Cynefin, Statistics and Decision Analysis

This paper explores the value of Cynefin framework in statistical inference and decision analysis. It discusses how the framework helps recognize which analytic and modeling methodologies are most suitable in different contexts. The paper also explores the relationship between scenario thinking and decision analysis in supporting decision makers.

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Cynefin, Statistics and Decision Analysis
Simon French
Risk Initiative and Statistical Consultancy Unit
Department of Statistics
University of Warwick
Coventry, CV4 7AL
[e-mail]
January 2012
Abstract
David Snowden’s Cynefin framework, introduced to articulate discussions of sense-making,
knowledge management and organisational learning, also has much to offer discussion of
statistical inference and decision analysis. I explore its value, particularly in its ability to help
recognise which analytic and modelling methodologies are most likely to offer appropriate
support in a given context. The framework also offers a further perspective on the relationship
between scenario thinking and decision analysis in supporting decision makers.
Keywords: Cynefin; Bayesian statistics; decision analysis; decision support systems;
knowledge management; scenarios; scientific induction; sense-making;
statistical inference.
1 Introduction
Several years I attended a seminar on knowledge management given by David Snowden. At
this he described the Cynefin conceptual framework, which, inter alia, offers a categorisation
of decision contexts (Snowden, 2002). Initially I saw little advantage over other
categorisations of decisions, such as the strategy pyramid: viz. strategic, tactical and
operational (see Figure 2 below). However, Carmen Niculae had more insight and working
with her and others, I have appreciated Cynefin’s power to articulate discussions of inference
and decision making. Below, I explore Cynefin and its import for thinking about statistics
and decision analysis. There is nothing dramatic in anything I shall say. Many will have
reached similar conclusions. Perhaps also David Snowden will take this as a small apology
for my initial dismissal of his ideas.

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I hope it becomes clear that Cynefin offers benefits to several types of user: analysts can use
it to help identify what methodologies might be suitable for the problem faced by their
clients; their clients themselves can use it to gain insight into the qualities of the issues that
they face; and academic researchers may use it in exploring and categorising methodologies
within statistics, decision analysis and operational research. In this paper, my discussion
leans much towards the last of these three, though there are many elements that speak to all
three audiences. Thus this paper adds to many discussions of operational research (OR)
methodology and the OR process that may be found in the literature, recasting parts of them
into the Cynefin framework and drawing, I believe, some new insights, particularly in
relation to the interplay between decision makers’ knowledge of the external world,
themselves and the types of statistical, decision and OR analysis that may be most suited to
their current context (see, e.g., White, 1975; 1985; Mingers and Brocklesby, 1997; Mingers,
2003; Ormerod, 2008; Luoma et al., 2011).
In the next section I describe Cynefin, before turning in Section 3 to some specific
applications that I have found helpful in articulating discussion across range of statistical and
decision analytic contexts. In Section 4, I explore the relationship between knowledge
management and decision making. Knowledge and the process of inference are intimately
related; in Section 5, I explore some relationships between Cynefin, the Scientific Method
and statistical methodology; and in Section 6 I build on this to discuss decision analysis in the
knowable and complex domains. There I discuss how uncertainties might be addressed,
exploring a relationship between scenario thinking and formal decision analysis. Section 7
offers some brief conclusions.
2 Cynefin
So what is Cynefin? The name comes from the Welsh for ‘habitat’, at least in a narrow
translation. But Snowden (2002) suggests there are also connotations of acquaintance and
familiarity, quoting Kyffin Williams, a Welsh artist:
“(Cynefin) describes that relationship – the place of your birth and of your upbringing, the
environment in which you live and to which you are naturally acclimatised.”
The embodiment of such ideas as familiarity makes Cynefin clearly relevant to knowledge
management. Nonaka’s concept of Ba serves similarly: a place for interactions around
knowledge creation, management and use (Nonaka, 1991; 1999; Nonaka and Toyama, 2003).
Snowden distinguishes Cynefin from Ba through the Welsh word’s association with

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community and shared history: for further discussion, see Nordberg (2006). Our concern will
be with how Cynefin
 characterises various forms of uncertainty,
 helps structure our thinking about statistical inference and the design of research studies,
 relates to decision making, decision analysis and decision support, and
 relates to our self-knowledge of our values – and values, it should be remembered,
should be “the driving force of our decision making” (Keeney, 1992).
Snowden’s Cynefin model roughly divides decision contexts into four spaces: see Figure 1.
In the known space, also called simple order or the Realm of Scientific Knowledge, the
relationships between cause and effect are well understood. The known space contains those
contexts with which we are most familiar because they occur repeatedly; and because we
have repeated experience of them, we have learnt underlying relationships and behaviours
sufficiently well that all systems can be fully modelled. The consequences of any course of
action can be predicted with near certainty, and decision making tends to take the form of
recognising patterns and responding to them with well-rehearsed actions. Snowden describes
decision making in these cases as SENSE, CATEGORISE AND RESPOND (Kurtz and Snowden,
2003). Klein (1993) terms this recognition-primed decision making; French, Maule and
Papamichail (2009) term such decision making instinctive.
In the knowable space, also called complicated order or the Realm of Scientific Inquiry, cause
and effect relationships are generally understood, but for any specific decision there is a need
to gather and analyse further data to predict
the consequences of a course of action with
any certainty. Snowden characterises
decision making in this space as SENSE,
ANALYSE AND RESPOND. Decision analysis
and support require the fitting and use of
models to forecast the consequences of
actions with appropriate levels of uncertainty.
In this realm standard methods of operational
research and decision analysis apply (see,
e.g., Clemen and Reilly, 2004; Taha, 2006).
Cause and effect can
be determined with
sufficient data
Knowable
The Realm of
Scientific Inquiry
Complex
The Realm of Social Systems
Cause and effect may be
determined after the event
Chaotic
Cause and effect
not discernable
Known
The Realm of Scientific
Knowledge
Cause and effect understood
and predicable
Figure 1: Cynefin

