking paradigm briefly outlined above
provides a basis for a deeper theory of the "human dimension" of global
change, as well as for other phenomena where recognition that humans are
not inert physical objects (machines) is essential (7).
- Input/output representation appears to be simpler in the sense that
it requires fewer items to be described. This, however, can be misleading.
If the system is truly goal-seeking the input/output representation
depends on the range of environmental influences (inputs). Under
different circumstances (different category of inputs) the input/output
representation becomes different. The system appears to "switch" from one
mode of behavior to an-other (e.g., in the so-called self-organizing
systems). If the environmental change is extensive, a large number of
alternative representations are needed with the system appearing to
switch, in time, from one mode of behavior to another. On the other hand,
if the goal-seeking representation is achievable, it remains invariant
over a large range of environmental inputs.
- Goal-seeking representation requires a deeper understanding of the
system and is often difficult, if not prohibitive. However, even if the
input/output description(s) has to be used, the results of the analysis
should be interpreted in reference to the true paradigm of the system.
2 Human as a Submodel
Accepting the need for
a reflexive and goal-seeking representation of humankind in global change,
the question is how this can be realized. One approach is to develop
computer algorithms which represent the processes which the goal-seeking
system uses to pursue its goal. This is within the domain of so-called
artificial intelligence. Another approach being considered at present
consists of putting the human inside the model. Rather than
simulating goal-seeking behavior by computer algorithms, the human (user)
is put in the position of being an integral part of the model (a
component, subsystem) representing goal-seeking (decisionmaking) behavior
(within the decisionmaking framework represented by Eq. 3-6). The human is
in a reflexive relationship with the computer models of the natural
systems. One way to look at this is to view the human as being in a "game"
type, interactive relationship with the computer algorithm parts of the
model. The human/ computer inter-linkage is "tight" in the sense that the
computer model cannot evolve in time unless the user "simulates"
the functioning of the humankind system. The architecture is that of a
blended simulation/gaming process. It is not pure simulation because the
computer components of the total model cannot proceed to the next step
without the human's actions and it is not pure gaming in the sense that
the human action is deeply imbedded in the structure of the overall system
(model) - it merely represents the subjective view of humans as to how
humankind responds to changes in the environment (8). A brief
description of such an interaction in reference to time evolution is given
in Box 1 and Figs.
4a and 4b.
Implementation of such human/computer modeling goes beyond the time
interactive process. The challenge of developing such symbiotic,
human/computer models consists fundamentally of carefully distinguishing
where human intuition, vision, views on uncertainty, etc., (subjective
aspects) are needed from where the logic, numbers, and facts (objective
aspects) are used for deeper computer analyses. As an illustration,
delineation of human computer roles in conflict resolution among multiple
objectives (a crucial issue in global change) is described in Box 2 and Fig. 5.
Symbiotic human/computer modeling provides a framework to take into
account non-numerical (non-measurable) aspects of reality. The omission of
non-measurable aspects can lead to a major distortion of the
representation (9).
Figure 5
3 Multilevel versus Integrated
Modeling
The need to represent phenomena from different scientific
disciplines in the modeling of global change leads to the concept of
integrated modeling in which all relevant disciplines are taken into
account. Early integrated models (more than twenty years ago) addressed
resource/population issues (10) while, more
recently, the emphasis has been on climate change (11). A
straightforward ("brute force") approach to integrated modeling consists
of developing models in the respective disciplines and then linking them
together without due regard as to how much is known about the linkages.
There are serious shortcomings to such an approach which can greatly
diminish the faithfulness of the constructed model. Views have been
expressed that an integrated model is as good as its component submodels.
The problem of the validity of such an integrated model goes much beyond
that. The key problem is in the linkage which integrates the submodels
into the overall integrated model. While the phenomena within disciplines
could be modelled with a degree of confidence, linking disciplinary models
is highly conjectural. The interdependence of the phenomena between
different disciplines can be viewed as one of the "ultimate" challenges to
science. Creating an integrated model poses the danger of
misrepresentation due to:
- Burying the lack of knowledge deep within the model structure
making it more difficult to understand what contributes to the overall
(integrated) model behavior;
- Conveying the impression of certainty where it does not exist;
and
- Resulting in fundamentally different behavior of the integrated
model from the behavior of the real system in spite of the faithfulness of
the submodels. Even the simple links between welldefined, fully
determinate models can lead to fundamentally different behavior. This can
easily be illustrated (12).
Consider two of the simplest possible systems described by the
equations:
where the indices 1 and 2 refer to the fact that the same variables are
recognized in two separate scientific domains. Recognizing that they refer
to the same real-life variables (although in different scientific
domains), the integrated model takes the form
While the two submodels are apparently quite well-behaved with fully
predictable future trajectories, the integrated model for certain values
of parameters (e.g., a = 2.1 and b = .04016) becomes chaotic, i.e.,
indeterminate and fully unpredictable. When the submodels are themselves
complex it is not possible with any degree of certainty to know whether
the resulting integrated model produces a fundamentally different behavior
from that observed in real life. Even a simple and weak linkage (as
given in the above example) can fully destroy the faithfulness of the
overall model in spite of submodels being consistent with reality.
