Chapter 6 : Development of the Problem-Structuring Approach
6.1 Introduction Before embarking upon the detail of the problem-structuring approach, it is useful to
draw together several of the important issues noted thus far. In particular, these relate to
the general problem which emerge from the sustainability discourse, the characteristics of
complex problems, and some of the more significant approaches to dealing with problem
complexity.
In Chapter 2, it was argued that two distinct philosophical positions have emerged
relating to sustainability: one, referred to in this dissertation as “sustainability”, is the
monist view that humanity is a part of nature, with no special privileges or moral
standing above other species. The other, referred to here as the “sustainable
development” position, is the dualist, anthropocentric view that mankind has some
special moral privilege but that it is in our interests to look after other species and
ecosystems. The other key point to emerge from the consideration of sustainability is
that, as recent awareness of these issues emerged over the last 30 years or so, many have
been found to be global in nature, often with great size and immense complexity. The
problems of sustainability are often cross-cultural, encompass great diversity of beliefs
and values, and result widely differing worldviews. Modern institutions struggle to deal
with the problems of sustainability, creating a scepticism in the eyes of many stakeholders
as to the capacity of the “expert” to effectively resolve the issues. Hence, a starting point
for a problem-structuring approach should be to establish a philosophical framework,
which, at the minimum, allows representation of the broadest range of philosophical
approaches encountered. In addition, it should provide a forum where critique is
encouraged and facilitated. Ideally, this should not be prescriptive in terms of ontology,
epistemology, or axiology – rather it should provide a framework in which ontological,
epistemological, and axiological issues can be identified, discussed, and resolved.
In Chapter 3, a new taxonomy was proposed for describing complex problems and a new
class of problem, the Type 3 problem, was identified. Type 3 problems are generally
difficult to resolve because of conflicting beliefs and values, substantial differences in
worldviews, a willingness among stakeholders to take advantage of disparate power
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situations in coercive environments, and a tendency among some stakeholders to disrupt
the decision-making process. This type of problem is often encountered in the context
of the sustainability discourse. Often, the apparent irreconcilable position of
stakeholders causes resolution of the issue to come to a standstill. For convenience, the
characteristics of the Type 3 problem, which were identified in Chapter 3, are
summarised here:
• despite what is often an immense mass of information, there is a sense that the
problem cannot be adequately described. It is not so much that there is
incomplete information, rather the difficulty is to organise the information so as
to give clarity to the problem definition;
• the problem is characterised by uncertainty, which derives both from the
indeterministic, systemic nature of the problem itself, and from the social
interaction of the domain of interests;
• there is lack of agreement over the relevance or importance of both qualitative
and quantitative parameters, resulting from conflicting objectives, or differences
in values and beliefs. The disparate worldviews of stakeholder groups give rise to
different interpretations of the nature of the problem and why it exists, so there
may be lack of clarity regarding the problem boundaries;
• the reductionist approach does not work. There are two main reasons for this.
First, because beliefs and values are at the heart of Type 3 problems, attempts at
reductionism, which is fundamentally an analytical approach, do not give notions
of beliefs, rights, and values adequate consideration in representation of the
problem information. And, in its simple application, the reductionist approach
does not recognise the interrelationship between problem elements. Type 3
problems generally have highly complex interrelationships between various
aspects of the problem, some of which are difficult to even identify, let alone
resolve – they are often better thought of as “systems”. A symptom of this
phenomenon is that what appears to be a rational determination of a solution to
one part of the problem turns out to be nothing more than a manifestation of
another part;
• unlike the complex problems confronted by science, where the goal is to
understand and “to know”, resolution of Type 3 problems is focused entirely on
delivering a good outcome, that is, a practical result, not merely the advancement
of understanding and knowledge;
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• related to the preceding point, solutions will generally be judged according to
whether they are good or bad, rather than whether they are true or false;
• Type 3 problems are often unique. There may be similar situations which can
inform the decision-maker but there are usually differences, which are substantive
enough to preclude the application of pre-existing solutions. Often, there is no
clear test for the soundness of potential solutions and problem complexity
prevents modelling of consequences. In addition, actions taken to resolve
aspects of the problem often have unexpected consequences;
• there are often substantial constraints limiting time available for analysis. Thus
judgement is required to determine when to decide to stop analysis and make a
decision;
• there is limited or no scope for trial-and-error: solutions are usually so large and
complex in themselves that they need to be made to work. For example, once a
dam or a mine is built, it is not easily moved!
In addition to the need for a robust, philosophical framework, noted above, there were
three important conclusions that emerged from this consideration of the characteristics
of Type 3 problems. First, the problem situation is usually similar to some form of
complex system, so a “systems” approach is more likely to be effective than the
traditional reductionist, engineering approach. Second, problem complexity is so great
that, in many circumstances, it will be beyond the cognitive capacity of the human mind
to conceive of it in its entirety. A problem-structuring approach, which facilitates the
organisation of the problem information in such a way that it is readily received by
human cognitive processes, would be expected to result in more successful engagement
by the domain of interests. And third, Type 3 problems are qualitatively different to
problems of Types 1 and 2. The complexity of Type 3 problems is largely due to
conflicts in beliefs, values, interests, desires, worldviews, power relationships etc. In
addition to traditional analytical problem-solving techniques, critical theory, ethics, and
reason are required to come to terms with this type of problem. These are the important
issues which will be considered as the problem-structuring approach is developed in this
chapter.
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6.2 Problem-structuring approaches – background Problem-structuring, in its broadest sense, is engagement with the problem so as to seek
a greater understanding of the problem situation and to determine a way forward in its
resolution. According to Rosenhead and Mingers (2001), formalisation of problem-
structuring as a sub-discipline of operational research came about in response to the
recognition that traditional operational research approaches to so-called “wicked”
problems (the precursor to the Type 3 problem explicated here) were ineffectual. This
led to the development of a variety of approaches, which attempted to improve our
understanding of ill-structured problems. Rosenhead and Mingers further note that the
traditional operational research paradigm has a number of deficiencies: it focuses
overwhelmingly on the analysis of data; it seeks to reduce the problem to a single
objective and to optimise a solution around that objective, thus trading off multiple
objectives; and it assumes a hierarchical decision-making domain which “scientises” and
depoliticises the decision-making process.
The problem-structuring techniques developed in response to these objections, such as
Eden’s and Ackerman’s Strategic Options Development and Analysis (SODA) (Eden
(1989), Eden and Ackermann (2004)), Checkland’s Soft Systems Methodology (SSM)
(Checkland (1985)), Friend’s and Hickling’s Strategic Choice (Friend (1989)), seek to be
non-optimising, attempt to represent problems on various dimensions in order to
recognise the influence of people on the problem situation. They include consideration
of uncertainty and they attempt to integrate hard and soft information, through the
incorporation of social judgements.
Rosenhead (1996) argues that this more recent problem-structuring paradigm identifies
an alternative means to formalise decision analysis techniques and hierarchical processes
to represent relationships between problem system constituents. However, the position
taken here is that problem-structuring is an important step by which problem information is
examined and criticised in order apply established analytical techniques more effectively. That is to say,
combining the critical aspects of this type of problem-structuring approach with the more
orthodox analytical approaches (such as MCDA), which are sometimes seen as problem-
structuring approaches in themselves (see Chapter 8 section 8.4.5) can move beyond this
and significantly extend the understanding of the problem. Hence, the position taken in
this dissertation is that problem-structuring is an important step in coming to terms with
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and understanding Type 3 problems. Thus, problem-structuring can be a valuable means
to critically analyse problem information, using this to inform development of objectives
and values hierarchies, for use in formalised, analytical approaches.
One branch of the decision sciences which has given attention to the challenge of
structuring these highly complex problems is multiple criteria decision analysis (MCDA)
(Belton and Stewart (2002a)). (The field is also referred to as multiple criteria decision-
making (MCDM), or sometimes just decision analysis). The common features of this
family of approaches are that they recognise various sources of uncertainty (from
incomplete information, linguistic imprecision, natural variability, differences in
preferences and values and so on (Morgan and Henrion (1990a), Morgan and Henrion
(1990b)). Also, there is acknowledgement of the existence of multiple, conflicting
objectives which require trade-offs; and that, generally, there are many stakeholders, all of
whom may formulate the problem somewhat differently in consideration of objectives
and options available. Furthermore, these methods generally attempt to recognise that
trade-offs and value judgements are personal; that risks, costs, and benefits may not be
shared equally by all stakeholders; and that this may lead to substantial disagreement and
disharmony within the domain of interests. The principal aim of all MCDA techniques is
to attempt to establish a rational process whereby the problem can be attacked. In so
doing, multiple, conflicting criteria can be taken into account and a process can be
established, which is transparent and as simple as the complexity of the situation will
allow (von Winterfeldt and Edwards (1986), Belton and Stewart (2002b)).
Although there are different ways of representing the process of problem resolution (for
example, see Morgan and Henrion (1990b) p42, Beinat (1997) p6, Belton and Stewart
(2002a) p6), practices common to MCDM techniques have five fundamental steps.
These are summarised in Table 6.1 (overleaf).
It is the second of these steps, problem-structuring, that is of particular interest here, because
of its importance in interpreting the complex issues of the Type 3 problem in such a way
that structured MCDM techniques can be effectively utilised. A further reason for
focusing on problem-structuring is that the philosophical framework which underlies the
MCDM approach to problem resolution, generally is not explicit82 (Chapter 3, section
82 The work of Ulrich (1983), and Midgley (2000) are an exception to this.
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198
3.7). In an attempt to determine what the underlying philosophical principles might be,
typically, focus has been on retrospective consideration of issues, inferring the underlying
philosophical and cognitive assumptions which underpin the approach, sometimes
unconsciously. There appears to have been little done by way of developing problem-
structuring approaches which start with a fundamental philosophical framework and then
attempt to take into account the cognitive limitations of the human mind and the
heuristics which people naturally use to discover information about the problem
situation, to inform their judgement, and to make decisions in uncertainty. As noted
above, one of the main aims of MCDM techniques is to establish an open, transparent
process for problem resolution, hence, it is important that the underlying philosophical
principles are made clear to the participants.
STEP PURPOSE 1 Identification of the problem and
associated issues.
Recognition of the problem situation, identification of the domain of interests and the issues and challenges which need to be addressed for the problem to be resolved.
2 Problem-structuring.
Identification of the stakeholders, beliefs, values, system and subsystem boundaries. Qualitative consideration of the characteristics of the problem system, identifying and clearly stating key issues, uncertainties and constraints which influence problem resolution.
3 Development of a suitable model to provide adequate representation of the problem situation.
Quantitative representation of the problem situation, values, and preferences. Identification of quantitative attributes, and identification of options for consideration.
4 Utilisation of the model to illuminate thinking, develop and evaluate options, and negotiate potential ways forward.
Use model output to stimulate thinking regarding the consequences of particular options, in particular the critique of intuitive approaches to problem resolution and to build a comprehensive, robust understanding of the problem situation.
5 Development and implementation of an action plan.
Develop a robust way forward, together with insights and results of the analysis documented and incorporated into a plan of action which takes into account model assumptions and uncertainties.
Table 6.1 – Problem solution approach using multi-criteria decision analysis
Thus, there are two aims in developing the problem-structuring approach in this
dissertation. First, the approach developed here is put together from first principles.
From the philosophical principles of sustainable engineering practice proposed in
Chapter 4, a problem-structuring approach is developed which aligns with established theory as to how
human cognitive processes are thought to function and which reflects the dynamic, systems nature of the
Chapter 6 – Development of the Problem-Structuring Approach…
Type 3 problem. The assertion made here is that, by clearly stating the practical
philosophical principles of the approach and aligning the approach with human cognitive
processes, there is a much greater opportunity for engagement across the breadth and
diversity of belief, values, and cognitive capacity represented within the domain of
interests. This will result in clearer insight into the options available to ultimately resolve
the problem situation. Second, is to propose a means by which a critical discourse can
take place around problem information, so as to facilitate the construction of a values
hierarchy (sometimes called an objectives hierarchy), which, itself, is an important step in
building an MCDM model.
The meaning of the term “value” in the decision-making context is most important in the
consideration of Type 3 problems. As discussed in Chapter 2 (section 2.4.2.2), there is
an important distinction between intrinsic value, the notion that something is of value in
and of itself, and extrinsic value, the concept that value is derived purely instrumentally
(Attfield (1987)). Furthermore, the ranking of the two types of intrinsic value (the object
kind and the moral kind) are fundamentally different problems: the ranking of objective
intrinsic values requires axiological judgements and the ranking of intrinsic values of moral
standing requires moral judgements.
One of the challenges taken up by MCDM is to identify ways in which quantitative
representations can be constructed as a proxy for the axiological and moral judgements,
which decision-makers must reach in identifying a solution to the type of problem which
includes issues of one or other form of intrinsic value. Determining extrinsic value of an
object or thing requires knowledge of its instrumental value to some other object or
thing. The judgements relating to extrinsic value can be ranked qualitatively (for
example, a eucalypt tree has a greater extrinsic value to a termite than a pine tree) and
quantitatively (for example, the price of teak sawlogs is greater than the price of eucalypt
sawlogs). The Type 3 problems of the sustainability discourse will often have issues of
intrinsic and extrinsic value entangled. Hence, in order to identify potential solutions to
these problems, the problem-solving approach must attempt to not only distinguish
between methods of quantitative valuation (direct, indirect, or proxy), which focus largely
on extrinsic value but must also identify the axiological and moral judgements required to
deal with issues of intrinsic value. Often, MCDM techniques do not clearly distinguish
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between intrinsic and extrinsic value and, hence, do not adequately present a decision-
maker a rich enough representation of the decision-making challenge.