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In the complex space, also called complex unorder or the Realm of Social Systems, decision
making situations involve many interacting causes and effects. Knowledge in this space is at
best qualitative: there are too many potential interactions to disentangle particular causes and
effects. Every situation has unique elements: some unfamiliarity. There are no precise
quantitative models to predict system behaviours such as in the known and knowable spaces.
Decision analysis is still possible, but its style will be broader, with less emphasis on details.
Decision support will be more focused on exploring judgement and issues, and on developing
broad strategies that are sufficiently flexible to accommodate evolving situations. Snowden
suggests that in these circumstances decision making will be more of the form: PROBE,
SENSE, AND RESPOND . Analysis begins, and perhaps ends, with informal qualitative models,
known as soft modelling, soft OR or problem structuring methods (Rosenhead and Mingers,
2001; Mingers and Rosenhead, 2004; Pidd, 2004; Franco et al., 2006; Shaw et al., 2007). If
quantitative models are used, then they are simple, perhaps linear multi-attribute value
models (Belton and Stewart, 2002). One point of terminology should be noted: namely, this
difficulty of understanding cause and effect can occur in environmental, biological and other
contexts as much as in social systems.
In discussing the complex space, one should be careful to avoid confusion with complexity
science. While some complexity science does relate to Snowden’s complex space, it is more
concerned with computational issues relating to very complicated models. Such models and
computational issues belong more to Snowden’s knowable and known spaces rather than the
complex one. Models, however complicated, seek to encode known understandings of cause
and effect. The difficulty is that, though causes and effects, correlations and non-linearities
are understood, their great number makes it difficult, if not intractable to compute the
predicted effects of a set of causes.
In the chaotic space, also called chaotic unorder, situations involve events and behaviours
beyond current experience with no obvious candidates for cause and effect. Decision making
cannot be based upon analysis because there are no concepts of how to separate entities and
predict their interactions. Decision makers will need to take probing actions and see what
happens, until they can make some sort of sense of the situation, gradually drawing the
context back into one of the other spaces. Snowden characterises such decision making as
ACT, SENSE AND RESPOND : more prosaically, ‘trial and error’ or even ‘poke it and see what
happens!’
Donald Rumsfeld famously said:

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“There are known knowns; there are things we know we know. We also know there are known
unknowns; that is to say we know there are some things we do not know. But there are also
unknown unknowns - the ones we don't know we don't know.”
He missed a category: ‘unknown knowns’. ‘Known knowns’ corresponds to knowledge in
the known space; ‘known unknowns’ to that in the knowable space; and ‘unknown
unknowns’ to that in the chaotic space. ‘Unknown knowns’ would correspond to our
knowledge in the complex space, in which we know candidates for causes and effects, but not
their full relationships.
Cynefin has parallels with the strategy pyramid: see Figure 2. In this the strategy pyramid has
been extended from its more common trichotomy of operational, tactical and strategic
decisions by including a fourth category of instinctive or recognition-primed decisions
(French et al., 2009). Strategic decisions set a broad direction, a framework in which more
detailed tactical and operational decisions may be taken. In delivering operational decisions,
many much smaller decisions have to be taken. These are the instinctive, recognition-primed
ones. Simon (1960) noted that strategic decisions tend to be associated with unstructured,
unfamiliar problems. Indeed, strategic decisions often have to be taken in the face of such a
myriad of ill-perceived issues, uncertainties and ill-defined objectives that Ackoff (1974)
dubbed such situations messes. There is a clear alignment of the context of strategic decision
making and the complex and even chaotic spaces of Cynefin. Tactical, operational and
instinctive decision contexts have increasing familiarity and structure, and occur with
increasing frequency. Again the alignment with Cynefin is clear. Jacques (1989)
distinguished four domains of activity, and hence decision making, within organisations: the
corporate strategic, general, operational and hands-on work. French et al. (2009) relate
these directly to the strategic, tactical, operational and instinctive categories in the extended
Strategic
Tactical
Operational
Instinctive
(recognition primed)
Knowable
Complex
Chaotic
Tactical
Strategic
Known
Instinctive
Operational
Figure 2: Relationship between the perspectives offered by the strategy pyramid and Cynefin