The important question in integrated modeling is how plausible it is
that the representation will not be distorted by the linkages. This
question needs careful scrutiny even in modeling of physical systems, such
as in linking atmosphere and ocean models, not to mention models involving
humans.
Other shortcomings of integrated climate change-focused models is that
they do not provide the possibility of accounting for the human
goal-seeking behavior. A set of numbers and fixed mapping functions are
used throughout the model to represent the results of complex and
uncertain individual and societal processes. A simple example is the use
of elasticities in economic modeling to represent the outcome of
exceedingly complex decision processes. A small set of numbers - values of
elasticities - stand for the reaction of individuals and societies to
change (e.g., energy consumption relative to prices). Although the
elasticity relationships are empirically established from the past data,
their validity over future time horizons depends on human decisions
(individual and societal) yet to be made. Justification for relying on
elasticities to encapsulate human behavior depends on the time horizon,
magnitude, rate and character of change.
An alternative to integrated modeling by the "hard wired" linking of
computer programs is the multilevel integrated modeling approach
which consists of four steps:
- Development of a multilevel, conceptual framework which will indicate
the relative position (role) of the disciplines and indicate the linkages
needed.
- Construction of the models within the disciplines represented.
- Linkage of the disciplinary models using either coded links where the
available knowledge is justified or via the user where the links are
conjectural or have to be carefully monitored.
- Development of a goal-seeking framework to incorporate the human
inside the model.
A multilevel framework currently being used to research cybernetics of
global change is shown in Fig. 6. The highest
level represents the individual's perspective (needs, values, etc.) The
next, so-called societal (or group), level represents formal and informal
organizations in reference to the problem domain for which the model is
built. The central level encompasses economics and demography - an
"accounting" view. Underneath this level is the representation in physical
terms, i.e., in terms of mass transfer and energy flows (metabolism). At
the very bottom, there is the level of natural, ecological/environmental
processes.
Several remarks should be made in reference to the multilevel
framework:
- The architecture shown in Fig. 6 is only one
of several possible alternatives. Important to the approach is not whether
the structure shown in Fig. 6 is the right
one, but rather that a multilevel structure should be constructed as the
first step in integrated modeling of complex systems.
Figure 6
- The multilevel architecture provides the basis for including the
human inside the model. First, the linkages between and within levels
which are uncertain are controlled by the human who can experiment with
alternatives to establish the most plausible relationships under the
circumstances. Second, the human represents (simulates) the
appropriate functions on the levels where the goal-seeking paradigm is
called for. In particular, functioning on the higher levels is not
amenable to state transition modeling and the human takes on the role of a
submodel.
- Using the multilevel approach helps avoid the misdirected efforts to
model various phenomena which do not fit the state transition paradigm.
The best examples, perhaps, were the attempts to model political processes
which lead to the most implausible conclusions (13). Actually, only
phenomena which are modelable by state transition should be modeled as
such. All uncertain phenomena or processes which cannot be modeled
numerically should not be included in the state transition type of models.
The multilevel approach
helps in the management of complexity. Integrated modeling leads to ever
more complex models for two reasons: first, by linking already large
disciplinary models; and, secondly, in order to resolve uncertainty an
increasing number of details are introduced in the models. However,
uncertainty and complexity are two different obstacles to
understanding which should not to be confused; instead they should be
addressed in different ways. Making representation of a real system more
complex does not diminish the underlying uncertainty; rather it merely
obscures the source of the lack of understanding. As Herbert Simon (14) pointed out:
"Forty years of experience in modeling systems on computers, which
every year have grown larger and faster, have taught us that brute force
does not carry us along a royal road to understanding such systems...
modeling, then, calls for some basic principles to manage this
complexity."
Actually, in a number of instances a simple projection of trends is not
much different from the results obtained by large input/output models. The
size of the model does not improve its being true to the reality.
Increasing the size of the model could be counter-productive by reducing
the transparency of representation (i.e., obscuring what is really
happening). This is particularly true when analysis is to result in
real-life policies. As Manne and Richels (11) observed:
"Results should not be trusted unless they are intuitively
understandable."
Complexity is a concept (or term) which does not have a meaning in
itself but acquires its meaning only in a broader context. There is a
dynamic, burgeoning, exciting new field of "complexitology" which attempts
to come to grips with a general theory (15). The research
has been criticized as accommodating too many distinct, even
contradictory, views. This is a bit unfair because complexity is a derived
rather than a primary concept. It can legitimately be defined in different
ways within different contexts.
Global change is most certainly a complex phenomenon. Understanding
global change requires the notion of a complex system. In this regard, the
notion of a complex system in the mathematical theory of general systems
is relevant (4).
The starting point is the notion of a system as a relation among items
or objects. A complex system is then defined as a relation among the
systems. Items which form a complex system through interaction (i.e.,
subsystems) have their own recognizable boundary and existence while their
behavior (functioning) is conditioned by their being integrated in the
overall system. The human body is an obvious example; its parts (i.e.,
organs) are recognizable as such but their functioning (and even
existence) is conditioned as being part of the total system, i.e., body.
In our view, it is futile to argue whether this concept is a valid
representation of the complexity. What is important is whether the concept
can help us in addressing the challenges such as global change. We argue
that the concept of a complex system can be useful in that respect in two
ways:
- in presenting a more truthful and credible representation of the
global change phenomenon; and
- in providing a framework (as illustrated by multilevel modeling) for
representation of the decisionmaking processes in the global change.