The problem-structuring technique developed here, because of its critical approach, helps
distinguish between intrinsic values, extrinsic values, and preferences through the discourse which emerges
from undertaking the problem-structuring exercise itself. This approach does not result in a values
or objectives hierarchy directly but attempts to make clear issues of intrinsic value,
extrinsic value, and preference, so that when the values or objectives hierarchy is
constructed, these issues will be properly dealt with.
In Chapter 4, it was argued that the engineering discipline, although progressing in the
technological sense, is locked in a dated philosophical paradigm. Engineering is still
largely positivist and does not reflect the substantial development of the philosophy of
science of the last 70 years. Furthermore, it was argued that engineering might be more
successful in its attempts to resolve the highly complex, Type 3 problems of the
sustainability discourse if it were to take a more critical approach, based on clearly stated
philosophical principles. The problem-structuring approach developed here uses these
principles as its philosophical foundation, in particular, arguing that in the case of
problems of the sustainability discourse, the engineer cannot adopt a position of
detached objectivity. Rather, it must be recognised that the characteristics of Type 3
problems require that the engineer must be engaged as part of the system; as a “system
component”. Information is then structured using principles derived from general
systems theory, as discussed in Chapter 3, and cognitive theory, discussed in Chapter 5.
6.3 Development of the problem-structuring approach 6.3.1 Overview The approach presented here is developed on three levels. The first of these is a
philosophical framework which allows consideration of a wide range of philosophical
beliefs and ideas. The focus is on ontology and epistemology in order to provide a
forum for axiological, semantic, and methodological issues to be explored and
considered. No distinctly philosophical defence of these positions is presented, rather
they are matters of belief and premise – generally they cannot be proved or refuted; they
are simply presented as the starting point from which the discourse can unfold.
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The second level contains a set of observations and theoretical propositions in relation to
Type 3 complex problems in two key areas. The first of these relates to the “systems”
nature that appears to be present in many of the situations encountered. And the second
relates to the nature and limitations of human cognition, and the use of a descriptive
theory of the way in which humans form judgements and make decisions under
uncertainty. This forms the basis for representing the problem in such a way as to
simplify the issues but not to discard the richness of information which is fundamental to
the description of the problem. Of key importance here is to be able to represent the
problem information without cognitive overload and to be able to quickly retrieve
information to explore new and different relationships. This requires considerable
flexibility to accommodate a wide range of cognitive abilities and preferences regarding
the way in which the human mind represents, considers, stores and accesses information.
The third level is the problem-structuring approach itself, in particular, the development
of some innovative tools and devices, with which to organise problem information in a
way which is consistent with the philosophical approach of the first level and the system
structure and cognitive constraints of the second.
There are a number of aspects of this approach which need to be noted at the outset. Of
particular importance is the underlying ontology and epistemology which encourage
critique. This will be described in detail shortly. Second, the approach to human
cognition used here is largely based on two important theories; first is Kelly’s theory of
personal constructs; and second is Beach and Connolly’s image theory. Such approaches
have been referred as naturalistic (or sometimes “second-generation behavioural”)
approaches to cognition, in that they attempt to describe the way in which humans
actually think and recognise that human cognition functions under significant constraints
as it attempts to come to terms with an immensely complex environment. Naturalistic
theories have not had the same impact as the two waves of behavioural cognitive
psychology (described in Chapter 5), which have given rise to the field of behavioural
economics, for example. However, the naturalistic theories, in particular, image theory,
have had a subtle but significant influence in both business management and public
administration. Indeed, many so-called “strategic management approaches” are entirely
consistent with the image theory paradigm. The reason that both personal constructs
theory and image theory have been used here as cornerstones of the cognitive framework
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for the problem-structuring approach is simply that it was considered desirable to have a
cognitive framework which is descriptive of the way in which people actually think. This
was considered preferable to normative cognitive approaches which attempt to explain
the differences between observed behaviour and expected so-called “rational behaviour”.
The point is simply that where the intention is to structure a problem in such a way as to
be able to engage as wide a range of interests as possible, it would seem that a good
approach would be to frame the approach around the way in which people naturally tend
to think, form judgements and make decisions.
And third, it is important to note that this is a problem-structuring approach and as such the
intent is to organise aspects of the problem (information, issues, beliefs, values, opinions,
questions, etc) across a range of dimensions, so that stakeholders may be more readily
engaged and can achieve greater commitment to problem resolution. This approach is
not intended to be or to become a problem-solving methodology – it is not merely a new set of
problem-solving tools and techniques. Rather, it should be seen as the first step in
coming to terms with the Type 3 problem, seeking to understand the problem better and
to illuminate problem information, so that the most appropriate analytical tools, such as
the family of multi-criteria decision analysis approaches, might then be applied with
greater insight and wisdom.
Albert Einstein (1954) observed that theories can be derived in two ways: constructive or
principled. Constructive theories are those which are derived from repeated observation
of phenomena allowing intuitive derivation of a framework of laws which explains the
phenomena and enables prediction. Principled theories are those which start from a
particular set of premises and which are logically derived to explain and predict observed
phenomena. The problem-structuring approach presented here has been derived using
both these avenues. The philosophical foundations are principle-derived; the cognitive
psychology and approach itself are constructive.
6.3.2 Level 1 – Philosophical framework In Chapter 4, six critical, practical philosophical principles were developed for
engagement in the highly complex Type 3 problem found in the sustainability discourse.
These are repeated here for ease of reference:
P1. There is a physical world which consists of mind-independent things;
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P2. There is a mental or psychological world which is an emergent, human
phenomenon, with which the human mind represents its perceptions of the
physical world;
P3. In a physical sense, there is an infinitely complex parade of events and
phenomena located in space and time – this is “the way the world is”;
P4. The world is fundamentally indeterministic in nature and its individual parts can
only be understood in relation to the whole, that is, in the context of the
“system”;
P5. Although it is beyond the capacity of the human mind to understand completely
and to describe adequately the way the world is, the human mind forms linguistic
and other representations based on perception and thought which attempt to
arrive at some “true” (but incomplete) understanding of the way the world is;
P6. “Truth”, a human phenomenon, may be considered from two perspectives. One
is an objective “fact of the matter” which relates to physical phenomena and
objective representations of the real world and these representations can be
determined to be either true or false. The other is a subjective representation of
socially-determined phenomena and the truth or falsity of these representations is
determined linguistically, according to generally accepted and verified beliefs and
values. In both cases, the appropriate position to take is to acknowledge them as
being true (or false) subject to the ever-present possibility of error and the
context in which their truth (or falsity) is determined. Both correspondence and
coherence approaches to truth each have their place but only as criteria for
determining truth.
6.3.2.1 Critique of established engineering practice in the context of these principles In Chapter 4 it was argued that to date, engineering practice has been largely
instrumentalist, in that it uses science as an instrument to model the real world and to
predict its behaviour. In addition, the scientific paradigm which most engineering
follows is positivist and mechanistic. Hence, the prevailing engineering approach to
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solving problems has been largely reductionist in its nature, aiming to break problems
down into their constituent parts, identifying solutions to these parts and then
synthesising a solution to the whole problem, by integrating the solutions to the parts. In
the last 50 years or so, systems analysis techniques have been utilised successfully, but
nonetheless, these are still based largely on the positivist, mechanistic, instrumentalist
worldview.
If this sketch of modern engineering practice is considered in relation to the six
principles noted above, it is clear that the strongest influence is principle P1: engineering
practice generally focuses on identifying quantitative model, which represent real world
things and phenomena. The complexity referred to in P5 is represented in the
uncertainty of quantitative modelling and the constraints placed on utilisation of model
output (for example, noting that models should only be used in certain circumstances or
applying safety factors to take into account differences between model behaviour and
observed real-world behaviour). Consideration of principles P2 and P5 suggests that
modern engineering practice typically gives little acknowledgement or credence to beliefs
and values which are consistent with the post-normal scientific paradigm. The concept
of “system” embodied in P4, whilst acknowledged in approaches such as systems
analysis, is compromised in the positivist concept of the independent, detached observer.
The extensive reliance of engineering practice on the simplifying assumptions contained
within reductionist analysis further conflicts with this principle. Criteria for determining
truth (P6) are largely correspondence-theoretical, typically structuring criteria for truth
according to whether or not model output corresponds to the observed behaviour of the
real world phenomena that it represents. Coherence-based approaches to truth are
mainly limited to establishing the coherence of quantitative models in relation to current
scientific theory and generally do not take into account issues of moral interests, rights,
beliefs, and values.
A conceptual representation of this is shown in Figure 6.1. That is, modern engineering
practice uses a problem-structuring approach with principle P1 at its centre, and with the
other philosophical principles being peripheral at best and having limited influence on
problem-structure.
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P1
P5
P2
P3
P6
P4
Figure 6.1 – Conceptual representation of traditional philosophical paradigm of engineering practice.
P1
P5
P2
P3
P6
P4
P1
P5
P2
P3
P6
P4
Figure 6.1 – Conceptual representation of traditional philosophical paradigm of engineering practice.
The manifestation of this paradigm in problem-structuring is a heavy technological
emphasis, with the engineer disconnected from most aspects of the problem, other than
through a determination to resolve technological issues. This often leads to seeing
aspects of the problem outside this representation as being interference and obstruction,
rather than parts of the problem which need to be taken into account. There are three
serious, fundamental deficiencies with his approach. First, the positivist, mechanistic
model which derives from the reductionist approach is inconsistent with the systems
approach required by P4. Hence, engineers often do not recognise their significant
influence in engagement with the full domain of interests (and often this influence
evokes a hostile response from other stakeholders.). Second, it does not give due
consideration to the social dimensions of the problem, which emerge when the domain
of interests’ widely differing beliefs, values systems, and interests are taken into account.
And third, the issues to which coherence-based approaches to the determination of truth
are most appropriate are often seen as being barriers external to the problem, rather than
part of it. Hence, often these are marginalised or excluded from consideration
altogether.
6.3.3 A new approach to structuring type 3 problems An alternative way to utilise these principles is to recognise their “connectedness” and
interdependency, as shown simply in Figure 6.2 (overleaf).
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P5
P4
P2P3
P1
P6
Figure 6.2 – Conceptual representation of the proposed philosophical paradigm of engineering practice.
P5
P4
P2P3
P1
P6
P5
P4
P2P3
P1
P6
Figure 6.2 – Conceptual representation of the proposed philosophical paradigm of engineering practice.
Considering the principles arranged in this way enables a fundamentally different
approach to structuring problems to be devised. Representation of the physical world is
no longer at the centre of the problem-structure. Rather, all principles are given equal
emphasis. A problem-structure which arises from this thinking might be described in
these terms. Of key importance is the “system” nature of the world, that is, the problem
situation is represented as an open subsystem. The psychological influences in creating
the problem-structure are also recognised as being key determinants of the approach and,
ultimately, the methodology by which a potential problem solution is identified. This
approach also recognises the importance of locating events both in space and in time.
The additional emphasis on the time dimension means that historical perspectives can be
utilised to contextualise the current problem-structure and to inform the domain of
interests on different perspectives of the problem. The complex nature of the problem
and the location of the problem within the holistic system suggest that a systems
approach to both problem-structuring and problem solution might be fruitful.
The recognition that problem complexity can never be completely and adequately
understood, due to human cognitive limitations suggests that the characteristics of
human cognition should be taken into consideration, as part of the problem-structuring
methodology. Using both correspondence and coherence criteria for the determination
of truth, both in structuring the problem and identifying possible courses towards
solution, significantly increases the potential for engagement with the domain of
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interests, because many worldviews can be represented and incorporated into the
problem-structure. Recognition of the ever-present possibility of error and that attempts
to arrive at a complete understanding of the problem can never be completely successful
due to the problem complexity and system indeterminacy suggest that a precautionary
approach should be adopted. This is particularly important in situations where potential
problems solutions may have major, intractable impact. Importantly, the engineer is no
longer seen to be an independent, detached observer but is engaged in the problem itself,
as a system element: the engineer is now a part of the problem.
In summary, such an approach as this places emphasis on two specific issues. First, is
the importance of representing the problem as a system, composed of system elements
and subsystems, contained within a holistic, systemic universe. And second, is the need
to take into account the functioning of human cognition. Of particular importance are the
way in which representations of the physical world are thought to be formed; the means
by which complexity is dealt with and judgements are formed (particularly in relation to
future events and uncertainty); and the way in which problems are contextualised in
relation to the spatial and temporal dimensions of the real world. This leads to the
second level of the problem-structuring approach (which will be developed in the next
section), namely utilising systems theory and a systems approach as the functional
structure for the representation of Type 3 problems. In addition, theoretical approaches
to human cognition, judgement, and decision-making are utilised to better engage the
domain of interests, thus enabling them to more clearly contextualise the problem within
their spatial and temporal frames of reference.
6.3.4 Level 2 – Systems and cognitive framework for structuring type 3 problems
As note above, at the second level, there are two theoretical building blocks used to
develop the problem-structuring approach. System theory is used as the paradigm for
describing and engaging with the problem itself. And a framework, built on
psychological theory, is developed to relate to the way in which human cognition
functions, in particular, regarding judgement, choice, and decision-making in uncertainty.
Two important theories are Kelly’s theory of personal constructs (Chapter 5, section
5.3.2) and Beach and Connolly’s image theory (Chapter 5, section 5.3.4). In order to
represent the system itself and the relationships between system elements and
subsystems, and to contextualise problem information in the three spatial dimensions,
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cognitive maps are used. Then, to represent the problem temporally, and to facilitate
identification of uncertainty, inconsistencies, and gaps in knowledge, storytelling,
scenario analysis, and narrative (Chapter 5, sections 5.3.5 and 5.3.6) are utilised.