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strategy pyramid, and hence they also relate to Cynefin as in the curved arrow in Figure 2.
Note, however, that I do not claim precise identification of the chaotic, complex, knowable
and known spaces with strategic, tactical, operational and instinctive decision making
contexts. While the appropriate domain for instinctive decision making may lie entirely
within the known space, operational, tactical and strategic decision making do not align quite
so neatly, overlapping adjacent spaces. Indeed, the boundaries between the four spaces in
Cynefin should not be taken as hard. The interpretation is much softer with recognition that
there are no clear cut boundaries and, say, some contexts in the knowable space may have a
minority of characteristics more appropriate to the complex.
Snowden uses Cynefin to discuss issues such as
organisational culture and leadership, and
knowledge management (Snowden, 2002;
Snowden and Boone, 2007). Within knowledge
management there is distinction between explicit
knowledge – i.e., knowledge with can be encoded –
and tacit knowledge – the skills, expertise, values
and so that we cannot articulate, at least currently,
other than by showing them in our behaviours (Polyani, 1962; French et al., 2009). Nonaka’s
SECI cycle (Figure 3) suggested four mechanisms by which knowledge is created, explored
and shared (Nonaka, 1991; 1999):
 Socialisation  sharing tacit knowledge in communities through mentoring, discussion,
collaboration etc.;
 Externalisation  articulating tacit knowledge explicitly in words, tables, charts,
diagrams, models, expert systems and so on;
 Combination  drawing together and systematising explicit knowledge into more generic,
simpler, and more widely applicable forms;
 Internalisation  intuitively understanding the implications of generic explicit knowledge
and deploying this tacit understanding in our behaviour and decision making.
Implicit in the socialisation loop is the possibility that some tacit knowledge will never be
rendered explicit. Within Cynefin one would expect tacit knowledge to dominate in the
complex and chaotic spaces, while explicit knowledge dominates in the known and knowable
spaces. This, in turn, suggests that knowledge management relies more on socialisation in
tacit
knowledge
explicit
knowledge
Socialisation
Internalisation
Combination
Externalisation
Figure 3: Nonaka's SECI cycle

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the complex and chaotic spaces and on combination in the known and knowable spaces.
Indeed, behaviour in the known and knowable space builds on scientific knowledge, the
archetypal example of combined explicit knowledge, i.e. scientific models and theories.
What does Cynefin bring to discussions of decision making? I do not claim that any of the
following could not be – indeed, has not been – discussed without the structure of Cynefin:
e.g., see Brundtland (1987). However, Cynefin does seem to facilitate such discussions well,
perhaps because it simultaneously addresses knowledge and decision making. In the next
section I illustrate this point with a number of applications.
3 Illustrations of how Cynefin can articulate issues and concerns in a
variety of applications.
[For many further examples and discussions of applications of Cynefin, see
http://www.cognitive-edge.com/.]
An interpretation of some of the issues in emergency management
Emergency management provided the first example to convince me of the power of Cynefin
to articulate and communicate issues. Looking at many past instances of the handling of
large scale emergencies, it was apparent to Carmen Niculae and me that the authorities,
despite addressing the physical aspects of the emergency well, often lost the confidence of
the public. We found that we could articulate the dynamics of an emergency intuitively using
Cynefin. Essentially, the authorities think that they are handing an event in the known or
knowable spaces, whereas associated socio-political-economic issues may pull the emergency
into the complex space. There is a dislocation between the authorities’ perception of the
situation and reality (French and Niculae, 2005). In the heat of a crisis the imperative is to do
all one physically can to save and protect life and to remove the source of the danger. But
many are affected in different, non-physical ways. Justifiable concerns and stresses build:
individuals fear for or mourn loved ones, and as do communities; ways of life are changed
temporally, perhaps permanently; economic effects occur and can quickly impact some
groups disproportionately; etc. Stresses and concerns grow rapidly (Barnett and Breakwell,
2003; Kasperson et al., 2003), outstripping the resources devoted to community care.
In the early phase of the Chernobyl Accident, the decision context could be placed in the
knowable space: causes and effects were understood, although there were gross uncertainties
about the source term and the distribution of the contamination. Successive post-accident