Several additional remarks on complexity as reflected in the above
notion of complex systems can help clarify the concept:
- Complexity should not be confused with unpredictability or
indeterminacy ("surprising behavior"). A simple system in the sense of
being faithfully described by a small set of equations can be chaotic
(i.e., indeterminate) or self-organizing (i.e., have several modes of
behavior) exhibiting surprising (unexpected) behavior without being
complex.
- The concept of a complex system has an intimate relationship with the
concept of hierarchy (another concept which can have alternative
legitimate interpretations!). The behavior of a complex system, by
definition, can be considered on at least two levels:
- the level of
subsystems; and
- the level of the overall system. Conversely, a
hierarchical system which has two or more levels can be legitimately
considered as complex.
- The distinction between complex and "complicated" systems is
suggestive in this context. Paul Hindemith's music has been described as
"complex without becoming complicated, that its harmony is intricate
and not involved is about as close as you can come in so brief a space to
the mystical style of Paul Hindemith" (16). A single level,
large, integrated model is "complicated". For example, some computer-based
policy models takes hours, if not days, for a single run (17). Such models are
not practical for policy analysis where uncertainty prevails and
transparency is a prerequisite.
In its crudest form a complex system is viewed as having a large number
of variables (items) and being characterized by the phrase, "everything
depends on everything else." However, complex systems do function in
nature in an orderly fashion and have so functioned throughout human
history. The Roman Empire provides an example of a system that was truly
complex in view of the available means for communication and management.
Yet the system functioned successfully for centuries. The statement
"everything depends on everything else" indicates the breakdown state of
the complex system which otherwise functions by its own internal
management rules. Under normal conditions a complex system possesses
internal rules of management or behavior which allocate the
responsibilities to subsystems commensurate to information processing and
decisionmaking capacities.
Multilevel modeling also provides a basis for time effective management
and credible policy development in complex situations. In addition to the
conceptual hierarchy illustrated in Figure 6, a
hierarchy for the policy analysis is used for this purpose. Such a
hierarchy for the problem of global coordination of national greenhouse
gases mitigation policies (as used at the UNESCO workshops described
subsequently) is shown in Figure 7. On the
policy level, national emission targets are determined for an assumed
coordination mechanism (trade in carbon rights, mitigation fund, etc.)
using aggregated indicators (e.g., per unit cost of emission reduction as
a function of time and volume). The emission targets are then used on a
more detailed level (referred to as the system level) to identify
feasible conditions to meet trade-offs on the policy level. For example, a
degree of reduction of energy intensity (conservation, change in energy
mix from fossil fuels to other sources, etc.) On the disciplinary models
level the feasibility of these changes are evaluated. Models on higher
levels are parameterized by the information from the more detailed, lower
level models.
Figure 7
The analysis using the hierarchy of models can also be conducted from
the bottom-up. Changes are assumed on the lower levels and the impact on
trade-offs is evaluated on the policy level.
- The multilevel approach to complexity should be contrasted with
single discipline models. In the latter, phenomena from other disciplines
are considered as externalities by translating the concepts (variables)
from other disciplines in terms of the concepts of the main discipline.
Systems dynamics which restrict attention to time changes is another
example of "flattening" real-life hierarchy.
- The scale at which the policymakers function is different from the
level of policy analysis using integrated models. Von Storch (8) suggests the scale
difference between global climate models and the need for analyses related
to human scale activity is a primary problem in global change studies. The
development using the hierarchical architecture of the ensemble of models
helps in facing this dilemma.
5 Integrated Assessment as a
Process
Integrated modeling for climate change was heralded as
meeting the need to consider several scientific disciplines at the same
time. The initial euphoria about integrated modeling had to be tampered
with because the futures outlined by different integrated models (most
developed with high professional standards) turned out to be vastly
different. The concept of integrated assessment is then introduced in
recognition of the less than reliable forecast capabilities of such
models. Although, in general, integrated assessment is not identified with
integrated modeling, in practice, integrated assessment very often turns
out to be the development of an integrated model followed by sensitivity
analysis.
The reasons for the shortcomings of such an approach become apparent by
taking the cybernetic view outlined above, in particular:
- Heroic assumptions have to be made for the values of the parameters
in input/output (state transition) type models which result from the human
(individual and social) choices. For example, the rate of change of the
autonomous energy efficiency improvements (AEEI) for all regions in the
world are sometimes assumed to converge and become identical from the year
2050 and beyond. Preferences, choices, and means available to societies in
North America, China, Africa and Latin America are vastly different, and
they can hardly be expected to converge in such a relatively short
interval or even in the longer foreseeable future. Attempts can be made to
remedy this by extensive sensitivity analysis, but the range for such
analysis again cannot be meaning-fully identified without paying attention
to the underlying social processes, individual preferences, values, and
the likes.
- Because of the certainty (determinism) introduced by integrated
models, the results of analyses using these models are being questioned.
For example, the National Academy expresses a preference for the
"bottom-up" engineering approach as contrasted with the "top-down"
integrated modeling approach (18).