6.3.4.1 System theory and the systems approach As noted in Chapter 3, many complex problems can be considered to be complex social
systems. von Bertalanffy (1950) was highly influential in the development of a systems
approach, with his general system theory. He noted that general system theory is not
simply a system of differential equations used as approximations to describe an
ontologically mechanistic world, rather it represents a means to attack a newly identified
class of problem, not found in exact science. Later, von Bertalanffy (1972) proposed a
distinction between what he referred to as “real systems” (those which exist independent
of the observer) and “conceptual systems” (products of human thought such as
mathematics, music, and logic). Together with his concept of “abstracted systems”, by
which the system concept corresponds with reality, von Bertalanffy developed what he
referred to as a “systems ontology” and a “systems epistemology”, differing significantly
from the logical positivist approach83. Von Bertalanffy also noted the potential of
adopting a systems approach to problems relating to values, referring to the emergence
of “two cultures” of science and humanities, previously observed by Snow (1959).
Utilising general system theory as the paradigm for structuring Type 3 complex problems
provides the means to move the paradigm of engineering practice away from the logical positivist approach
to the critical approach, which has evolved in both the natural and social sciences since the
1930s. Such a step is consistent with the philosophical principles established in Level 1.
Operational research has seen a number of systems approaches in dealing with problem
complexity, particularly those relating to complex social problems. Multi-methodology
approaches, such as “total systems intervention” or “system of system methodologies”,
(Flood and Jackson (1991a), Flood and Jackson (1991b)) are representative of
approaches which aimed to bring a range of interventions, selecting those which appear
to be most effective for different parts of the problem. These have been useful but have
also been criticised as lacking sufficient epistemological depth and uncompromising
rigour in their pragmatic acceptance of particular methodological interventions (see
Mingers (1992), Brocklesby and Cummings (1996), Ulrich (2001)). Other ways such as
83 These three system types are coherent with Popper’s three worlds.
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integrating “hard” and “soft” systems analysis techniques have also been attempted.
However, these appear to suffer from the same lack of philosophical depth and would be
expected to be only partly useful, because they do not rigorously provide the means to
take into account the issues of moral accountability, values, beliefs, and rights of those
with moral interests. Thus, they lack the means to make the practitioner’s value
judgements clear in determining system boundaries.
The challenge in many of the complex problems of the sustainability discourse is that any
model of the system needs to represent not only its physical characteristics, but also
issues of morals, ethics, values, beliefs and the interests of both human and non-human
constituents. Generally, this requires moving beyond the simplistic but widely embraced
“triple bottom-line” approach to which sustainable development resorts in order to
accommodate these issues. Rather, the problems of the sustainability discourse present
unique challenges, which can best be approached by integrating “hard”, “soft”, social,
and natural systems methodologies, recognising that the traditional analytical and
strategic methodologies cannot be completely satisfactory, because they do not
adequately take into account the issues of moral accountability, values, and so on, as
noted above. Ulrich developed a critical systems methodology (Ulrich (2001), Ulrich
(2003)), which proposes the conduct of a critical, rational, argumentative discourse,
recognising the importance of a civil society, in which many voices can be heard and the
validity of their claims tested. Ulrich argues for an emancipatory orientation, aimed at
removing coercion and power imbalances and a reflective professional practice which has
an open mind towards new approaches and complementary techniques. In addition, he
stresses the importance of establishing system boundaries and of the critique of the
judgements as to where these boundaries lie.
However, none of these approaches explores the dynamic nature of systems and their
response to disturbances. An established way of modelling and characterising systems
analytically (for example, in engineering practice) is to develop a mathematical model of
the system, apply a disturbance to both the model and to the system and the to compare
the response of the model with the observed response of the real-world system. If both
responses are the same, the model is accepted as a satisfactory representation of the real
system, at least within the range of assumptions under which the model is built and
tested. A key assertion here is that qualitative exploration of the response of the Type 3
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problem to a hypothetical disturbance can be a valuable means to gain insights to the
problem and to its systemic nature. Such insights can then be used to further inform the
way in which the problem information might be structured.
Hence, this approach is to structure the problem, using the system metaphor, and then to
explore qualitatively how such a system might respond to a hypothetical disturbance
across a range of problem dimensions, which are representative of the nature of the
problem. In particular, emphasis is placed on qualitative and intuitive exploration of the
problem, with the intention being that the approach should be enquiring, discursive, and
critical, with emphasis on values, beliefs, and issues of moral standing and accountability,
because these important considerations are generally discarded in the traditional,
reductionist engineering approach. The purpose is to encourage the qualitative and
intuitive engagement of human cognitive processes by structuring the problem
information in such a way that it can be readily accessed as needed for future analysis.
Also, importantly, the aim is to facilitate understanding and thereby to encourage the
inclusion of as wide a representation of the domain of interests as possible.
Once the problem is structured in this way, it is anticipated that rigorous analytical,
quantitative methodologies can be more productively employed, ultimately to identify a
“solution space” for the problem. In many problem situations, it is anticipated that these
analyses can be applied iteratively, thus gaining further insight into the nature of the
problem. In order to consider the response of the system to the hypothetical
disturbance, assumptions need to be framed regarding system boundaries. That is not to
say that the system metaphor used here is one of a closed system, rather it is a bounded,
open system. The process of boundary critique, identified by Ulrich and Midgley, is
important in order to determine what interests are to be “swept in”, or included in
consideration of the problem.
Drawing upon the above points, a set of systems principles are proposed upon which the
problem-structuring approach is based:
S1. A system consists of complex, interacting elements;
S2. In many systems, there is a hierarchy of order, which includes subsystems;
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S3. The system responds as a whole, the changes to individual elements (or
subsystems) being dependent on all the others;
S4. A small change in one part of the system may cause significant change to the
state of the total system;
S5. Dynamic mechanisms exist within the system, which determine the way in which
it responds to disturbances;
Furthermore, in the case of the social systems found in Type 3 problems, these additional
principles apply:
S6. System and subsystem boundaries can be identified through rigorous critique;
S7. An emancipatory orientation should be used to remove the effects of coercion
and power imbalances;
S8. Insight into dynamic system response can be gained through the conduct of a
critical, rational, argumentative discourse.
This outlines the systems paradigm upon which the problem-structuring approach is
based. The next question to consider is the way in which representations of the problem
can be formed, so that problem complexity can be dealt with adequately within the
limitations of human cognition.
The approach taken to this challenge is to model the system according to, what are
understood to be, the important human cognitive processes used in forming judgements,
making choices, and reaching decisions in uncertainty. One of the significant challenges
in structuring Type 3 problems in a way which moves beyond the traditional, reductionist
engineering approach, is to organise problem information in such a way that it can be
dealt with within the constraints of human cognition. At the same time, the approach
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must protect against loss of important information and should work towards a greater
richness in the description of the problem.
6.3.4.2 The application of cognitive theory The issues of human cognitive psychology, which are drawn upon to develop the
approach, were identified in Chapter 5 (sections 5.3.2 to 5.3.6) and are repeated again
here for convenience. They are:
C1. Humans form mental representations of things and phenomena which they
encounter in World One. These appear to consist of some form of dynamic
system of depictions, non-visual patterns, and linguistic representations, which
can be stored, recalled, processed, correlated, and evaluated. These
representations, or “images”, form the substance of our World Two knowings
and are used to construct World Three propositional knowledge;
C2. The human mind can only retain about five or six units of concentration in short-
term memory at any one time, but has a very great capacity to retain information
in long-term memory and recall this to short-term memory as required;
C3. Fundamental to the way in which humans construct their conceptions of reality is
the “dichotomous construct”, in which three representations are needed: two of
these represent opposing poles of a similarity, against which the third
representation is contrasted. People then make use of a “scalar” to represent the
magnitude of the contrast;
C4. When confronted with situations where there is considerable uncertainty, humans
form judgements, make choices, and reach decisions using processes, which can
be adequately described using the “image theory” model. Of particular
importance here are the three types of image utilised by the decision-maker. The
“value image” helps the decision-maker frame the problem in the context of their
beliefs and values and what they believe is right. The “trajectory image” sets an
agenda or set of goals. And the “strategic image” is an image of plans to guide
behaviour and to create impressions of what the future will be like. Image theory
is well suited to problem situations which have strong intuitive, axiological issues
because of the way in which values are embedded in the decision-making process
itself through, the representations contained in the value image;
C5. Humans attempt to relate spatially to things and phenomena in the real world,
using various forms of cognitive map. The schematic structures of cognitive
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maps provide a means to combine both intuitive and analytical information and
thought, depicting these in a single, physical, visual representation. Such maps
become holistic representations of the entire system under consideration. They
need not be specifically isomorphic, but represent an important mechanism by
which information is contextualised and structured. This is to facilitate the
reaching of decisions, both for short-term, instantaneous decision-making and
long-term, contemplative deliberation;
C6. Whilst cognitive maps provide the means for humans to spatially orientate
problem information, there appears to be an additional cognitive process to
contextualise problem information in the temporal dimension. The use of
narrative is an important cognitive and social device to integrate temporal and
spatial representations of problem information. The use of narrative is also
thought to be an important cognitive device for reconciling apparent
inconsistencies and deficiencies in knowledge regarding the problem;
C7. Taken together, the cognitive map and the narrative allow physical representation
of cognitive processes in all four real-world dimensions and provide a means to
reconcile inconsistencies and identify gaps in our knowledge. Thus we come to
understand better the various aspects of problem complexity.
6.3.4.3 Integration of the systems approach and cognitive theory Using concepts drawn from both systems and cognitive theory, the problem-structuring
approach developed here qualitatively represents the Type 3 problem as a bounded, open
system. To facilitate analysis of the response of the system to disturbances, system
boundaries are proposed and subjected to critique, through engagement of a wide
representation from the domain of interests.
6.3.4.3.1 Identifying and structuring problem information
There are two major challenges in organising information relating to Type 3 problems.
First is that the problems are of such complexity, that a large amount of information is
required to characterise them. And second, is that there is usually an overwhelming
amount of information available regarding the problem, which will be considered to be
of differing degrees of importance, depending on the perspective of the various members
of the domain of interests. Human cognitive limitations make it difficult for individuals
to come to terms with both the breadth and depth of problem information. The solution
proposed here to this issue is to identify ways in which various problem dimensions may
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be identified, and then to categorise and sort information according to whichever
dimension it is most closely associated. One way to achieve this is to identify aspects of
the problem which exhibit particular similarities (Verma (1998)), representing these as
problem dimensions84. In addition, elements of problem information are examined to
determine what relationships might exit between them, both within and between
problem dimensions. In some cases, these relationships will be relatively simple but in
others, subsystems within the problem system boundary must be identified and
represented.
As information is identified which is anticipated to be influential on system behaviour, it
may be mapped, so as to represent important relationships across the various problem
dimensions. The way in which this mapping takes place will be described in the detail of
the problem-structuring devices developed in Level 3. The important point to note here
is that the mapping process should align with the cognitive processes which take place to
identify and to evaluate problem information. Three elements of cognitive psychology
are important in preparing these cognitive maps. First, information must be organised in
representations which come most easily to those involved in the mapping process. That
is, the representations should be depictions, linguistic descriptions, non-visual patterns,
or a combination of all of these – the determinant should be the ease with which these
are identified and recorded for future use. Second, wherever possible, problem
information should be represented in groups of five to eight units of concentration, so
that the part of the problem under consideration can be focused on by participants as a
single, integrated piece of information. And third, by exploring the relationships between
system elements (as grouped into their units of concentration of five to eight pieces), the
richness of problem information can be identified and recorded, using dichotomous
constructs, as suggested by personal construct theory.
6.3.4.3.2 Application of system theory
Once an initial characterisation of the Type 3 problem has been formulated and the
relationships between important aspect of the problem have been identified, an initial
image of the system can be described. This process is as follows:
84 Verma notes that similarities are fundamental to the way in which people understand things. There are
three types of similarity: structural similarity; functional similarity; and teleological similarity. An example might demonstrate the distinction. Sugar and saccharin are both sweet, so they are functionally similar. However, they are different chemical compounds, so they are structurally dissimilar. They are also teleologically dissimilar because they were not intended to be sweet, they just happen to be so.
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i. Describing the initial system state – This is referred to as the “As-Is” system state
– that is, an initial description of the system, which identifies the key relationships and
the dynamics of the system’s elements and its subsystems. There is an implication
that the “As-Is” system state is in stable equilibrium, however, this need not be the
case. As part of this initial system characterisation, an initial description of the various
“value images” represented in the domain of interests can be described. This is a
comprehensive description of the moral and ethical issues contained in the problem
description, contextualised in terms of the initial system model. This is an important
step in identifying the moral issues represented in the domain of interests and is
essential in order to undertake a substantive boundary critique.
An important characteristic of systems, identified by von Bertalanffy, is the nature of the
system’s response to some disturbance. The system responds as a whole, determined by
the interrelationships both within and between subsystems and system elements. Such
responses can be exponential. Hence, the next step in structuring the problem is to
consider what qualitative response the system might exhibit to some hypothetical
disturbance.
ii. Defining a plausible, hypothetical system disturbance – Such a hypothetical
disturbance is prepared using a similar approach to the characterisation of the system
itself – that is, establishing a plausible system disturbance and developing a rich
description of the disturbance, using the construct approach briefly outlined above.
This will be discussed in further detail in the Level 3 description (section 6.3.5.2).
Once the hypothetical disturbance has been imagined, the response of the system model
to this disturbance can be explored.
iii. Qualitative exploration of system response – Representatives from the domain
of interests are encouraged to discuss their views of the likely outcome, as the system
responds to the hypothetical disturbance. It is important to note that this is primarily
an intuitive, qualitative, discussion involving stakeholders. Ultimately, the aim is to
identify issues which subsequently can be explored, using established analytical and
quantitative techniques. Using the initial description of the “As-Is” system, various
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scenarios can be explored as plausible responses to the hypothetical disturbance.