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strategies, which continued to be based upon assumptions belonging to the known and
knowable spaces, focused on technical issues of radiation protection and neglected the
enormous social and cultural harm that the accident was causing (International Atomic
Energy Agency, 1991; Karaoglou et al., 1996; French et al., 2009). Thus, the context passed
into the complex space, but for a period was managed as if it were in the known or knowable
domains. This dislocation led to affected communities questioning and essentially rejecting
all the authorities’ protective and recovery measures. Eventually socio-economic issues were
addressed. For instance, the ETHOS project applied an approach which explored social and
cultural understandings along with more technical perspectives through multi-disciplinary
teams and strong involvement of the local population to rebuild a good overall quality of life
(Heriard Dubreuil et al., 1999).
The same issues can be discerned in the handling of many crises: e.g. Three Mile Island,
Mad-Cow Disease, and Hurricane Katrina (Niculae, 2005). Indeed, as I write this, BP is
being pilloried for its mismanagement of the Gulf Oil Spill and, admittedly before all the
evidence is published, I cannot help reflect that they may have myopically concentrated on
the technical issues of sealing the well-head, issues largely in the known and knowable
spaces, and missed the socio-economic and cultural impact, both actual and feared, that the
spill was creating, issues that clearly lie in the complex space. Such issues have led many to
argue for a more coherent socio-technical approach to emergencies in which the authorities
embrace and address all the public’s concerns throughout the response and not just recovery
phase (Fischhoff, 1995; Mumford, 2003; French et al., 2005; French and Niculae, 2005).
Categorisation of decision support process and systems
To understand the appropriate use of decision analysis and support, one needs to categorise
decision support processes and systems according to the level of support provided and the
decision context (2009). French (2010) categorises the level of support as in Table 1 and
uses Cynefin for decision contexts: see Figure 4. This suggests, for instance, that simulation
methods have a role to play in offering Level 2 support in the known and known spaces, but
are not relevant to the complex or chaotic spaces because in those cause and effect are not
understood sufficiently for simulation. Many similar points become apparent on mapping
other decision support processes and systems into this categorisation: e.g.
 Databases and data mining provide Level 0 support over all the spaces, but are often
called management information systems ( MIS) or executive information systems ( EIS)

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in the knowable and complex spaces, respectively (Alter, 2002; Laudon and Laudon,
2009).
 Expert systems ( ES), neural nets, and other artificial intelligence ( AI) techniques provide
level 2 and 3 support. Some authors suggest thatAI-based systems have much wider
application, but such systems are only really suited to the highly structured, repetitive
situations in the known and knowable spaces because of their need for large training sets
(see also Edwards et al., 2000).
 Quantitative OR modelling, e.g. linear
programming, inventory and maintenance
models (Denardo, 2002; Taha, 2006),
underpins many of the systems used in
the knowable space at levels 2 and 3, but
most quantitative OR techniques assume
too much structure to be appropriate for
the complex space.
 Problem and issue structuring tools
(PSM), often called soft OR or soft
modelling tools, can provide Level 0
support in the complex space (Rosenhead
and Mingers, 2001; Mingers and
Rosenhead, 2004; Franco et al., 2006;
Shaw et al., 2007; French et al., 2009).
So can exploratory data analysis ( EDA)
Chaotic
Level 0:
Level 1:
Level 2:
Level 3:
exploration,
trial and error,
the practice of
science.
}{ Known
Level 0: database systems
Level 1: forecasting
Level 2: simulation
Level 3: AI, e.g. expert systems,
neural nets
Knowable
Level 0: databases, MIS
Level 1: statistical inference,
forecasting
Level 2: OR models e.g. LP,
simulation
Level 3: decision trees,
influence diagrams
Complex
Level 0: soft OR, PSM, EDA, EIS , data-
mining
Level 1: expert judgement
Level 2: metagames and scenario planning
Level 3: simpler MCDM models,
simpler decision trees,
influence diagrams
Data and model based systems
(cf. combination cycle of SECI)Collaboration
tools, GDSS, decision
conferencing, etc.
(cf. socialisation cycle of SECI)
 Figure 4: Categorisation of decision
support processes and systems (French, 2010).
Key: AI – artificial intelligence; EDA – exploratory
data analysis; EIS – executive information system;
LP – linear programming; MCDM – multi-criteria
decision making; MIS – management information
system; OR – operational research; PSM – problem
structuring methods.
 Level 0 Acquisition, checking and presentation of data, directly or with minimal analysis, to decision
makers.
Level 1 Analysis and forecasting of the current and future environment.
Level 2 Simulation and analysis of the consequences of potential strategies; determination of their
feasibility and quantification of their benefits and disadvantages.
Level 3 Evaluation and ranking of alternative strategies in the face of uncertainty by balancing their
respective benefits and disadvantages.
Table 1: Levels of support that may be offered by decision support processes and systems (French et
al., 2009). Note that levels 0 – 2 relate mainly to supporting the evolution of decision makers’
perceptions of the external world; whereas level 3 relates to their understanding of their
preferences and evaluation of the options before them, i.e. their understanding of themselves.