- Decisionmakers face a multiplicity of conflicting objectives -
mitigation of climate change being only one of them. Analysis of the
conflict between developmental and climate change mitigation objectives by
Goldemberg (19)
points out the potential unsustainability intrinsic in global climate
change mitigation. Sustainable development has been widely accepted as a
paradigm for a desirable future. In the assessment of conditions for
sustainable development, the emphasis has been put overwhelmingly on
resources and the supporting environment. Although the social and human
domain of the world problematique has been recognized, it has not been
accounted for in the analysis. Yet without sustainable societies there
cannot be sustainable development. After all, sine qua non of development
is the satisfaction of basic needs: physical, societal and psychological.
A sustainable society requires satisfaction of basic human needs, which in
turn requires energy available at the location, which contributes to
greenhouse gases concentration and impacts sustainability of development
(Fig. 8).
Figure 8
Satisfying the basic human needs objective and the objective of
sustainable resources use and environment are in conflict. Focusing on one
of these two objectives while ignoring (openly or implicitly) the other
objective is a non-starter. There is hardly a need for complex analysis to
appreciate the reality of the conflict between these two objectives.
Consider the case of China. China consumes at least 25% more coal than the
United States while using ten times more energy per unit of GNP. Even at a
moderate economic growth rate (e.g., assumed by the IPCC) the use of coal
in China will triple by the middle of the next century. If a higher (at
present, more probable) growth rate for China is assumed the coal
consumption will be even higher. It is to be expected that China will make
efforts, on her own and with the help of the international community, to
reduce environmental degradation, but it is hard to envision how the use
of fossil fuels in China can be reduced to the extent needed to keep the
emission of greenhouse gases near the required 1990 level as indicated for
the amelioration of the global warming prospects.
Identifying realistic policy alternatives cannot be solved by computer
models alone. While computer simulations may provide useful information
and guidance, decisionmakers are faced with a more complex process in
creating effective policies. Simulation models in a prediction mode must
be taken for what they are, one version of the future based on a set of
algorithms within computer software. To represent realistically the role
of the human, it is necessary to change decisions and algorithms during
the very process of the scenario evolution in simulation time to reflect
changing conditions and allow stakeholders to seek specific goals, not all
of them representable in numerical form. An alternative to using
integrated modeling is to use partial, more credible models (or even
trends) and support reasoning about the future by explicit argumentation
about the logic which leads to the statements about the future.
A good example is the limits to growth dilemma. Arguments in favor of
limits to growth are much more convincing by the analysis in separate
scientific domains and interpreting the results, rather than by arguing
that the limits are proven by a "Newtonian mechanics" construction
(model). The prospects for doubling Africa's population in one or two
generations and the analysis of the carrying capacity of the African
continent conducted by IIASA and the FAO (20) (which concluded
that the carrying capacity in a number of African countries will be
exceeded early in the 21st century) clearly indicate that the limits to
growth requires serious considerations. This is why arguments in favor of
limits based on partial but credible evidence, e.g., Daly (21), carry more
weight than the arguments based on simple (or complex) models with
questionable relationships. Similarly, the analysis by Cline (22) of the climate
change mitigation appears more convincing than model-based analysis, such
as, e.g., based on optimal behavior of humans and societies (23). Even if the
assumption about the optimal path development (economic, energy,
technology, etc.) in the highly industrialized countries such as the U.S.
and Germany is accepted (a big if), it is hard to discern optimal
behavior, not only in countries such as Zaire and Somalia, but even in
Brazil and India.
From the cybernetic viewpoint, integrated assessment is a human-based
process of reasoning about the future in which all available tools and
information are used in contrast to the computer-based approach,
such as in integrated modeling plus sensitivity analysis. The process is
akin to the decision support approach used in management science and
practice.
6 GLOBESIGHT - A Case Study
To research
integrated assessment as a process, a prototype of an integrated
assessment support system (named GLOBESIGHT - from GLOBal forESIGHT) has
been developed and used in several alternative circumstances. It belongs
to the class of active decision support systems investigated by Yasuhiko
Takahara (24). In
the process of reasoning about the future GLOBESIGHT plays the role of a
"consultant." Historical data (time series), other kinds of information
(i.e., textual), and a family of models (both integrated and partial) are
used in the reasoning process. The architecture of GLOBESIGHT is shown in
Fig. 11.
The Information Base contains numerical time series, textual
information, etc. which the user can consult when formulating policies and
assumptions.
The Models (Algorithms) Base contains a plethora of
procedures to explore feasible future evolution and consequences of
policies.
The Tools Base contains interactive procedures which allow the
user to actively participate in the process.
The Issues Base is a depository of the analyses (results, as
well as assumptions) already conducted for future reference, comparative
evaluation and extension of analyses.
Using a time interactive, "reflexive", feedback configuration of the
human and the computer (as illustrated in Fig. 9), the human
and the computer "walk hand in hand", step by step, along alternative,
feasible, future paths. The time horizon is broken into shorter time
intervals and at the end of each time interval the human reconsiders
assumptions (regarding policies, as well as scientific uncertainties) and
makes the necessary changes for the next time interval. The scenario
which emerges in such a process is not known beforehand (i.e., at the
beginning of the model run). It is the result of a symbiotic relationship
between the human and the computer in which objective (numerical) and
subjective (human visions) sides of the future evolution are blended.