Scenario analysis is a technique which has been developed in large government and
commercial enterprises (for example, Royal Dutch Shell) and will be covered in
greater depth, as Level 3 of the problem-structuring approach is developed further in
the next section. The various scenarios representing the system model response to
the hypothetical disturbance are considered by the representatives from the domain of
interests. Then, one plausible scenario is identified as being the “Likely Future”
response of the system to the hypothetical disturbance.
iv. Exploration of alternative “Desirable Futures” – In the same way as the initial
“As-Is” system was developed, the “Likely Future” scenario-set is also represented in
a set of multidimensional cognitive maps, together with any other depictions and
textual material which may have been developed to enhance the richness of the
system model. Now the model description can be explored further by the
representatives of the domain of interests. An alternative system response, capable of
achieving the “Desirable Future” can be considered. This uses the existing analysis,
exploring changes which might be made to the “As-Is” system model (and by
implication to the real-world things and phenomena represented by the system
model), so that it becomes capable of delivering the “Desirable Future” response.
Up to this point, it is expected that, for practical reasons, the problem-structuring would
have been undertaken by a relatively small group of representatives from the domain of
interests. Normally, these people will be chosen because of their knowledge, expertise,
or specific interest in the problem situation under consideration. In order for the broad
engagement of the wider community to take place, the problem needs to be more readily
accessible. In other words, the process sketched up to this point is relatively complex
and would be unlikely to appeal to many in the community. The way in which this issue
is addressed is through the development of a set of word-rich narratives, which concisely
but comprehensively describe the problem from the range of perspectives likely to be
encountered in the broader community. The important point here is that the problem
must be described using a set of agreed information. This information may be interpreted
differently, depending on perspectives represented in the domain of interest. Narratives
developed from this set of agreed problem information, must then be able to be
deconstructed later (as might be done by a naïve, informed reader), to reveal the
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information, even though it may be coloured by the perspective, beliefs, and values of
the narrative writer. That is to say, such narratives rigorously prepared will carry
conscious or unconscious bias, due to the perspective of the narrative writer, but the
deconstructed content of the narrative will contain all of the agreed problem
information. Indeed, the way in which the final quality of the set of narratives is assured
is by objective deconstruction of each narrative in order to ensure that all problem
information agreed by the original representatives of the domain of interests is present.
v. Engaging the broader community – the narratives are used as the principal
means of communicating the issues to the community. Broad-based community
critique of the problem system, as represented in the set of narratives is encouraged
through activities such as seminars, public meetings, and workshops of interested
community representatives. During this process, specific narratives are selected the
set prepared using the process described above, according to interests of the particular
audience. Discussion and evaluation by the audience further elicits values and
preferences, which can be captured and built into the problem structure. After each
such community engagement session, narratives can be revised and further developed,
provided there is time and resource available to rigorously critique the resultant
revision against the original agreed set of problem information. The information
elicited through this additional debate and discussion facilitates the construction of
the values hierarchy to be used as part of the formal application of decision-analysis
techniques.
Having outlined the way in which the theoretical components have been integrated into
the problem-structuring approach, the third level will now be developed. This level
contains the devices and tools which have been created to give substance to the
theoretical approach just discussed.
6.3.5 Level 3 – Tools and devices for structuring type 3 problems This section describes a number of devices which have been developed and assembled
into an integrated approach, in order to structure the information which characterises the
Type 3 problem. It is consistent with the underlying philosophical principles of Level 1
and the integrated framework of systems theory and cognitive theory developed in Level
2. The aim is for these to facilitate representation of the problem information using the
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system paradigm and structuring the information in a way which relates isomorphically to
human cognitive processes.
Many Type 3 complex problems of sustainability can be thought of as complex “socio-
eco-systems”, often with technologically-based subsystems, which often are the main
focus of analysis. Examples of these types of systems are development of sustainable
solutions to complex problems such as water, transportation, energy infrastructure, and
large resource developments (for example, mines and oil-and-gas projects), and so on.
That is, they are comprised of complex, interacting social and ecological system elements
which respond as a whole and which contain complex interrelationships between system
elements. Often, the subsystem (such as the water supply, the mine, the transportation
system, etc) can be well defined in a technological sense but the interactions with the
social and ecological system elements are complex and poorly understood. Frequently,
there are diverse and even irreconcilable worldviews and philosophical positions among
stakeholders and, because of the complexity of both the issues and of the information
available, there are cognitive limits to the extent to which the problem can be described
and comprehended. Hence, it is argued here that if the problem structure is isomorphic
with or mimics human cognitive processes, there is a greater likelihood of a wider
constituency being able to engage with and understand the issues contained within the
problem.
This approach differs from established engineering practice, in that it is deliberately non-
reductionist – information is not discarded, rather it is arranged and stored in a way to
identify relationships between various aspects of the problem, so that they can be readily
recalled for later consideration. In comparing the established, reductionist engineering
approach with the systems approach developed here, on one hand, the reductionist
approach attempts to focus on critical issues relating to the problem, discarding those
things which are considered to be unimportant or marginal. On the other, the systems
approach treats all problem information as potentially useful and attempts to identify
important relationships which exist within the system. However, information and
relationships which, at the time, are not considered to be important are not discarded;
they are kept for future reference. This might be likened to identifying landmarks (Fiol
and Huff (1992), Chown et al. (1995)) in a piece of scenery – certain aspects stand out
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and may be considered to be more important than others in some contexts but no
information is lost.
To achieve this, problem information needs to be organised in such a way that
relationships between elements can be readily identified, so that the systems nature of the
problem situation becomes clear. To avoid to avoid losing clarity as the complexity and
richness as the model development proceeds, problem information can be arranged in
dimensions or layers, so that important information and relationships can be stored for
later reference. This also facilitates representation of problem information in a way
whereby relevant aspects of the problem can be accessed readily by the various
representatives from the domain of interests. Thus, the problem-structuring process
consists of five steps (see also Figure 6.3 overleaf):
1. System representation.
2. Definition of hypothetical system disturbances.
3. Imagine plausible system responses.
4. Multi-dimensional critique.
5. Critical discourse.
Because of the relative complexity of the modelling process, it would be expected that
the first four of these steps would be manageable only with a relatively small working
group of participants representative of the entire domain of interests. It is important to
select these participants, based on their interest in the issue, the extent to which they
represent the domain of interests (or at lease one part of it), their knowledge of the issue,
so that a balanced, diverse representation of the entire domain of interests is achieved.
Arguably, this is the most challenging aspect of the problem-structuring approach
because of its susceptibility to bias (both conscious and unconscious), and the need to
have potentially irreconcilable and confrontational positions represented on the working
group. Indeed, many projects become unviable because of the differences among
participants over representation or power within of the domain of interests. (Experience
in the case study suggests that this also can be manifested in people deciding not to
participate because they believe their work will not be valued or influential).
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Dimension 5Dimension 4
1. System representation:
2. Define hypothetical disturbances:
3. Imagine plausible system responses:
4. Multi-dimensional critique:
Dimension 7 Dimension 6 Dimension 8…
Dimension 1 Dimension 3Dimension 2
Disturbance
5. Critical discourse: Engagement with Domain of Interests
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Figure 6.3 – Concept map of the problem-structuring process
Dimension 5Dimension 4
1. System representation:
2. Define hypothetical disturbances:
3. Imagine plausible system responses:
4. Multi-dimensional critique:
Dimension 7 Dimension 6 Dimension 8…
Dimension 1 Dimension 3Dimension 2
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5. Critical discourse: Engagement with Domain of Interests
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Dimension 5Dimension 4
1. System representation:
2. Define hypothetical disturbances:
3. Imagine plausible system responses:
4. Multi-dimensional critique:
Dimension 7 Dimension 6 Dimension 8…
Dimension 1 Dimension 3Dimension 2
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5. Critical discourse: Engagement with Domain of Interests
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Figure 6.3 – Concept map of the problem-structuring process Each of these steps will now be described in greater detail.
6.3.5.1 Step 1. System representation – boundary definition and trilemma system mapping
The problem is initially described in such a way that system boundaries can be identified,
“landmark” system elements can be recognised, and subsystems can be defined. The
challenge here is to retain as much information as possible about the definition of the
problem and to organise it in a way, which does not overload cognitive function but
allows quick recall of information for further consideration. Important in structuring the
problem information as a system is the “trilemma”, a cognitive mapping device for
representing problem information as a set of cognitive constructs, consistent with Kelly’s
theory of personal constructs. Boundary critique and the development of an initial
“value image” as part of the “As-Is” initial system state system are key parts of the first
step.
There are three important considerations in representing the problem as a system:
• establishing the context of the problem using a comprehensive narrative to
provide background context to participants in the problem-structuring exercise.
In many cases, this narrative will be historical in nature, because many Type 3
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problems are identified as a consequence of their having been recognised for a
long time but attempts to solve them have not been successful;
• establishing system boundaries and undertaking “boundary critique” to determine
what aspects of the problem should be “swept in”, what should be excluded, and
what should be considered to be at the margin between that which is included
and that which is not. It is important that the process for undertaking boundary
critique is rigorous. Reference is made here to an extensive line of work on this,
developed in the area of operational research;
• identifying “landmark” system elements and subsystems which are expected to
have the most influence on system response and behaviour. This takes place at
the same time as the boundary critique
Each of these will now be considered more closely.
6.3.5.1.1 Development of a contextual narrative
This initial step is to acquaint those interested in the problem with the various issues
which have contributed to the emergence of the problem situation. Because most Type
3 problems emerge over a fairly long period of time, the narrative tends to be historical
and should focus on key aspects of the problem, which are thought to be the major
contributors to complexity and problem irreconcilability. This narrative should be brief
but cover thoroughly the major influences on the problem. The narrative presented in
the case study in Chapter 7 gives both a chronological perspective of the way in which
the problem has developed and also gives some insight into the way in which the system
has responded to various disturbances. Generally, the narrative will be prepared by one
person but should be subject to critique to ensure that it is factually accurate and to
identify and to make transparent the implicit and explicit values and beliefs of the author.
6.3.5.1.2 Boundary definition and critique
The first step in defining the system boundary is a concise but embracing statement of
the problem. For example, “the challenge of providing a sustainable water system for a
major metropolis” (which is the broad theme of the case study developed in Chapter 7).
This stimulates many questions (for example, does the problem relate only to provision
of potable water? Is sewerage included? What constraints are there in terms of
infrastructure development – such as capital cost, environmental issues, health and so
on? What political obstacles might exist? What technologies are available? How far
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beyond the metropolis does the impact of the water system extend? What institutional
barriers may need to be considered? What moral interests need to be recognised and
taken into account?) These questions naturally lead to consideration of system
boundaries. With Type 3 complex problems, boundary definition is usually unclear and
judgement needs to be used as to what should be included and excluded from
consideration and the extent to which certain problem aspects and interests may be
marginalised. The approach adopted here follows a line of thought which originated in
Churchman (1970) and Ulrich (1987), and was further developed by Midgley (1992),
Ulrich (2001), Yolles (2001), Ulrich (2003), Midgley (2003), Midgley and Reynolds (2004),
Ulrich (2006). (The detail of the approach appears in the case study example – see
Chapter 7, section 7.3.5.)
This considers boundary definition to take place through a discursive process of dialogue
between members of the domain of interests, taking into consideration not only the
technical judgement of experts but also giving due weight to the ethical consideration of
the interests which should be represented in the problem (see Figure 6.4).
SystemSystemSystemSystem
Outer boundary
Inner boundary
Area for boundary critique
SystemElement
SystemElement
SystemElement
SystemElement
SystemElement
SystemElementSystem
Element
Sub-systemSub-system
SystemSystemSystemSystem
Outer boundary
Inner boundary
Area for boundary critique
SystemElement
SystemElement
SystemElement
SystemElement
SystemElement
SystemElementSystem
Element
Sub-systemSub-system
Figure 6.4 – Determining system boundaries – boundary critique. In practice, this is achieved by identifying experts, knowledgeable in the issues proposed
for inclusion in the problem system, together with representatives from interest groups
which are identified as taking a particular interest in the problem. These could be
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community groups, NGOs, or vocal individuals. Type 3 problems are usually of such a
magnitude and prominent position in the community that normally both experts and
interest groups are identified readily. The challenge for the steering group is to ensure
that all interests in the problem are represented and that participation does not favour
one group over another.
The important point to note is that for Type 3 problems, this is an ethical discourse not
simply definition of a system boundary in its physical sense. For example, in undertaking
boundary critique for a metropolitan water system, ethical considerations regarding
marginalisation of interests of small population groups well away from the metropolitan
area but within a water catchment requires careful consideration. Similarly, depending on
the intrinsic valuation established during the boundary critique process, non-human
moral interests, such as those of threatened species or ecosystems will be either included
(if a sustainability position is adopted) or possibly excluded (should a sustainable
development position be taken). In practical terms, the way in which this boundary
critique takes place is through a process of engagement with representatives from the
entire domain of interests. Irrespective of the actual determination of system boundaries,
if the process if followed rigorously, both implicit and explicit valuations will become
transparent both to the participants in the analysis and to those who may not be directly
involved.
6.3.5.1.3 Representing system elements
The approach taken here is that the system can be represented in sets of about five to
eight system elements or subsystems. As many sets as required to fully represent the
problem information can be prepared, with relationships being identified between system
elements both within and among these sets, provided that the data sets can be readily
stored and recalled for consideration. As noted above, the device used to represent
system elements is the “trilemma”, a cognitive mapping device coherent with Kelly’s
personal construct theory. This is consistent with principle C2, allowing the mind to
focus on a small number of specific aspects of the problem at one time, while quickly
being able to recall other information for consideration.