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(Tukey, 1977), which is often incorporated into EIS. Modern data mining techniques may
also be appropriate here (Hand et al., 2001; Korb and Nicholson, 2004). However,
automated though these procedures seem, they inevitably require judgement to separate
interesting and useful patterns from spurious ones; there is insufficient repetitivity for
more ‘objective’ techniques such as confirmatory significance testing.
 For level 1 or level 2 support in the complex space one may use methodologies such as
scenario planning (Schoemaker, 1995; van der Heijden, 1996; Montibeller et al., 2006)
or metagames (Howard, 1971), methodologies that stimulate decision makers to
anticipate contingencies, and perhaps provide some simple qualitative consequence
modelling.
 Level 3 support in the complex space may be provided by some simpler multi-criteria
decision making models (Belton and Stewart, 2002), such as multi-attribute value
analysis (Keeney and Raiffa, 1976), multi-criteria decision aids (Roy, 1996) or the
analytic hierarchy process (Saaty, 1980), which help decision makers explore their
values. Simple decision trees and influence diagrams may also be used to understand
some of the broad uncertainties facing decision makers. Further discussion is offered in
Section 6.
Finally remembering our discussion of Nonaka’s SECI cycle, decision making in the complex
and chaotic spaces on the left hand side of Cynefin will be based more on judgement, tacit
knowledge and exploration. Thus the primary activity in deliberating on possible strategies
will be the socialisation and sharing of tacit knowledge. Whereas in the known or knowable
spaces, decision making will be based more on explicit knowledge and the use of decision
models and data will be more common (Niculae et al., 2004). This suggests that in the
complex or chaotic spaces effective decision support needs to focus on facilitating
collaboration, whereas in the known or knowable spaces decision support systems will be
data- or model-based: see Figure 4.
Human behaviour, risk analysis and high reliability organisations
Recently, I was part of a research project to survey and critique human reliability analysis
(HRA) methodologies and consider their role in summative risk and reliability analyses
(Adhikari et al., 2008; French et al., 2010a). Our findings were not comforting. The
empirical evidence is that human behaviour, not necessarily erroneous behaviour, is involved
in something like 75% of all systemic failures. Yet currentHRA methodologies lack the

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sophistication to model current understandings of human behaviour, particularly in relation to
the correlations and dependencies that it can introduce into systems. Modelling approaches
used in HRA tend to be focussed on easily describable sequential, low-level tasks, i.e. ones in
the known space in which the operators tend to use recognition-primed decision making. But
such tasks are seldom the initiators of systemic failures, which almost invariably involve the
occurrence of and higher-level responses to unexpected, infrequent events in the complex
space, for which operators need problem solving and decision making skills. Moreover, such
high level responses can affect many parts of the system, correlating events. In other words,
the empirical base of HRA is inappropriate to many of the behaviours in systemic failures.
Our research found that Cynefin was an effective in articulating such issues, providing a
framework to discuss the applicability of different HRA methodologies (see also Deloitte,
2009). Further, we also suggested that risk and reliability studies should use Cynefin to
categorise the various contexts of human activity within a system before beginning any HRA.
We also considered high reliability organisation ( HRO) theory as part of our studies. Again
Cynefin offered an effective way of articulating a concern. Early HRO theory drew on
examples such as carrier flight deck operations to provide its empirical base and then
extrapolated its thinking to risk and crisis management in contexts such as Bhopal and
Chernobyl (see, e.g, Weick, 1987). Yet this moves from repetitive contexts in Cynefin’s
known space to unique contexts in the complex or chaotic spaces. High reliability in known
contexts is likely to be based upon agreed single perspective science – a single shared mental
model; whereas in complex contexts high reliability organisations need to manage multiple
perspectives and families of shared mental models.
Cynefin, sense-making and problem structuring
At its simplest the decision analysis cycle involves three phases (French et al., 2009):
 Formulation or Sense-making Phase, during which the problem, issues, objectives
uncertainties and options are identified and formulated. This phase is much more visible
in the knowable and complex spaces. In the known space, the problems repeat so often
that they were formulated long ago and sense-making becomes a matter of recognition,
as acknowledged in term ‘recognition-primed decision making’.
 Analysis Phase, during which the issues, objectives, uncertainties and options are
modelled and analysed. This involves predicting the consequences of each possible
option in terms of their success in achieving the decision makers’ objectives, taking

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account of the uncertainties in the prediction. Thus the analysis offers guidance towards
options which promise to achieve their objectives. The analysis itself may be formalised
as in the quantitative techniques of finance, decision analysis and OR; or it may be much
more informal and qualitative, perhaps a few diagrams and lists. Moreover, there may be
more than one strand of analysis, each representing a different perspective on the
problem, perhaps those of different stakeholders or, as discussed below in Section 6,
different future scenarios (French, 2003; French et al., 2009).
 Appraisal and Decision Phase, during which the decision makers decide which option to
implement or whether more analysis is needed. Since any model is a simplification of
the real world, there will be a need to reflect on the recommendation and see if it makes
sense once the complexity of reality re-enters the discussion. Has the analysis brought
enough understanding to make the decision? Is it requisite (Phillips, 1984; French et al.,
2009)? If so, decide and implement; if not, introduce further issues into the formulation
and reanalyse.
During the formulation or sense-making phase, an analyst seeks to explore, evolve and
challenge the perspectives of the decision makers to build a shared understanding of the
issues and problem(s). The PSMs, referred to above, provide tools to help in this. As
Snowden and his colleagues have emphasised (Kurtz and Snowden, 2003), Cynefin is an
excellent PSM for challenging decision makers to explore the context of the problem. In
decision workshops I have outlined the Cynefin framework to participants, sketched it on a
flipchart and then invited them to discuss where the issues that concern them lay, perhaps
locating them on the chart using post-its. The ensuing discussion has always been
enlightening. Participants seem to find the Cynefin framework intuitive and catalytic. Not
only does it help them set the issues that they face within a broader context, it has proved
useful in helping them understand why their favourite problem solving tools may be
inappropriate in this case: cf. Figure 4. For case studies of the use of Cynefin in problem
structuring, see Deloitte (2009) and www.cognitive-edge.com.
4 Knowledge Management, Inference and Decisions
Snowden introduced Cynefin as a framework to discuss knowledge management. Since the
main focus of this paper relates to statistical inference and decision analysis, it may be helpful
to discuss the relationship between the three topics briefly (for further discussion of decision
making and knowledge management, see Nicolas, 2004). Let me begin by making distinction