Figure 9
The integrated assessment process approach is being used in two ongoing
efforts:
1. A series of UNESCO workshops on Bridging
the Gap Between Science and Decisionmaking.
Two workshops have been held so far: the first was held in May of 1993
in Venice, Italy; the second in September of 1994 in Santiago, Chile. The
second workshop focused on the Western Hemisphere and was co-sponsored by
the Inter-American Institute for Global Climate Change Research (IAI).
Twenty-six nations and international organizations participated in the
workshops (25).
Each nation's/organization's team consisted of at least one scientist and
one decisionmaker. The advantages of using the integrated assessment
process in international negotiations and national/international consensus
building was one of the focal points of the proceedings. Feasibility of a
joint and integrated effort of science/policy communities for better
utilization of science by decisionmakers and the ability to identify
priorities for future scientific research more responsive to
decisionmakers needs were also considered.
The workshops involve scientists and decisionmakers (or their staff) in
a joint effort of policy development in which both sides are active
participants. Using GLOBESIGHT and proceeding step-wise in time intervals,
assumptions on policies and uncertainties (scientific and other) were
formulated in a dialogue involving both sides. The workshops started with
background scenarios and some policy scenarios based on publicly available
information (26).
Participants were then involved in developing their own scenarios by
modifying assumed policies in a time interactive process to conform to
their own views. At the Venice workshop, the background scenarios were
changed by the participants, particularly in reference to the nuclear
option for the European Community and the energy conservation and change
in energy mix in China. At the Santiago workshop questions were raised of
specific interest to Latin America and corresponding scenarios were
developed in a "game-like" and step-wise iterative process with active
involvement by all participants. Questions raised by the participants at
the workshop and analyzed in the participatory effort included the
following:
- How important is Latin American participation in the global effort to
reach the global emissions reduction goal by the reduction of fossil fuel
use, and what would be the impact of such policies on regional development
prospects?
- Deforestation is clearly of major concern for bio-diversity. However,
how important is the reduction of deforestation to the climate problem?
The results of the scenario exercise are illustrated in Figs. 10-14.
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
In the Business as Usual scenario (no action!), the Latin American
percentage of global emission contribution declines at the year 2030 from
11% to 10% (Figs. 10 and 11) of the world
emission. With full participation of Latin America in the global
cooperation case the Latin American contribution declines at 2030 to 8%
rather than 10% (Fig. 12). The
reduction in energy use in the case of full compliance of Latin America
with the Rio treaty, measured in energy intensity (energy consumption per
unit of GNP), is shown in Fig. 13. Energy
consumption per GNP has to be reduced to one-half compared with the BaU
case. The impact of energy shortage and investment needed for industrial
development in such a case were considered to be far in excess of
contribution to the global goal which amounts to barely 2% (8% rather than
10%) of the world impact. In view of the relative marginal impact of the
Latin American effort and with recognition of the uncertainties involved,
opinions were expressed that Latin American abstention in the global
effort can be rationalized and that other regions which will be much
higher polluters by the year 2030 have to carry the burden.
A similar conclusion was reached in reference to the importance of
deforestation for climate change. A scenario was developed at the workshop
in which deforestation in Latin America was reduced gradually to zero by
the year 2030. A rather extreme assumption! The Latin American
contribution (all other assumptions being the same) reduces from 8% to 6%
(Fig. 14). Still
a marginal impact on the global effort.
The impact of Canadian participation in emission reduction versus an
adaptation strategy were also considered. Because of its geographic
position and an expected milder climate in Canada resulting in the
extension of the growing season for agricultural products, it was argued
that the adaptation strategy, rather than mitigation (emission reduction),
was preferable for Canada.
Both of these conclusions lead to the global commons dilemma: if all
regions follow their preferable course of action the global goal will not
be achieved by default. The conclusion from the proceedings, then, was
that if the global goal is to be achieved, the policies cannot be based on
an independent, separate assessment of local conditions but have to be
negotiated using scientific facts (for the sake of credibility) and with
all sides involved on an equal footing (for the sake of acceptability of
conclusions). Hence, the need for a participatory, consensus-building,
negotiation support process which blends scientific knowledge with
developmental objectives.
Education for the 21st Century.
The need for a new pedagogy to prepare youth for the challenges of the
global society in the 21st century is widely recognized and debated. In
addition to strengthening education on traditional subjects, the need to
provide the students with critical thinking and problem-solving skills by
what is called "active learning" has been emphasized. Global change
provides an appropriate topic for such a new pedagogy. The approach has
been used in university-wide undergraduate courses at Case Western Reserve
University for more than five years and will be offered in an
appropriately modified form to two high schools in Ohio. A preliminary
evaluation by teachers and students in Ontario, Canada, has been very
encouraging.
UNESCO has launched a Globally-Oriented Universities Education
Consortium with the goal of establishing a network of universities in
selected parts of the world whose students will be engaged in the
examination of conditions for sustainable development and social harmony.
The participants will be connected via the Internet and will share data
and textual information bases, models and assessment algorithms, etc.