The term “trilemma”, representing a situation of three conflicting choices or pathways is
by no means new but it has been given popular currency recently, particularly in
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economics and in corporate strategic planning. Rodrik (2000) refers to the work done by
Obstfeld and Taylor in relation to Mundell’s and Fleming’s work in the 1950s on
exchange rates, capital movement, and monetary policy. Rodrik presents the trilemma in
its original interpretation: it is impossible to satisfy all three constraints at once – in order
to achieve two outcomes, the third must be sacrificed. However, Royal Dutch/Shell, in
their long-range strategic planning process developed the trilemma concept somewhat
differently – they considered a trilemma of three major forces in tension and considered
what the outcomes might be if one or other of the forces were to become dominant (van
der Veer (2005)). In this process they conceived of “Utopias” – desirable, but practically
unachievable future states – at each vertex of the triangle. This enabled them to explore
scenarios on a “two-win, one-loss” basis. The Royal Dutch/Shell trilemma approach is a
powerful means of exploring hypothetical futures. However it suffers two notable
deficiencies: first, it is highly reductionist, attempting to characterise an enormously
complex system as a single trilemma representing the interaction between just three
forces; and second, there is no reason why future states necessarily need be utopic –
when a particular force dominates, the outcome at a triangle vertex can conceivably be
either desirable or undesirable – either a utopia or a “dystopia”. To address the first of
these deficiencies, the approach taken here is to further develop the concept of the
trilemma, not as a system, but rather as a system element. And to address the second, the
relationship between the three forces is explored so as to identify both utopic and dystopic
responses to the hypothetical disturbance and a plausible outcome which is reflective of
some balance between the two (see Figures 6.5 and 6.6 opposite). (It should be noted
that this approach contains a simplifying assumption: it assumes that the three forces
represented in the trilemma are independent and that linear combinations of the forces do
not give rise to more extreme positions than those represented at the vertices of the
triangle in which a single force dominates. Furthermore, it does not take into account
relationships that might exist between trilemmas. It would be expected that these
assumptions be considered during the critique process and used to develop relationships
in approaches such as MCDA which attempt to represent the relationships of
“preferential independence”, whereby a preference for an issue represented by one
trilemma can be expressed independently of how one might feel about other issues
represented by other trilemmas)
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Major influences or forces which are acting in opposing directions are identified.
Most complex problems will have no more than 20 to 40 major influences.• most problems can be represented
by 4 to 8 trilemmas
Major influences or forces which are acting in opposing directions are identified.
Most complex problems will have no more than 20 to 40 major influences.• most problems can be represented
by 4 to 8 trilemmas
Figure 6.5 – Constructing a trilemma: identifying opposing forces
It should be noted that neither Rodrik’s work nor the Royal Dutch/Shell approach make
specific reference to psychological theory. However, using the trilemma as a device to
represent system elements aligns well with Kelly’s personal construct theory (principle
C3) – holding one force constant and exploring the effect of varying the other two is
analogous to Kelly’s concept of the dichotomous construct. It also gives a sense of the
strength of the relationship between the force which is held constant and the other two,
analogous to the scalar representation in Kelly’s theory.
They are then refined by holding one force constantand exploring the effect of varying the other two.
Utopia Dystopia
Plausibleoutcome
Plausibleoutcome
Utopia
DystopiaPlausibleoutcome
PlausibleoutcomeUtopia
Dystopia
Plausibleoutcome
Plausibleoutcome
“Plausible Outcomes” are imagined based on each force being dominant
They are initially identifiedby assuming one force is dominant and imagining a plausible set of outcomes
• Utopias are an “ideal”outcome when a force prevails
• Dystopias are the “nightmare” outcome
Figure 6.6 – Constructing a trilemma: identifying utopias, dystopias and plausible outcomes
They are then refined by holding one force constantand exploring the effect of varying the other two.
Utopia Dystopia
Plausibleoutcome
Plausibleoutcome
Utopia
DystopiaPlausibleoutcome
PlausibleoutcomeUtopia
Dystopia
Plausibleoutcome
Plausibleoutcome
“Plausible Outcomes” are imagined based on each force being dominant
They are initially identifiedby assuming one force is dominant and imagining a plausible set of outcomes
• Utopias are an “ideal”outcome when a force prevails
• Dystopias are the “nightmare” outcome
They are then refined by holding one force constantand exploring the effect of varying the other two.
Utopia Dystopia
Plausibleoutcome
Plausibleoutcome
Utopia
DystopiaPlausibleoutcome
PlausibleoutcomeUtopia
Dystopia
Plausibleoutcome
Plausibleoutcome
Utopia Dystopia
Plausibleoutcome
Plausibleoutcome
Utopia Dystopia
Plausibleoutcome
Plausibleoutcome
Utopia
DystopiaPlausibleoutcome
Plausibleoutcome
Utopia
DystopiaPlausibleoutcome
PlausibleoutcomeUtopia
Dystopia
Plausibleoutcome
Plausibleoutcome
Utopia
Dystopia
Plausibleoutcome
Plausibleoutcome
“Plausible Outcomes” are imagined based on each force being dominant
They are initially identifiedby assuming one force is dominant and imagining a plausible set of outcomes
• Utopias are an “ideal”outcome when a force prevails
• Dystopias are the “nightmare” outcome
Figure 6.6 – Constructing a trilemma: identifying utopias, dystopias and plausible outcomes
Using trilemmas to characterise a system
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The process for developing the system map using trilemmas as the means for
representing system elements is this:
• identify the forces at play in the system – usually these can be thought of as
“force-pairs” which are represented as dichotomous constructs and these
correspond to aspects of the problem which are in tension. One way to identify
the dichotomies is through the well-established practice of brainstorming
(Brazzard (1989), Bryson and Ackermann (2004)). In some cases, the nature of
the dichotomies will be apparent from the problem itself; in others, they are
constructed just by taking one problem feature and considering its opposite. (See
example in Figure 6.7.) Note that in the example, in some instances, the
dichotomous constructs represented are not forces but symptoms representing
behaviours in response to the forces. This suggests that there is potential to
develop the trilemma device at two levels in terms of means and ends. On one
hand, the trilemma might be thought of as the means by which the system’s
response can be evaluated. On the other, the system states represented at the
vertices of the trilemma indicate the end, that it, a plausible system response.
Figure 6.7 – Brainstorm dichotomous constructs and identify underlying force pairs.
Health Risk ↔ Recycling
Change Acceptance ↔ Change Aversion
High Rainfall ↔ Low Rainfall Enabling Technology ↔ Politics
Public Ownership ↔ Private Ownership
Community Concern ↔ Technological Influence
Greenfield ↔ BrownfieldFree Market ↔ Monopoly
Political Self-Interest ↔ Community Concern
Centralised ↔ Decentralised
Regulation ↔ Monopoly
Media Influence ↔ Politics
Vested Interests ↔ Community Engagement
Opinion Leaders ↔ Community EngagementOpinion Leaders ↔ Politics
Free-market Capitalism ↔ Big Government
Vested Interests ↔ Community Interests
Economics ↔ Environmental Concern
Legal Activism ↔ Politics
Legal Activism ↔ Corporate Governance
Population Growth ↔ Population DeclineHigh Energy Cost ↔ Low Energy Cost
Identify the dichotomies…
Political Self-Interest ↔Technological Influence
Figure 6.7 – Brainstorm dichotomous constructs and identify underlying force pairs.
Health Risk ↔ Recycling
Change Acceptance ↔ Change Aversion
High Rainfall ↔ Low Rainfall Enabling Technology ↔ Politics
Public Ownership ↔ Private Ownership
Community Concern ↔ Technological Influence
Greenfield ↔ BrownfieldFree Market ↔ Monopoly
Political Self-Interest ↔ Community Concern
Centralised ↔ Decentralised
Regulation ↔ Monopoly
Media Influence ↔ Politics
Vested Interests ↔ Community Engagement
Opinion Leaders ↔ Community EngagementOpinion Leaders ↔ Politics
Free-market Capitalism ↔ Big Government
Vested Interests ↔ Community Interests
Economics ↔ Environmental Concern
Legal Activism ↔ Politics
Legal Activism ↔ Corporate Governance
Population Growth ↔ Population DeclineHigh Energy Cost ↔ Low Energy Cost
Identify the dichotomies…
Political Self-Interest ↔Technological InfluenceHealth Risk ↔ Recycling
Change Acceptance ↔ Change Aversion
High Rainfall ↔ Low Rainfall Enabling Technology ↔ Politics
Public Ownership ↔ Private Ownership
Community Concern ↔ Technological Influence
Greenfield ↔ BrownfieldFree Market ↔ Monopoly
Political Self-Interest ↔ Community Concern
Centralised ↔ Decentralised
Regulation ↔ Monopoly
Media Influence ↔ Politics
Vested Interests ↔ Community Engagement
Opinion Leaders ↔ Community EngagementOpinion Leaders ↔ Politics
Free-market Capitalism ↔ Big Government
Vested Interests ↔ Community Interests
Economics ↔ Environmental Concern
Legal Activism ↔ Politics
Legal Activism ↔ Corporate Governance
Population Growth ↔ Population DeclineHigh Energy Cost ↔ Low Energy Cost
Identify the dichotomies…
Political Self-Interest ↔Technological Influence
• identify force-pairs which might be able to be arranged as true trilemmas. A “true
trilemma” is one where the three forces exist independently but interact and have
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a natural tendency oppose one another (this step is intended to strengthen the
simplifying assumption noted earlier);
• in turn, hold each force constant and imagine and describe a utopia and dystopia
which would prevail if the remaining force-pair under consideration were to
prevail (this is analogous to the personal construct as hypothesised by Kelly
(1955));
• characterise the “As-Is” system by developing the sets or maps of five to eight
trilemmas and identifying the relationships that might exist between the
trilemmas themselves. Some system elements may be found to contain greater
complexity than can be represented in several trilemmas and these should be
treated as subsystems being mapped at a greater level of detail. As these
relationships are developed, they can be used to assist in constructing objectives
hierarchies in MCDA;
• engage with the group of representatives from the domain of interests to critique
the system model, with particular emphasis on understanding element
relationships. This includes undertaking the discourse necessary to establish and
criticise system boundaries. It is important to note that development of the
system model requires a substantial understanding of the problem-structuring
process itself. It consists of a set of cognitive maps representing problem
information as trilemmas and with some of the relationships which exist between
trilemmas also identified. Similar maps of subsystems might also have to be
developed. Hence, for the critique of the system model to be valuable, at this
early stage, it needs to be confined to project team members who are well-versed
in the methodology. Typically, this group of people would be expected to be the
facilitators who will ultimately lead the broader critique of the system model in
the wider community.
One means of establishing the sets of trilemmas representing the total system is to
consider the various dimensions of the problem, so that pieces of information can be
grouped according to their similarity and criticised. For example, van der Heijden (1996),
in his approach to scenario analysis, identifies such structural issues as information,
resources, cultural, legislation, political, financial, technology, ecology, physics, policy,
thresholds, power distribution, territorial, regulation, agreements, and demography.
These are also encountered in other approaches to corporate strategic planning and
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sometimes referred to as “environmental scanning” (see, for example, Kotler (1991),
Macmillan and Tampoe (2000)). In some instances, this deliberation will need to be
made iteratively, as a problem dimensions are descriptors of system response and insight
will emerge from considering the system response to disturbances. Developing the
model in this way recognises that the problem is a dynamic system. One powerful aspect
of the approach is that the richness of the model develops as the response of the system to the
disturbance is tested repeatedly.
Experience in the case study suggests that even very complex systems can be
comprehensively represented by considering thirty to forty force-pairs and that these can
then be represented in five to eight trilemmas. Important aspects of the problem can be
considered in further depth when represented as subsystems.
The sets of trilemmas can then be explored, both as individual system elements, and also
in the way in which they interrelate to represent the total system. Trilemmas identified at
a high-level may be further investigated as subsystems and explored in more depth if this
is useful in forming a more comprehensive representation of the problem situation.
This approach of qualitatively representing a complex socio-eco-system has a number of
benefits:
• it allows representation of a large amount of information using relatively simple
schema – this meets challenge of having only five to eight units of concentration,
so the mind can keep focused, consistent with principle C2;
• the trilemma device is consistent with dichotomous constructs (principle C3);
• each trilemma is effectively “self-bounded” – description of any situation in
relation to the three trilemma forces will be contained within the triangle, thus
facilitating boundary critique of the system as a whole;
• by engaging a wide range of stakeholder perspectives in identifying the forces and
creating the trilemmas, there is both engagement of stakeholders and the creation
of a common language to describe the problem;
• it is relatively straightforward to identify utopic/dystopic positions by holding
one force constant and exploring the effect of varying the other two (principle
C3).
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6.3.5.2 Step 2. Definition of hypothetical system disturbances This step identifies one or more hypothetical disturbances to be applied to the system in
order to allow qualitative exploration of the way in which the system might respond.
Issues to consider in defining such a disturbance are these:
• Whether the origin of the disturbance should be external or internal to the
system. In the initial analysis, only one disturbance is considered. Whether the
hypothetical disturbance should be internal or external should be determined by
the most likely risk: is it some external shock to the system (for example, in the
case study in Chapter 7, a metropolitan water system, permanent climate change
was considered); or is it some internal disturbance (for example, collapse of
critical infrastructure, such as a major dam might be plausible);
• for reasons of practicality, the type of disturbance will probably be either a step
change or a ramp change in external conditions85;
• the hypothetical disturbance should be relate to some major aspect of uncertainty,
which might be expected to have a significant impact on the system stability;
• trilemma analysis can be helpful in conceiving system disturbances;
• a simple disturbance will be more easily analysed than a complex one.
Once the disturbance has been determined, it should be described clearly and concisely
in words. It is important that the description of the system disturbance is simple and
readily understood.