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between data, information and knowledge (for references and further discussion of this
distinction, see French et al., 2009). Data are facts about things, events, activities,
transactions, etc. Data do not relate for any specific context. Information, on the other hand,
does relate to a specific context. Information is formed by selecting, organising, summarising
data to be meaningful and useful within a specific context. Knowledge is more generic,
relevant to many contexts, and longer lasting. Knowledge includes, among other things, the
understandings – Boisot (1998) terms these perceptual and conceptual filters – that enable us
to make inferences, forecasts and decisions. As we have noted, some knowledge is tacit;
data and information, however, can always be made explicit and codified.
Philosophers have distinguished kinds of knowledge in many ways: e.g. procedural
knowledge or ‘knowing how’, which refers to a person’s skills; propositional knowledge or
‘knowing that’, which refers to general theories, models and understandings; and personal
knowledge or ‘knowing things through acquaintance’, which refers to an individual’s
knowledge of objects, people and systems. For me, personal knowledge also contains a
person’s understanding of his or her internal self, including his or her beliefs and values.
Figure 5 outlines one view of how knowledge is used in decision making; the related tasks of
inferring and forecasting are discussed shortly. Firstly, one needs to recognise that
information can take many forms. Information is data selected and organised for a purpose in
the formulation phase. In the complex or the chaotic spaces that may require an exploration
of a range of nebulous, partially perceived issues and feelings to bound the context and
identify the need for a decision: sense-making in the broadest of senses. Decision makers and
their analysts need to draw on much judgement, experience and creativity to make sense of
the situation, using tacit as well as explicit knowledge, leading into much clearer processes,
the use of PSMs, EDA and so forth. In the known and knowable spaces, the formulation phase
will be well-rehearsed, maybe completely
so, drawing on much explicit knowledge
learnt from past experience. Arrow A in
Figure 5 indicates these uses of
knowledge in the formulation phase.
More detailed organisation of data in the
analysis phase of decision making is also
indicated by arrow A. In the known and
knowable spaces, the analysis may be
INFERENCES
FORECASTS
DECISIONS
DATA  INFORMATION  KNOWLEDGE
A
B
C D
Figure 5: The relationship between data, information
and knowledge and the making of inferences,
forecasts and decisions (French et al., 2009)

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quantitative involving statistical, mathematical, or operational research models; in the
complex and certainly the chaotic spaces, it will be much less formal.
Arrow C indicates that the decision makers are informed by the outputs of the formulation
and analysis phases. Arrow D indicates that to understand the information and accord it
appropriate weight in their thinking, they need to draw on their knowledge of the analytic
methods used and how these have helped them in the past. Kuhn (1961) makes a similar
remark relating to scientific inference. They need to use further judgement, i.e. tacit
knowledge, to recognise when the analysis is requisite, providing them with sufficient
understanding to proceed to decide and implement a course of action.
Over time, decision makers, or their analysts, may recognise certain new common patterns in
several of the contexts in which they have formulated, analysed and made decisions. This in
turn may suggest new generic insights, i.e. new knowledge, which may be applied in the
future. This ability to recognise such patterns and form new knowledge requires insight and
higher level knowledge, indicated by arrow B in Figure 5, of how one learns; some of this
will be explicit, but much may be tacit. For a recent review on this aspect of learning and
how it might occur, see Dorfer (2010). This ability underpins the practice of scientific
induction to be discussed in the next section.
5 Cynefin, Sense-making and Statistical Inference
Much of my early career was spent in the then nascent school of modern Bayesian statistics.
Back in the 1970s and 1980s there were many vibrant, some might say bitter, discussions of
scientific induction, the role of statistical inferential methods, objectivity versus subjectivity,
the need for repeatability, and where EDA fitted in (for a broad discussion of these issues, see
Barnett, 1999; Nakicenovic and Swart, 2000). Looking at Cynefin, I wonder if we might
have articulated those discussions better within its framework.
One can sketch the growth of human knowledge – science, in its generic meaning – on
Cynefin very easily. We begin ignorant, facing events and behaviours in the chaotic space.
Causes and effects are unclear. Things are ‘random’ to us. Gradually through our
interactions with our environment: by ‘environment’ I mean anything that we observe and
which affects our lives. By acting, sensing and responding, we begin to make sense of some
parts of it, seeing patterns or some such. Slowly our perception of those parts of the
environment move into the complex space as we perceive some causes and their effects, but
we still do not have enough understanding to model and predict the precise effects of a set of