The pedagogical principles for the effort include:
- A holistic view of global change based on a multi-disciplinary
foundation;
- The blending of physical and social sciences with the humanities
(scientific facts and knowledge with humanistic goals and visions).
- The use of advances in informatics to enable the students to manage
the complexity of the world problematique and address a plethora of
uncertainties intrinsic in the integrated assessment of alternative global
futures.
- Provide a channel for students from different cultural backgrounds
and who face different sets of issues to communicate, sharing their
experiences and promote understanding.
The initial members of the Consortium are Case Western Reserve
University (Cleveland, Ohio, USA), Bilkent University (Ankara, Turkey) and
the Technical University of Catalunya (Spain). Up to twelve other
universities from different world regions are being invited to participate
in the first phase of the program. The project, is developed within the
context of UNESCO's Management of Social Transformation programme (MOST),
UNITWIN-UNESCO Chairs Programme, as well as Coordination Unit for the
Environmental Programmes.
7 Domain of GLOBESIGHT
Applications
GLOBESIGHT is just a prototype of a tool which will
enable development of rational, fact-based, integrated assessment as a
process. The potential applications of such an approach include:
1. Multi-agency cooperative development of
policies.
The policymaking and the science of climate change operate separately
("at a distance") guided and constrained by their own distinct values,
criteria, methodologies and processes. Science generates knowledge and
facts, policymaking is motivated by values and objectives. Typically, the
interaction between the two communities takes place in one of two
ways:
- science discovers some facts which have policy implications and calls
possible implication of the new knowledge to the policy-makers' (and the
public-at-large) attention;
- policymakers face a dilemma which can be addressed more
satisfactorily if scientific research can provide answers to certain
questions. Scientific research is then conducted providing the answers to
the extent that data and knowledge are available at the time.
The prevailing way of science/decisionmaking interaction is that the
two activities proceed in parallel with "contacts" at appropriate points
in time; e.g., when the policy related questions are raised and when the
scientific research answering these questions is completed. Scientists
produce reports which are then explained and summarized for use in
decisionmaking. The use of a GLOBESIGHT-type support system has two
advantages:
- it would involve decisionmaking bodies as participating partners in
actual policy development; and
- it would facilitate identification of the scientific research
priorities more directly responsive to policy needs.
A facility can be developed based on the GLOBESIGHT concept for
participation of different agencies in the process of policy development,
from the diplomatic and policy perspective to science, technology and data
perspectives.
2. Support in international negotiations and
consensus building.
International negotiations are conducted in the political arena with
diplomatic concerns often playing a dominant role. The Earth Summit held
in 1992 in Rio de Janeiro and negotiations which preceded it are no
exceptions. While information provided by science is being considered in
such fora in a general way, the negotiation process can be expedited and
focused more sharply on the specific points of contention if the reasons
for different positions of negotiations are better understood. An
integrated assessment negotiation support system can contribute
significantly to such a process. An illustration of the use of the system
for support in decisionmaking and negotiations is provided by the UNESCO
series of workshops mentioned earlier.
3. Presentation to the public
(non-expert) audience of the policy impact and potential benefit from
the results of scientific research.
The support system can be used on-line to respond to the questions from
the audience as to the reasons for the selection of the proposed policy,
success of the policy under changed circumstances, consequences of
alternative policies, and the likes.
4. Networking of researchers
for sharing information, avoiding duplications, etc. Climate change
research is being conducted by a vast number of researchers around the
world. Sharing of information and communication about the results,
objectives, plans, schedules, etc., of research could greatly enhance the
cost effectiveness of the future global effort and expedite the progress.
The Information and Models Bases of a GLOBESIGHT-type system implemented
on Internet for use world-wide would enhance collaboration between
scientists.
5. Education and training
in a formal setting such as in universities, high schools, and
briefings for decisionmakers and their staff, etc.
Box 1
In order to blend subjective
(humanistic, non-numerical) aspects of the future and to avoid projection
of the past into the future in a "mechanistic" fashion governed
exclusively by a model, symbiotic interactive processes of scenario
formulation and assessment are used in the workshops. In traditional
scenario analysis the assumptions and policy options are selected at the
beginning of the model run and the future is determined from the initial
time until the end of the entire policy time horizon solely by the fixed
structure of the computer model and parameters estimated from the past
data trends (Fig.
4a). In the interactive process used at the workshops (Fig. 4b) the future
course is outlined in time increments; the human is but a submodel on par
with the computer algorithms. The process starts with the implementation
of present policies and assumptions about uncertainties over a relatively
short time increment (although the long-term view is taken into account as
needed in making the incremental assumptions). The computer programme
portion of the model generates feasible consequences of the policies and
assumptions at the end of the first increment. The human then makes new
policy choices and assumptions for the second time increment on the basis
of the newly arrived at state of the system at the end of the first time
increment. In response, the computer generates the state of the system at
the end of the second time increment providing a basis for policy
consideration by the human for the next time increment. The process
proceeds iteratively until the end of the entire policy time horizon.
Computer algorithms (models) do not predict the future in such a process
but rather have the role of consistency checks to make the vision and
goals of the human consistent with the facts (reality).