6.3.5.3 Step 3. Imagine plausible system responses – scenario analysis In this step, plausible responses of the “As-Is” system to the hypothetical disturbances
are considered. At this stage, the system response is explored qualitatively, identifying
areas where quantitative analysis might be useful. At least two future system states are
considered in this step. On one hand, is the likely response of the “As-Is” system to the
disturbance; on the other is a desirable response to the disturbance. In the first instance, the
most likely response of the “As-Is” system is explored, assuming that no structural
change is made to the “As-Is” system as a direct consequence of the disturbance itself.
In the second case, consideration is given to what structural changes to the “As-Is”
system would be required for it to be able to respond and to deliver the “Desirable
85 Systems analysis theory of mathematically defined systems can treat many different types of disturbance
but the qualitative approaches being used here suggest some practical limitation on the type of disturbance considered.
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Future” response. In the case study in Chapter 7, only these two scenarios were
considered. However, depending on the nature of the problem and the time horizon
under consideration, further scenarios may be required to populate the range of
uncertainty in the problem. This is considered when preparing for the “scenario
analysis” (see below). The “As-Is” system is described using a combination of cognitive
maps, depictions, symbols, diagrams, and words to capture the richness of the qualitative
analysis86.
Then a different trajectory is imagined for the system towards a “Desirable Future”,
identifying differences in strategic image required for the “Desirable Future” to become
achievable. As with the “As-Is” system description, a rich representation of the
“Desirable Future” system state is captured using cognitive maps, depictions, diagrams,
symbols and words.
To explore the potential effects of the hypothetical disturbance, a well-established
technique (or, rather, family of techniques), which has evolved over the last 50 years or
so (Miller and Waller (2003)), called “scenario analysis” is used87. To determine which
specific technique to use, it is useful to consider what the ultimate objective of the
scenario analysis is (whether it is explorative or descriptive); the process to be used
(whether it is to be intuitive or analytical); and the content (whether it is to be simple or
complex) (van Notten et al. (2003)). (Olson (1994), Godet (2000), Krueger et al. (2001),
Raskin and Banuri (2002), and Swart et al. (2004)) provide examples of the application of
scenario analysis to inform strategy, using scenario analysis in relation to complex social
problems, and integrating the approach into consideration of the complex problems of
sustainability.)
The general process for scenario analysis can be described thus:
86 For example, in a typical working group, notes might be recorded on flip chart paper, complemented by
graphs, sketches, diagrams, and even photographs. Together these can be used to convey important information from the analysis to others working on the project.
87 Scenario analysis originated as part of the Manhattan project when modelling was done to determine the strategic effects of the atomic bomb and, after the war, was extended into strategic thinking as the Cold War commenced. A number of consulting firms introduced the technique to large corporations and over the next twenty years it became well accepted as a tool for corporate strategic planning. In the 1970s, when the first oil shock occurred, Royal Dutch/Shell extended the concept to look out over much greater time-frames, modelling the impact of variables such as oil prices and energy supply/demand as part of their strategic analysis.
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• Define the time horizon over which the response of the system to the
hypothetical disturbance will be considered. As noted above, with most Type 3
problems, 10 to 50 years is not uncommon. As the timeframe lengthens, the
range of uncertainty increases and each scenario-set requires the development of
a larger number of scenarios to consider in order to cover the range of
uncertainty. (See Figure 6.8) The consideration of uncertainty is an important
issue in determining both the nature and number of scenarios explored.
Uncertainty in relation to Type 3 problems can derive from a number of sources:
it may be due to incomplete information, natural variability, differences in the
way in which the problem is described or characterised, or it may arise from
differences in preference and values (Morgan and Henrion (1990a)).
Ran
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Scen
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Scenario analysis
Figure 6.8 – Developing scenarios to span the range of uncertainty
Scenario analysis
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Figure 6.8 – Developing scenarios to span the range of uncertainty • Representation of the domain of interests. It is important that all interests be
represented in the analysis, so that the ultimate scenario-sets have access to all
perspectives which need to be taken into account. The issues relating to
uncertainty noted in the previous point are also likely to influence the selection of
this representation.
• Identify those issues which are “knowable”. Extrapolate from past trends
and attempt to ascribe the uncertainty related to each. Where possible, attempt
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to identify qualitative relationships between system elements and the way in
which these will respond to the disturbances.
• Define the boundary scenario themes. This may be simply putting all positive
responses into one scenario and all negative ones into another – the essential
point is to attempt to conceive of boundary scenarios for the range of
uncertainty. It is important to note is that the uncertainty dealt with here is of a
“structural” nature and is characteristic of the system. That is, future events are
not particularly influenced by past events and multiple interpretations are possible
both to explain and to predict behaviour of the system. Identification of the
boundary scenario themes (and, indeed, those in between – as depicted in Figure
6.8) is intended to appeal to our capacity for intuitive thought (as discussed
Chapter 5, section 5.2.1). That is, it is intended to stimulate recognition of
patterns in the system and consideration of how they might develop (van der
Heijden (1996)).
• Cover the range of uncertainty by developing additional scenarios as necessary
to fill the future landscape.
• Analyse for plausibility and internal consistency. Once initial scenarios have
been drafted they should be refined and tested for internal consistency and to
assure that any irrationality is identified and noted. Whereas the definition and
development of scenarios is largely intuitive, the consideration of their plausibility
and internal consistency is analytical. However, where beliefs and values are
involved, inevitably there will be irreconcilable differences identified. Skilled
facilitators are needed to mediate in these discussions to ensure that imbalances
in power are considered and that coercive elements within the domain of
interests are restrained from having undue influence. The starting requirement is
that the working group is structured in such a way (both in terms of hierarchy
and representation) that power imbalances are minimised. This can be
challenging indeed where political or commercial interests dominate the
structuring of the working group.
• Seek opportunities to strengthen scenarios. Note where gathering objective
information would fill gaps in knowledge regarding the problem.
• Convert scenarios into “storyboards”. Using cognitive maps, symbols, and
depictions, to develop representations of each scenario-set, drawing these
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together with a number of narratives prepared from different stakeholder
perspectives. In some cases, facilitators might be required to prepare initial
narratives as a starting point for critique and discussion. This is explored in
terms of the case study in Chapter 7.
• Explore opportunities to develop quantitative models. Note areas where
quantitative modelling (for example, economic modelling, values preference
modelling, etc) might be used to gain further insight into the scenario-sets.
• Test the final scenario-sets. Ensure that all participants in the analysis have
had the opportunity to critically examine the scenarios and to contribute to
additions or changes.
The principal advantages of this approach relative to the alternative quantitative
forecasting approaches are these:
• it extends consideration of uncertainty beyond the limitations of quantitative
forecasting approaches by taking into account the structural uncertainty in the
problem situation. These derive from incomplete information, linguistic
differences in problem description, and differences in preferences and values
(Morgan and Henrion (1990a), van der Heijden (1996)). As noted in Chapter 5,
significant psychological biases appear which may result in either under-
estimating or over-estimating probabilities, particularly where intuitive judgement
is involved. That is not to say that quantitative methods for representing
uncertainty should be excluded. Rather, it is suggested that the construction of a
set of scenarios to span a range of problem uncertainty allows insight into the
problem to be developed beyond that represented by purely quantitative
methods. This is particularly so in areas where there is no basis for assignment of
probabilities;
• presentation of a number of scenarios, using storyboard techniques, is a good
way to engage stakeholders by avoiding often complex analytical approaches;
• constructing a number of stories from different stakeholder perspectives allows
representation and engagement of a diverse range of stakeholder views and
values;
• it allows deconstruction of highly complex future environments into a number of
simpler and more readily understood parts. (It should be noted that this is not
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necessarily reductionist, rather it allows system elements and subsystems to be
explored and their interrelationships examined.);
• a number of variables may be changed simultaneously. This may be complex in
formal analytical methodologies;
• because initial definition of the starting point for scenarios is largely subjective,
the beliefs, values, and biases of participants become embedded in the scenarios.
This is by no means undesirable but care needs to be taken to identify these and
to retain their transparency as the scenarios are developed. These values-laden
scenarios are useful in developing complete narratives, which represent the
problem system from a range of different worldviews. Again, this is explored in
Chapter 7.
When undertaking scenario analysis, there are a number of important aspects to keep in
mind about the technique. With infrastructure projects, where there is large capital
expenditure or potentially significant social and environmental impact, the analysis looks
a long way into the future (usually from 10 to 50 years or more). Because of the inherent
uncertainty this implies, scenarios cannot be considered to be forecasts, as there is only a
relatively small likelihood of any one scenario actually coming true. Hence, scenarios
should not be thought of as being predictions. Rather, they should be considered to be
outcomes which could emerge from considering a set of plausible assumptions and
parameters in relation to the existing system state. The technique is at its most powerful
when a number of “story-lines” or narratives can be developed which are internally self-
consistent and can be contrasted with one another. The most extreme positions define
the boundary conditions and the intermediate scenarios propose situations which are
plausible outcomes. Consideration and evaluation of these allow strategic plans to be
developed in such a way as to allow greater flexibility in the way in which resources are
deployed (Science and Technology Policy Research Unit (2002)).
Because development of the scenarios is a mixture of both rational thought and intuition,
with disparate views and values, reaching consensus can be difficult or even impossible.
There needs to be a recognition of this difficulty and a commitment among participants
to follow a defensible process. This is required to arrive at a comprehensive range of
story-lines. Furthermore, it should be emphasised that this approach is to identify issues
and their interrelationships through a widely accessible structuring of problem
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information, the aim being to inform decision-makers about important issues. This issue
is by no means trivial – it requires skilled facilitation and a comprehensive understanding
of the entire problem-structuring approach and, preferably subsequent analytical
approaches such as MCDA.
It is not intended nor is it expected that the approach described here will lead directly to
solutions to the problem. Rather, issues will be identified which will need further
exploration and deeper analysis, using formalised analytical techniques such as preference
modelling, economic modelling, multi-criteria modelling, and so on. The value of the
approach developed in this dissertation is that it strengthens these techniques through
developing a problem structure which takes into account the dynamic nature of the
problem system to plausible, hypothetical disturbances. This allows greater confidence
to be established in parameters utilised in formalised modelling techniques and greater
insight into the broad range of hard, soft, and values-laden information. This approach
is expected to be of particular use in techniques such as MCDA which attempt to
develop comprehensive mathematical representations of incommensurate aspects of the
problem through development of mathematical functions representing values and
preferences and then to facilitate their comparison through techniques such as out
ranking methods.
In the problem-structuring approach developed here, the application of scenario analysis,
while adhering to the general principles outlined above, has been modified slightly as
shown in Figure 6.9 (overleaf). The specific steps used in this approach are;
• Start with the “As-Is” system, and consider qualitatively the way in which this
system might respond to the hypothetical disturbance (or disturbances)
conceived of in Step 2.
• Imagine a plausible future scenario-set, representing the response of the “As-Is”
system to the disturbance, assuming no fundamental change to the “As-Is”
system characteristics. This is called the “Likely Future” scenario-set.
• Imagine a second plausible scenario-set representing a “Desirable Future”, a
response to the disturbance which is desired but which the “As-Is” system is
incapable of delivering. Development of this scenario-sets requires critique of
the system elements, subsystems and their interrelationships to determine the
challenges in arriving at utopic positions.
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• Once the “Desirable Future” scenario-set has been conceived, it can be
compared with the “Likely Future” scenario-set across the problem dimensions.
This allows identification of issues across the breadth of the problem as
characterised by the “As-Is” system which need to be addressed if the “Desirable
Future” scenario-set is to become achievable.
What Strategies are needed to change the Trajectory?
• Describe these in terms of the problem dimensions
Characterise the “As Is”system…
• Establish and critique boundaries
• Describe the history and “Values” which shaped the system
• Identify the forces at work• Formulate and critique the Trilemmas
• Characterise the sub-system
Disturbance
Describe the two scenarios:• Words, diagrams, pictures, etc
Interpret the effect in terms of the Trilemmas
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Figure 6.9 – The system model underlying the problem-structuring approach.
What Strategies are needed to change the Trajectory?
• Describe these in terms of the problem dimensions
Characterise the “As Is”system…
• Establish and critique boundaries
• Describe the history and “Values” which shaped the system
• Identify the forces at work• Formulate and critique the Trilemmas
• Characterise the sub-system
Disturbance
Describe the two scenarios:• Words, diagrams, pictures, etc
Interpret the effect in terms of the Trilemmas
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What Strategies are needed to change the Trajectory?
• Describe these in terms of the problem dimensions
Characterise the “As Is”system…
• Establish and critique boundaries
• Describe the history and “Values” which shaped the system
• Identify the forces at work• Formulate and critique the Trilemmas
• Characterise the sub-system
Disturbance
Describe the two scenarios:• Words, diagrams, pictures, etc
Interpret the effect in terms of the Trilemmas
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Figure 6.9 – The system model underlying the problem-structuring approach.
This important step in the process is the starting point to identify options which will
be available finally to the decision-makers in formulating policy and designing
interventions to achieve desired real-world system responses. In identifying the
characteristics of the system that need to be changed, it is almost inevitable that
trade-offs will be required in different system elements or subsystems. In order for
these to be included in quantitative analyses in the latter stages of decision-making, it
is important that comprehensive documentation is developed. This should
comprehensively record the problem-structuring approach (including participants,
discussion notes, meeting minutes, depictions of the system model, narratives, and
conclusions). This document should be widely disseminated among the domain of
interests, so it can be referred to as policy formulation and decision-making takes
place to ensure that all aspects of the problem are properly addressed. Such a
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document can be instrumental in removing power imbalances, particularly if it is
disseminated through he media.