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causes. Through continued interactions with the environment – probing, sensing and
responding – we make more sense of some of the behaviours and build a much tighter
understanding of cause and effects, building models and laws. While we still cannot predict
perfectly, needing parameters or similar to complete the models; our understanding moves
into the knowable space. Next we seek data to determine parameters and so on, which will
allow our models to predict more detailed effects from a particular set of causes. Through
more interactions – sensing, analysing and responding – we determine the parameters, finding
that some are very widely applicable, e.g. physical constants, and these aspects of our
understanding move into the known space, where we can predict effects from a set of causes
without further data simply by categorising and responding.
Of course, this is a very naive description of the process of science, though perhaps not as
naive a description as I have heard in some research methodology courses! While this
description lacks subtlety and sophistication, particularly in the later stages of the process as
models and laws are inferred, it has the virtue of beginning with sense-making in the chaotic
and complex spaces. Too often thinking on scientific induction and statistical inference begin
with the assumption that we have made enough progress to have some understanding of cause
and effect, perhaps even a hypothesis or putative model. Sense-making has not received the
attention it should have in the Philosophy of Science; and it still has to achieve an equal
status with statistical inference within descriptions of scientific induction. Rather the
methodology of science has focused much more on the testing and validation of models and
theories and the estimation of parameters, i.e. those processes that fall in the known and
knowable spaces. Such contexts are necessarily repeatable or commonly occurring.
Repeatability has come to lie at the heart of the scientific induction (Chalmers, 1999).
Scientific models and theories can only be validated if they can be tested again and again in
identical circumstances and shown to explain and predict system behaviours. It is not
surprising, therefore, that Frequentist approaches dominated as statistical methodologies
developed during the late 19th and most of the 20th century: i.e. approaches based upon
conceptions of probability which have repeatability at their heart. Moreover, the primary
goal of such statistical methods was to formalise the processes of estimation of parameters
and confirmation or refutation of hypotheses. Such statistical inference comes at the end of a
long process of sense-making, discovery, creation and refinement of scientific knowledge.
The underpinning philosophy and methodology of statistical inference are controversial
topics, surprisingly so for what is often perceived as a dry subject (Barnett, 1999). There are

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many schools of thought, often gathered together into the Bayesian and Frequentist. But that
dichotomy is a gross categorisation that misses many subtle differences. Below I identify
four general schools, and even that rides roughshod over many issues deserving more careful
exploration. But four will be sufficient to locate and explain my position.
Broadly, the categories relate to two related factors: whether a school takes an objective or
subjective stance and how it interprets probability.
 Subjective Subjectivist Schools. Essentially these approaches lie at the extreme of the
subjective-objective dimension. The statistician or scientist lies at the heart of the
approach and the role of scientific induction is to make sense of and ultimately predict
the sensory stimuli that he or she receives. Often one feels that the existence of an
external world may be moot, though whether it exists is immaterial to the theories of
inference that are developed. An external world may explain the correlations between
the sensory stimuli, but such explanations are strictly irrelevant to the process of
inference which aims simply to model the correlations between stimuli to predict further
stimuli. Sense-making becomes a matter of perceiving exchangeability relations between
series of stimuli and developing previsions, DeFinneti’s term for what are essentially
probabilities of future stimuli. From these probability models follow, along with prior
beliefs. Inference based upon Bayesian updating naturally follows. Prior probability
distributions are updated through a likelihood function in the light of new evidence to
give posterior probability distributions. Any parameters that arise within the probability
models are artefacts which characterise the exchangeability assumptions and do not
necessarily represent ‘states of the world’, i.e. measurements of external entities: for key
references see DeFinetti (1974; 1975) and Lad (1996).
 Subjective Objectivist Schools. These approaches do begin by postulating an external
world and the process of sense-making is, first, one of identifying a state space that has
the capacity to describe the statistician’s or scientist’s perception of this world and,
second, of building sufficient understanding of the world that his or her beliefs about it
may be articulated through probability distributions over the state space. Again inference
based upon Bayesian updating follows naturally. In this case, however, at least some the
parameters in the models may be identified with measurements of external entities.
Moreover, in addition to representing his or her beliefs about entities, the probability
distribution may also represent randomness that he or she believes to be present in the