In policy development for complex
societal problems, it is essential to recognize the existence of
competing, conflicting, objectives which have to be taken into account
simultaneously. For example, economic growth, unemployment, trade, etc.,
have to be harmonized with the environmental concerns (e.g., increase in
concentration of greenhouse gases). A solution to this dilemma is achieved
by a careful delineation of responsibility between the computer and the
human - the former contributing efficient logical procedures, the latter
providing values preferences and subjective judgments. The process is
shown in Figure
5. The process starts with the human specifying a range of alternative
policy options to which the support system (computer) responds by
outlining the corresponding evolution of the system for each of the policy
options. An initial set of alternative scenarios is generated. In the next
step, the user specifies the (conflicting) objectives which provide a
basis for the support system to eliminate all "inferior" scenarios. A
scenario, A, is inferior if there exists another scenario, B, in the set,
such that B is preferable to A with respect to all objectives. The
surviving scenarios represent the set of non-inferior (mutually
conflicting) scenarios. A set of non-inferior scenarios is much smaller
than the initial scenario set. This reduced set contains only conflicting
scenarios in the sense that there is no scenario which has the best
performance in all objectives. The user then specifies the tolerable
(acceptable) level of performance for all objectives which allows the
support system to reduce the set of scenarios to a still smaller set of
"satisfactory" scenarios all having acceptable levels of performance. To
make the final choice an ensemble of "conflict resolution principles" are
available. They differ in the way the user's views are explicitly taken
into account. On one end of the spectrum is a procedure in which the user
indicates preferences among conflicting objectives by assigning the
relative Weight (importance) to different objectives. The computer then
takes over and identifies the implied choice of policy. On the other end
of the spectrum, where human judgment is given a major role an interactive
conflict resolution process is used. In the process the user compares the
scenarios pair-wise and indicates the preference between them. The process
starts by presenting the user with two conflicting scenarios. The user is
then requested to select the preferable scenario. The choice is made not
only on the basis of the extent of conflict in the objectives, but also
keeping in mind a whole range of other factors. The survived scenario is
then paired with another scenario from the acceptable set. The human again
selects the preferable one. While the process of pair-wise comparisons
proceeds, the computer deduces the preference scale from among the
satisfactory but mutually conflicting scenarios of the human on the basis
of the choices which the user is making. The support system then indicates
the final choice on the basis of the user's deduced preferences. To avoid
being "boxed-in" by the computer procedures, a large number of different
conflict resolution principles are often used. The final choice is made by
the human in reference to which of the conflict resolution principles
intuitively yields the most appealing outcome.
Notes1. The paradigm is
referred to in the sense of: Norbert, Wiener. Cybernetics or Control
and Communication in Machines and Animals, Cambridge, Mass., MIT Press,
1948.
2. George Soros, Alchemy of Finance, Simon
& Schuster, New York, 1987.
3. Earth Systems Science: A Program for Global
Change, Washington, DC, 1989.
4. Mihajlo Mesarovic and Yasuhiko Takahara,
Mathematical Theory of General Systems, Academic Press, 1974; Mihajlo
Mesarovic and Yasuhiko Takahara, Abstract Systems Theory, Lecture
Notes in Control and Information Sciences, eds. M. Thoma and A. Wyner,
Springer-Verlag, Berlin, Heidelberg, 1989.
5. Herbert Simon, Models of Man, J. Wiley,
1976.
6. Kenneth Arrow, Science, March 17, 1995,
pg. 1617.
7. Y. Takahara, B. Nakano and K. Kijima,
Characterization of the Satisfactory Decision Principle, J. of the
Oper. Res. Soc. of Japan, Vol. 21, No. 3, 1978; Y. Takahara, B.
Nakano and K. Kijima, A Structure of Rational Decision Principles,
Int. J. of Systems Science, Vol. 7, 1981.
8. The time interactive approach to incorporate
human into a model is analogous to the organizational behavior pointed out
by a number of authors. They argued that policy decisions are mired in an
incremental approach and that policymakers tend to muddle through and
redefine goals when expectations are unrealized. In such approaches a
range of policy alternatives are considered, resulting in a set of
satisfactory (acceptable) policies (bounded rationality) rather than the
"best" policy. It is then left up to the decisionmaker (user) to decide
which of the alternative policies to pursue on the basis of risk aversion,
rules-of-thumb, conflict avoidance and the likes.
See for
example:
- H. Von Storch, Inconsistencies at the Interface of
Climate Impact Studies and Global Climate Research, Meteorol.
Zeitschrift, N.F. 4, pp. 72-80, April, 1995;
- J. Rees,
Natural Resources: Allocation, Economics and Policy, Second Edition,
Rutledge, New York, 1990;
- G.T. Allison, Essence of
Decision: Explaining the Cuban Missile Crisis, Little, Brown, Boston,
Mass., 1971;
- H. A. Simon, A Behavioral Model of Rational
Choices, Quarterly Journal of Economics, Vol. 69, February, 1955,
pp. 99-118;
- C. E. Lindblom, The Science of Muddling
Through, Public Administration Review,
Vol. 19, 1959, pp.
79-99.
9. A telling quote indicating the dangers of
ignoring non-measurable aspects of reality was attributed to Daniel
Yankelovich in D. Katzner, Analysis Without Measurement, Cambridge
University Press, 1985:
"The first step is to measure whatever can
be easily measured. This is okay as far as it goes. The second step is to
disregard that which can't be measured or give it an arbitrary
quantitative value. This is artificial and misleading. The third step is
to presume that what can't be measured easily really isn't very important.