6.3.5.4 Step 4. Multi-dimensional critique – “straw proposal” cognitive maps and narratives
Now, critique of the system can be undertaken. The “Likely Future” and “Desirable
Future” scenario-sets are examined as outlined below, capturing these in several word-
rich narratives representative of the range of perspectives of the entire domain of
interests. By referring to the “As-Is”, “Likely Future” and “Desirable Future” trilemma
system maps, a comprehensive narrative or storyline can be prepared as a “straw-
proposal” for discussion and critique by stakeholders88. Groups of stakeholders with
expertise or interest in a particular problem dimension can be assembled to examine
relevant aspects of the problem, or “multi-dimensional” groups can consider
relationships between information across problem dimensions.
Multidimensional critique is important in two respects. First, it draws together and
considers critically the response of the system to the hypothetical disturbances; and
second, it summarises the interpretation of the system response from a range of
perspectives representative of the domain of interests. This combination of the
description of system response from a number of differing perspectives ultimately allows
a greater engagement by the domain of interests, because a range of values and belief
systems is represented. Cognitive mapping of “landmark” system elements facilitates the
identification of important aspects of the problem, together with relationships which can
be identified between them. This can take place across multiple problem dimensions.
This is the starting point for developing narratives which allow identification of and
reflection upon gaps in knowledge regarding the problem and system element
interrelationships. Also, the various values, beliefs, and interests of the representatives of
problem stakeholders emerge from the structure of the narratives, consistent with
principle C6.
The process for multidimensional critique is this:
88 “Straw-man proposal” or “straw proposal” is a term commonly used in business analysis as a starting
point proposal for developing business strategy. Anecdotally, its origin is an effigy of a man made of straw used in jousting practice. Hence, it is a proposal which is to be knocked down, picked up, reshaped, and knocked down again in order to shape and refine thinking.
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• a steering group prepares an initial draft of the “As-Is” and “Desirable Future”
system responses to the hypothetical disturbance. This is a set of draft cognitive
maps and a brief, “straw-proposal” narrative as a starting point for group
discussion.
• Small groups (with from four to eight members) are assembled representing
specific areas of expertise in particular problem dimensions or chosen as
representatives of various parts of the domain of interests. Using the straw-
proposal as a starting point, these groups develop narratives (together with
supporting information such as diagrams, pictures, symbols, etc), which are
descriptive of both the “As-Is” and “Desirable Future” system responses.
• As part of this process, each narrative is deconstructed to determine the extent to
which the original problem information is represented in the narrative. This is an
important step in controlling the quality of the problem-structuring process. It
should be noted that the intention is not to restrict or discourage values-laden
interpretation of problem information, rather it is to ensure that as much
problem information as possible is represented and interpreted in the narrative
context.
• Once the draft narratives have been completed by the working groups, they are
circulated for consideration and discussion to the full working group engaged in
structuring the problem.
• The full working group has the opportunity to contribute to and to criticise the
draft narratives, with an iterative process of review and re-work taking place until
there is consensus that the sets of narratives fairly represent the diverse view of
the working group regarding the problem structure. That is that they contain an
agreed set of problem information. Because the full working group has been selected to
represent the entire domain of interests, the resultant narratives would be
expected to contain a fair representation of the entire range interests present in
the problem.
(A cognitive mapping technique has been developed to facilitate recording information
from the system critique. This is based on utilising visual or word-based representations
of issues identified in the critique, highlighting major relationships (called “conjugation
points”), both within and between problem dimensions. This allows large amounts of
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information to be recorded about the problem and not lost through any tendency to
reductionism. This technique is outlined in Appendix 6.1.)
6.3.5.5 Step 5. Critical discourse – community engagement The final step in the problem-structuring process is to use the information prepared in
the preceding four steps to engage the broader community within the domain of interests
with the issues identified in the problem-structuring exercise. Depending on the nature
of the specific Type 3 problem under consideration, this may be as simple as identifying a
particular intervention (for example, based on technological requirements). On the other
hand, it may extend all the way through to extensive media engagement, utilising formal,
paid consulting resources to ensure engagement of all elements of the domain of
interests (as established in the boundary critique), with the explicit intention of
influencing policy direction. Typical avenues for community engagement include
participating in public meetings and forums, the sponsoring of specialised conferences to
consider the problem situation, testifying before formal hearings into the problem
situation, and engagement with mass media (including utilisation of professional public
relations resource). The difference between the approach taken here and typical public
consultation approaches is that representatives from the domain of interests are engaged
at the very first stages of problem definition. The community is presented with a
comprehensive, wide-ranging brief of information representing the full diversity of the
interests represented in the problem. Because this should take place well before a final
decision on solution to the problem is required, there is maximum opportunity for
engagement. This contrasts with more established community consultation approaches
which are often little more than presentation of a set of two or three options at the
conclusion of often secretive investigative and design work, which represents a single
powerful or influential worldview.
It is this last point which leads to a further important application of the problem-
structuring approach to be developed in the next section.
6.3.6 Retrospective critique Thus far, the approach has been prospective – the assumption is that a problem has been
recognised, that there is a determination that it be characterised and resolved, and that a
process is under way to resolve it (as outlined in Table 6.1). However, many Type 3
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problems are not new. Rather they have been either neglected or considered to be too
hard by successive groups of decision-makers. Ultimately, some crisis results in there
being no alternative but to take action. Often in these situations analytical work is done
in “crisis mode”, with a solution being identified with incomplete analysis and presented
to the domain of interests with little time for consultation or consideration. In these
circumstances the problem-structuring approach developed here can be utilised
retrospectively to critique potential solutions, which may have been forced to
implementation without adequate policy consideration. Representing the problem
structure as described here allows identification of important problem information,
which can be used as the basis for critique of other work on the problem system which
may have been already completed. This is explored as part of the case study in Chapter
7.
The approach developed here to review and critique existing policy or plans has been
developed in two strands. First, is a normative approach, whereby important system
information is determined through analysis of the trilemmas system maps and
multidimensional cognitive maps developed during the problem-structuring exercise.
This enables “landmark” features of the problem system to be identified across all the
problem dimensions which, together, form the system model. It is then a
straightforward task to review existing policy and planning documents against a checklist
of these landmark features to determine whether they have been adequately considered in
framing the policy or preparing a strategic plan. Second, is a more holistic, intuitive
approach: interrogating the narratives prepared during the problem-structuring exercise
and conducting a critique of the policy or strategic planning information, informed by the
worldviews and perspectives of the narrative writers. In the case study in Chapter 7,
both normative and holistic approaches are demonstrated in a review of a major, state
infrastructure planning process.
6.4 Research approach As noted in Chapter 1 (section 1.6.2), where the distinction was drawn between the
characteristics of quantitative and qualitative research, the research approach taken here
sits within the postpositivist and critical theorist positions, that is, it is based upon the
qualitative research paradigm.
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The realist ontology argued in Chapter 4 (principle P1, Chapter 4) clearly precludes a
constructivist approach and also rejects the naive realism of positivism, arguing not for a
mechanistic ontology, but rather, for a holistic, systems approach (principle P4). But
there is also a recognition that social, political, institutional, economic, ecological, and
technological history has shaped the way in which the problems of sustainability have
been dealt with and are expected to emerge in the 21st century. Hence, the approach
must position extend into the realm of critical theory. In this dissertation, a clear
distinction is drawn between the real-world things and phenomena of World One, and
our World Two and World Three representations of them. The discussion of
philosophical principles and cognitive psychology argued that limitations and flaws in our
representations of World One phenomena are primarily a result of human cognitive
constraints, rather than being an ontological feature of the universe. That is, they are
epistemological, rather than being ontological.
A important aim of this research is to develop models and devices which can contribute
to our capacity to develop our knowledge and understanding of the real world in which
we find ourselves. But in addition, there is a central intention of this work to influence
and change the way in which engineering is practised – that is, it is emancipatory in
nature, intending to free engineers from a dated paradigm. The widely held engineering
paradigm, which evolved in the first 200 years of engineering practice, has been
extensively criticised and a new set of principles to deal with the complexity and
challenges of sustainability in the 21st century has been proposed. Again, this places this
research within the domain of the critical theorist and emphasises the importance of
basing criticism on a practical representation of the problem. It also calls for a better,
more complete way of structuring Type 3 complex problems so as to improve our
understanding of them and, most importantly, to contribute to their solution. The
theoretical consideration of cognitive processes, particularly the importance of cognitive
mapping and narrative, identified these two processes as being primary means by which
humans place their experiences in both spatial and temporal context. In addition,
humans utilise narrative as an important cognitive mechanism to resolve inconsistencies
and to fill in gaps in our understanding. This further emphasises the importance and
influence of critical theory on this research. Turning to axiology, values and ethics are
considered to be intrinsic to the resolution of problems of the sustainability discourse. It
is argued here that engineering cannot be considered to be a values-free discipline. The
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ethical position of engineers in considering and taking into account the moral standing of
all interests in the Type 3 problem is a key feature of the approach being advocated here.
Furthermore, it is expected that engineers who embrace the change to the paradigm of
engineering practice proposed here will become active advocates of the need for such a
change.
The important point to draw from this discussion is that the research approach in this
thesis is primarily critical and qualitative, rather than quantitative in its nature. The
intention is to stimulate and advocate change in engineering practice, so that the
problems of sustainability can be resolved, not simply to further inform the decision-
making and policy formulation of others.
6.5 The case study The qualitative, critical research methodology discussed above is used as the foundation
for the case study in the following chapter. The problem-structuring devices developed
in Chapter 6 have been applied to a Type 3 problem relating to the water system of
Sydney, Australia, a large metropolis with some particularly challenging contemporary
issues regarding its water system. The approach taken was one of “action research”, with
the intention that the problem-structuring methodology be utilised, tested and criticised
to determine its robustness and its value in structuring the problem for greater
engagement of the community interests and as intervention to make a contribution to
resolution of the problem.
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Appendix 6.1 – Multi-Dimensional Conjugate Cognitive Mapping (MDCCM)
Cognitive mapping techniques are inherently reductionist – they attempt to represent
complex systems visually, so problems can be represented in pieces small enough for the
mind to be able to comprehend them effectively. Consequently, cognitive maps generally
suffer from one (or both) of two deficiencies: either they become so large and complex
that they defeat their purpose, or they oversimplify the situation, losing important
information. Bougon (1992) recognised this and created the concept of congregate
cognitive mapping. The technique described below builds on the approach initiated by
Bougon, significantly improving the way in which information is managed and presented.
Multi-Dimensional Conjugate cognitive Mapping (MDCCM) is a technique developed to
enhance the problem-structuring technique developed here which aims to simplify
representation of the problem without loss of information. It allows information to be
“conjugated” according to the similarities defined by Verma (1998), or by other
groupings which might be useful.
Building a MDCCM consists of the following steps:
• identifying all relevant aspects of the problem, describing these as words, phrases
or sentences (or diagrams, sketches, images, etc);
• these may then be grouped according to one or other of their similarities;
• “conjugation points”, which link groups are then added;
• additional problem dimensions possibly other similarities, issues, approaches etc
can be identified on transparent “layers” which can be added, either temporarily
or permanently, to facilitate decision-making or judgement.
The MDCCM can be prepared on paper, with transparent layers being added, or using
any one of a number of computer programs capable of displaying a mixture of text,
graphics and layers (or dimensions).
The technique has a number of advantages over other cognitive mapping approaches:
• it becomes practical to handle large amounts of data, particularly if computer
software is used;
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• dimensions can be added or removed as layers, depending on the level of
complexity to be represented and the degree of focus desired;
• all important, relevant information can be recorded and retrieved as needed;
• multiple perspectives and worldviews can be accommodated using different
dimensions. This can be a useful mechanism for finding differences, but more
importantly, the common ground where there are widely differing perspectives or
worldviews can be identified.
A partially populated MDCCM is represented below to demonstrate application of the
technique to the subject considered in this dissertation – see Figures 6.10 to 6.13.
Conjugate Cognitive Map
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Figure 6.10 – Conjugate cognitive map framework
Conjugate Cognitive Map
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Figure 6.10 – Conjugate cognitive map framework As many aspects of the problem as possible are identified and represented on the map.
These are grouped into broad categories to give a visual representation of the problem
and principal conjugation points are identified and added to the map as shown in Figure
6.11.
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SustainabilityEconomic
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Figure 6.11 – Populating the map with issues and identifying important conjugation points – Issues dimension
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Figure 6.11 – Populating the map with issues and identifying important conjugation points – Issues dimension These may be layered also, depending on the type of problem being addressed.
Relationships are identified and further dimensions are added (Figures 6.12 and 6.13),
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Figure 6.12 –Identifying important analytical relationships – Approaches dimension
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Complex Problems
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Figure 6.13 –Identifying important cognitive relationships – Approaches dimension
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Figure 6.13 –Identifying important cognitive relationships – Approaches dimension representing different similarities or other aspects of the problem which are important.
As many layers as needed may be added to represent the required richness of information
– see Figure 6.14.