This is blindness. The fourth step is to say that what can't be easily
measured really doesn't exist. This is suicide."
10. Mihajlo Mesarovic and Eduard Pestel,
Mankind at the Turning Point, Dutton/ Reader's Digest, 1974.
11. See, for example:
- Jae Edmonds
and F. Reilly, Global Energy: Assessing the Future, Oxford
University Press, 1985.
- A. Manne and R. Richels, Buying
Greenhouse Insurance: The Economic Costs of Carbon Dioxide Emission
Limits, Cambridge, MA, MIT Press, 1992.
- Y. Matsuoka, et
al., Scenario Analysis of Global Warming Using the Asian-Pacific
Integrated Model (AIM), in Integrative Assessment of Mitigation,
Impacts, and Adaptation to Climate Change, N. Nakicenovic, W.D.
Nordhaus, R. Richels, and F.L. Toth, eds., 1994.
12. A. Ichikawa, Plenary Lecture,
U.S./Japan Symposium on Integrated Assessment, Honolulu, Hawaii,
1994.
13. S. Bremer, ed., The GLOBUS Model:
Computer Simulation of Worldwide Political and Economic Development,
Westview, Boulder, Colorado, 1987. Runs of the GLOBUS model in the
1980s predicted the political system in Rumania to be more stable than
that in Western Europe.
14. H. Simon, Operation Research Journal,
1992.
15. M. Gell-mann, The Quark and the Jaguar:
Adventures in the Simple and the Complex, New York, W. H. Freeman,
1994.
16. P. Laki, Cleveland Orchestra Annotations,
1994-95 Season, quoting George Henry Lovett Smith.
17. IMAGE 2.0: Integrated Modeling of Global
Climate Change, Joseph Alcamo, ed., Kluwer Academic Publishers,
1994.
18. Policy Implications of Greenhouse Warming
- Synthesis, Panel Report of National Academy of Sciences, National
Academy Press, Washington, DC, 1992.
19. Jose Goldemberg, Energy Needs in Developing
Countries and Sustainability, Science, Vol. 269, August 25, 1995,
pp. 1058-1059.
20. Carrying Capacity of African Countries,
IIASA/FAO, Laxemburg, Austria, 1990.
21. H. Daly and J. Cobb, For the Common Good,
Boston, Mass., Beacon Press, 1994.
22. W. Cline, The Economics of Global
Warming, (Institute of International Economics, Washington, DC, USA,
1992).
23. W.D. Nordhaus, Managing the Global
Commons: The Economics of the Greenhouse Effect, Cambridge, MA, MIT
Press, USA, 1994.
24. Yasuhiko Takahara, et. al., A Hierarchy
of Decision Making Concepts - Conceptual Foundation of DSS, J. of
General Systems Theory, 1994.
25. List of countries and organizations that
participated in the UNESCO Workshop held in Venice, Italy, in May, 1993:
Brazil, China, Egypt, India, Italy, Mexico, Russia, USA, European
Community, UNEP, UNDP, WMO, University of Venice, Municipality of Venice,
Third World Academy of Sciences, Association for Global Studies,
UNESCO.
List of countries and organizations that participated
in the UNESCO Workshop held in Santiago, Chile, in September, 1994:
Argentina, Bolivia, Brazil, Canada, Chile, Colombia, Costa Rica, Cuba,
Dominican Republic, Ecuador, Mexico, Panama, Paraguay, Peru, Uruguay, USA,
IAI, UNESCO.
26. A. Ilyutovich, B. Venkatesh, N. Sreenath and
M. Mesarovic, Multi-level Architecture for Integrated Assessment of
Global Climate Change Mitigation Policies, Int. J. of General Systems,
1996.
Mihajlo D. Mesarovic*
Mihajlo D. Mesarovic, a member of the Club of Rome, is Cady Professor
of Systems Engineering and Mathematics at Case Western Reserve University
in Cleveland (Ohio), and UNESCO Consultant for developing and directing
the Globally-oriented Universities Education Consortium. He has done
research on complex systems and the global problematic and authored a
number of books, including Abstract Systems Theory (1994), General Systems
Theory: Mathematical Foundation (1980), Mankind at the Turning Point,
Second Report to the Club of Rome (1974). He has also been a consultant to
governments, international organizations and the private sector on
decision support for top executives, global issues analysis and strategic
planning.
David
L. McGinnis
David L. McGinnis is a Research Associate with the Cooperative
Institute for Research in Environmental Sciences at the University of
Colorado. His research topics range from General Circulation Models
Simulation of Arctic Climate to possible change in Western United States
water resources emanating from high altitude snowpack. Working at the
interface between physical and social sciences, he attempts to add human
dimensions to climate research topics.
University of Colorado
Boulder, Colorado
Dalton A. West
Dalton A. West is Vice-President of the United States Global Strategy
Council. He writes and lectures on global strategic problems, particularly
in the Asia-Pacific and the former USSR, and has experience in lecturing
at Colleges and universities in the United States, Canada and the Asia
Pacific.
Systems Applications, Inc.
Annandale, Virginia
|
|