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SocialEcological Moral
Aesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Issues
ObjectiveMessy problems
RiskJudgment
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
MoralsEpistemology
Politics
Philosophy ofScience
Beliefs
Behaviours
SolutionsAgreements
SustainabilityEconomic
SocialEcological Moral
AestheticSustainability
EconomicSocial
Ecological MoralAesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social Context
Social ContextIndividual ContextIndividual Context
WorldviewsWorldviews
CognitionCognition
PhilosophyPhilosophy
Conjugate Cognitive Map Issues
ObjectiveMessy problems
RiskJudgment
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
MoralsEpistemology
Politics
Philosophy ofScience
Beliefs
Behaviours
SolutionsAgreements
SustainabilityEconomic
SocialEcological Moral
Aesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Issues
ObjectiveMessy problems
RiskJudgment
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
MoralsEpistemology
Politics
Philosophy ofScience
Beliefs
Behaviours
SolutionsAgreements
SustainabilityEconomic
SocialEcological Moral
AestheticSustainability
EconomicSocial
Ecological MoralAesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social Context
Social ContextIndividual ContextIndividual Context
WorldviewsWorldviews
CognitionCognition
PhilosophyPhilosophy
Conjugate Cognitive Map Issues
ObjectiveMessy problems
RiskJudgment
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
MoralsEpistemology
Politics
Philosophy ofScience
Beliefs
Behaviours
SolutionsAgreements
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Issues
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
SustainabilityEconomic
SocialEcological Moral
Aesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social Context
Social ContextIndividual ContextIndividual Context
WorldviewsWorldviews
CognitionCognition
PhilosophyPhilosophy
Conjugate Cognitive Map Issues
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
SustainabilityEconomic
SocialEcological Moral
AestheticSustainability
EconomicSocial
Ecological MoralAesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Sustainability
Problems
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
Complexity &Strategic
Decision-Making
ScenarioPlanning
Policy ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative viewsof Rationality
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Bucketand
Search-lightExpected
Utility
Cost-BenefitAnalysis
Reductionism
InstitutionalEconomics
SubjectiveExpected
Utility
Reductionism
SocialJudgment Theory
SustainabilitySustainability
Problems
Social Context
Social ContextIndividual Context
WorldviewsWorldviews
Cognition
PhilosophyPhilosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
Complexity &Strategic
Decision-Making
ScenarioPlanning
Policy ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative viewsof Rationality
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Bucketand
Search-lightExpected
Utility
Cost-BenefitAnalysis
Reductionism
InstitutionalEconomics
SubjectiveExpected
Utility
Reductionism
SocialJudgment Theory
Sustainability
Problems
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
Complexity &Strategic
Decision-Making
ScenarioPlanning
Policy ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative viewsof Rationality
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Bucketand
Search-lightExpected
Utility
Cost-BenefitAnalysis
Reductionism
InstitutionalEconomics
SubjectiveExpected
Utility
Reductionism
SocialJudgment Theory
SustainabilitySustainability
Problems
Social Context
Social ContextIndividual Context
WorldviewsWorldviews
Cognition
PhilosophyPhilosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
Complexity &Strategic
Decision-Making
ScenarioPlanning
Policy ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative viewsof Rationality
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Bucketand
Search-lightExpected
Utility
Cost-BenefitAnalysis
Reductionism
InstitutionalEconomics
SubjectiveExpected
Utility
Reductionism
SocialJudgment Theory
Problems
Sustainability
Social Context Individual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
ReductionismComplexity &
StrategicDecision-Making
ScenarioPlanning
Cost-BenefitAnalysis
InstitutionalEconomics
SubjectiveExpected
UtilityPolicy
ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative Viewsof Rationality
SocialJudgment Theory
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Reductionism
Bucketand
Search-light
ExpectedUtility
ProblemsProblems
SustainabilitySustainability
Social Context Individual Context
Worldviews
Cognition
PhilosophyPhilosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
ReductionismComplexity &
StrategicDecision-Making
ScenarioPlanning
Cost-BenefitAnalysis
InstitutionalEconomics
SubjectiveExpected
UtilityPolicy
ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative Viewsof Rationality
SocialJudgment Theory
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Reductionism
Bucketand
Search-light
ExpectedUtility
Problems
Sustainability
Social Context Individual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
ReductionismComplexity &
StrategicDecision-Making
ScenarioPlanning
Cost-BenefitAnalysis
InstitutionalEconomics
SubjectiveExpected
UtilityPolicy
ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative Viewsof Rationality
SocialJudgment Theory
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Reductionism
Bucketand
Search-light
ExpectedUtility
ProblemsProblems
SustainabilitySustainability
Social Context Individual Context
Worldviews
Cognition
PhilosophyPhilosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
ReductionismComplexity &
StrategicDecision-Making
ScenarioPlanning
Cost-BenefitAnalysis
InstitutionalEconomics
SubjectiveExpected
UtilityPolicy
ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative Viewsof Rationality
SocialJudgment Theory
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Reductionism
Bucketand
Search-light
ExpectedUtility
Figure 6.14 – Add as many dimension as need to fully represent the problem issues and relationships.
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Issues
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
SustainabilityEconomic
SocialEcological Moral
Aesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social Context
Social ContextIndividual ContextIndividual Context
WorldviewsWorldviews
CognitionCognition
PhilosophyPhilosophy
Conjugate Cognitive Map Issues
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
SustainabilityEconomic
SocialEcological Moral
AestheticSustainability
EconomicSocial
Ecological MoralAesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
SustainabilityEconomic
SocialEcological Moral
Aesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Issues
ObjectiveMessy problems
RiskJudgment
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
MoralsEpistemology
Politics
Philosophy ofScience
Beliefs
Behaviours
SolutionsAgreements
SustainabilityEconomic
SocialEcological Moral
AestheticSustainability
EconomicSocial
Ecological MoralAesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social Context
Social ContextIndividual ContextIndividual Context
WorldviewsWorldviews
CognitionCognition
PhilosophyPhilosophy
Conjugate Cognitive Map Issues
ObjectiveMessy problems
RiskJudgment
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
MoralsEpistemology
Politics
Philosophy ofScience
Beliefs
Behaviours
SolutionsAgreements
SustainabilityEconomic
SocialEcological Moral
Aesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Issues
ObjectiveMessy problems
RiskJudgment
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
MoralsEpistemology
Politics
Philosophy ofScience
Beliefs
Behaviours
SolutionsAgreements
SustainabilityEconomic
SocialEcological Moral
AestheticSustainability
EconomicSocial
Ecological MoralAesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social Context
Social ContextIndividual ContextIndividual Context
WorldviewsWorldviews
CognitionCognition
PhilosophyPhilosophy
Conjugate Cognitive Map Issues
ObjectiveMessy problems
RiskJudgment
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
MoralsEpistemology
Politics
Philosophy ofScience
Beliefs
Behaviours
SolutionsAgreements
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Issues
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
SustainabilityEconomic
SocialEcological Moral
Aesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social Context
Social ContextIndividual ContextIndividual Context
WorldviewsWorldviews
CognitionCognition
PhilosophyPhilosophy
Conjugate Cognitive Map Issues
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
SustainabilityEconomic
SocialEcological Moral
AestheticSustainability
EconomicSocial
Ecological MoralAesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Sustainability
Problems
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
Complexity &Strategic
Decision-Making
ScenarioPlanning
Policy ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative viewsof Rationality
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Bucketand
Search-lightExpected
Utility
Cost-BenefitAnalysis
Reductionism
InstitutionalEconomics
SubjectiveExpected
Utility
Reductionism
SocialJudgment Theory
SustainabilitySustainability
Problems
Social Context
Social ContextIndividual Context
WorldviewsWorldviews
Cognition
PhilosophyPhilosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
Complexity &Strategic
Decision-Making
ScenarioPlanning
Policy ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative viewsof Rationality
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Bucketand
Search-lightExpected
Utility
Cost-BenefitAnalysis
Reductionism
InstitutionalEconomics
SubjectiveExpected
Utility
Reductionism
SocialJudgment Theory
Sustainability
Problems
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
Complexity &Strategic
Decision-Making
ScenarioPlanning
Policy ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative viewsof Rationality
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Bucketand
Search-lightExpected
Utility
Cost-BenefitAnalysis
Reductionism
InstitutionalEconomics
SubjectiveExpected
Utility
Reductionism
SocialJudgment Theory
SustainabilitySustainability
Problems
Social Context
Social ContextIndividual Context
WorldviewsWorldviews
Cognition
PhilosophyPhilosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
Complexity &Strategic
Decision-Making
ScenarioPlanning
Policy ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative viewsof Rationality
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Bucketand
Search-lightExpected
Utility
Cost-BenefitAnalysis
Reductionism
InstitutionalEconomics
SubjectiveExpected
Utility
Reductionism
SocialJudgment Theory
Problems
Sustainability
Social Context Individual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
ReductionismComplexity &
StrategicDecision-Making
ScenarioPlanning
Cost-BenefitAnalysis
InstitutionalEconomics
SubjectiveExpected
UtilityPolicy
ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative Viewsof Rationality
SocialJudgment Theory
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Reductionism
Bucketand
Search-light
ExpectedUtility
ProblemsProblems
SustainabilitySustainability
Social Context Individual Context
Worldviews
Cognition
PhilosophyPhilosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
ReductionismComplexity &
StrategicDecision-Making
ScenarioPlanning
Cost-BenefitAnalysis
InstitutionalEconomics
SubjectiveExpected
UtilityPolicy
ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative Viewsof Rationality
SocialJudgment Theory
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Reductionism
Bucketand
Search-light
ExpectedUtility
Problems
Sustainability
Social Context Individual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
ReductionismComplexity &
StrategicDecision-Making
ScenarioPlanning
Cost-BenefitAnalysis
InstitutionalEconomics
SubjectiveExpected
UtilityPolicy
ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative Viewsof Rationality
SocialJudgment Theory
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Reductionism
Bucketand
Search-light
ExpectedUtility
ProblemsProblems
SustainabilitySustainability
Social Context Individual Context
Worldviews
Cognition
PhilosophyPhilosophy
Conjugate Cognitive Map Approaches
GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
ReductionismComplexity &
StrategicDecision-Making
ScenarioPlanning
Cost-BenefitAnalysis
InstitutionalEconomics
SubjectiveExpected
UtilityPolicy
ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative Viewsof Rationality
SocialJudgment Theory
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
Three Worldsof Knowledge
Clocksand
Clouds
DifferentOntologies
Reductionism
Bucketand
Search-light
ExpectedUtility
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Issues
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
SustainabilityEconomic
SocialEcological Moral
Aesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social Context
Social ContextIndividual ContextIndividual Context
WorldviewsWorldviews
CognitionCognition
PhilosophyPhilosophy
Conjugate Cognitive Map Issues
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
SustainabilityEconomic
SocialEcological Moral
AestheticSustainability
EconomicSocial
Ecological MoralAesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
SustainabilityEconomic
SocialEcological Moral
Aesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Issues
ObjectiveMessy problems
RiskJudgment
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
MoralsEpistemology
Politics
Philosophy ofScience
Beliefs
Behaviours
SolutionsAgreements
SustainabilityEconomic
SocialEcological Moral
AestheticSustainability
EconomicSocial
Ecological MoralAesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social Context
Social ContextIndividual ContextIndividual Context
WorldviewsWorldviews
CognitionCognition
PhilosophyPhilosophy
Conjugate Cognitive Map Issues
ObjectiveMessy problems
RiskJudgment
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
MoralsEpistemology
Politics
Philosophy ofScience
Beliefs
Behaviours
SolutionsAgreements
SustainabilityEconomic
SocialEcological Moral
Aesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Issues
ObjectiveMessy problems
RiskJudgment
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
MoralsEpistemology
Politics
Philosophy ofScience
Beliefs
Behaviours
SolutionsAgreements
SustainabilityEconomic
SocialEcological Moral
AestheticSustainability
EconomicSocial
Ecological MoralAesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social Context
Social ContextIndividual ContextIndividual Context
WorldviewsWorldviews
CognitionCognition
PhilosophyPhilosophy
Conjugate Cognitive Map Issues
ObjectiveMessy problems
RiskJudgment
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
MoralsEpistemology
Politics
Philosophy ofScience
Beliefs
Behaviours
SolutionsAgreements
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
Conjugate Cognitive Map Issues
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
SustainabilityEconomic
SocialEcological Moral
Aesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Social Context
Social ContextIndividual ContextIndividual Context
WorldviewsWorldviews
CognitionCognition
PhilosophyPhilosophy
Conjugate Cognitive Map Issues
Social meaningof risk
Moral standing
Power
Dominance
Social constructionof risk
Dominance
MedicalLegal
Leadingquestions
PowerPreferences
MoralstandingRisk
Acceptance
PoliticsWorldview AWorldview B
Postmodernism Modernism
Neoconfucianism
Biases and Heuristics
ValuesBeliefs
Anarchism
Older theory vsbetter theory
Rationality
SustainabilityEconomic
SocialEcological Moral
AestheticSustainability
EconomicSocial
Ecological MoralAesthetic
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Complex ProblemsObjective SubjectiveMessy problemsRisk
JudgmentMoral problemsType 3
Sustainability
Problems
Social ContextIndividual Context
Worldviews
Cognition
Philosophy
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GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
Complexity &Strategic
Decision-Making
ScenarioPlanning
Policy ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative viewsof Rationality
SocialRepresentations
Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
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Search-lightExpected
Utility
Cost-BenefitAnalysis
Reductionism
InstitutionalEconomics
SubjectiveExpected
Utility
Reductionism
SocialJudgment Theory
SustainabilitySustainability
Problems
Social Context
Social ContextIndividual Context
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Cognition
PhilosophyPhilosophy
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GameTheory
Multi-CriteriaMapping
MUAT SystemsThinking Mixed
MethodologiesProblem
Structuring
Complexity &Strategic
Decision-Making
ScenarioPlanning
Policy ManagementStyles
Engagementof Public
ValueElicitation
BoundedRationality
ProspectTheory
ImageTheory
CognitiveMapping
Congregative CognitiveMapping
Alternative viewsof Rationality
SocialRepresentations
Philosophiesof Science
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Language
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Utility
Cost-BenefitAnalysis
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MUAT SystemsThinking Mixed
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Philosophiesof Science
Anarchy
CosmologiesMetalanguage
Language
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Figure 6.14 – Add as many dimension as need to fully represent the problem issues and relationships. The information gathered in the critique is being used to inform decision-makers of what
other investigating work needs to be done to address the issues highlighted in the
structuring approach.
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