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world. Key references in this area are Lindley (1965), DeGroot (1970), Box and Tiao
(2008), Forster and O’Hagan (2004), French and Rios Insua (2000) and Savage (1972).
 Logical Objectivist Schools. In this case the focus is on the language in which the
external world is described. This language is independent of the scientist or statistician: a
public language. These approaches suggest that objective knowledge can be embedded
in an objective language. Probability is defined by through assessments of how much the
truth of one proposition is entailed through the truth or falsity of other propositions.
Sense-making becomes a matter of exploring potential descriptions of the world and
identifying their prior probability from the relationship between their expression in
propositions and propositional knowledge already encoded probabilistically. Since both
prior knowledge and evidence are both encoded through probability distributions,
Bayesian updating again becomes the mechanism of inference. Key references in this
area are Carnap (2009), Jeffreys (1961) and Keynes (2004).
 Empirical Objectivist Schools. Here the focus is on repeatability. Probability is
interpreted as the long run frequency of outcomes of an infinitely repeated experiment or
infinitely repeated sampling of a population. Probability thus relates to external
randomness and not to beliefs or knowledge. Frequentist statistical inference cannot use
Bayesian updating as a model of inference because it has no probabilistic way of
representing prior beliefs or knowledge. In this case sense-making relates, as in
subjective objectivist schools, to identifying a state space that has the capacity to describe
the statistician’s or scientist’s perception of this world, but unlike the subjectivist
objectivist schools it does not extend to building sufficient understanding that beliefs or
knowledge about this space may be expressed probabilistically. Inference becomes a
case of defining functions of the data, e.g. estimators or test statistics, which are sensitive
in some sense to the region of the state space which best describes the external world.
There are a host of references in this area since frequentist statistics was the dominant
methodology for so long and so many statisticians contributed to its foundations; so I cite
only the encyclopaedic work of Stuart (2009).
The first three schools use the same methods to guide inference and are often perceived as
simply providing different justifications of the Bayesian approach. But it need be recognised
that sense-making needs to be explored and interpreted in different ways according which
philosophical perspective one takes. Thus exploratory data analysis will have a different
interpretation in each school. I have always found myself most comfortable in the subjective

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objectivist school. For me the world exists: I can model those parts of it that I understand
with mathematical models replete with parameters that have cognitive meaning. In other
words, the state space has existence – in a sense that I shall qualify below – before I ascribe
probabilities to represent my beliefs. My view of scientific induction is that one cannot avoid
the prejudices and influence of prior knowledge so I include it explicitly in my statistical
modelling, rather than implicitly as frequentists inevitably do.
I am making these points because I need to be clear that this perspective shapes my
discussion of statistical inference and subsequently decision analysis. If you adopt another
perspective, you will need your own explanations.
I interpret Bayesian statistics as a model of rational inference not a prescription of it (French,
1986; French, 2011). I believe that Bayesian analyses guide our inferences. They do this by
providing a model of rational inference in a model world. The model of inference reflects my
real beliefs about behaviours in the real world through the use of prior probability
distributions and likelihoods relating parameters and observations in the model world. Note
that these are never going to be perfect reflections of my beliefs in the real world. I doubt
that I am ever as perfectly rational as demanded by the Bayesian paradigm, and I certainly do
not have the infinitely fine levels of discrimination demanded of my judgements. But subject
to constraints of rationality embodied in the probability calculus, they are as close to my
beliefs as possible. Moreover, the process of elicitation helps my actual beliefs evolve
towards rationality (Phillips, 1984; French, 1986; French, 1988; French and Smith, 1997;
French et al., 2009). By exploring that model of rational inference, investigating its
behaviour as the prior and likelihood are varied a little, i.e. by using sensitivity and
robustness analyses (Rios Insua and Ruggeri, 2000; French, 2003), I gain further insight into
the real world and my beliefs about it. The Bayesian model of inference is no more than a
close metaphor for the cognitive process that I should like to follow; as with all metaphors, it
helps my understanding and communication.
It should be clear from my move to the first person active that I see the process of inference
as a personal one. Wider scientific knowledge reflects consensus among statisticians and
scientists which forms as more and more data accumulate and the likelihood dominates
individual priors leading to a common posterior (Box and Taio, 1973; Smith, 1984; Rios
Insua and Ruggeri, 2000; French, 2003). But I shall not discuss that here.

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Back to Cynefin and sense-making: since I see Bayesian analysis as a model of an idealised
statistician making inferences in a model world, the process of sense-making is one of model
building; but model building both of the real world and of me. At least, it is clearly so in the
knowable and known spaces; in the complex space things are, well, more complex. Since my
understanding of cause and effect in behaviours there is poor, any model of the real world
there will at best be simplistic, if possible at all. Moreover, the lack of repeatability means I
will seldom have experienced the same events and behaviours before, so I will be unclear on
both my judgements on what might be happening and on my values in how much I care if it
does. Thus building subjective probability, value and utility models will be challenging, and
will certainly require much introspection and thought, if possible at all.
The lack of clarity on cause and effect will mean that situations will have an element of
novelty to them. As a statistician/scientist I will need to explore what observations I have
and see if I can begin to discern patterns. This is the motivation for usingEDA. Tukey
(1977) suggested methods that were often pen and paper based and hence two-dimensional;
but the advent of easily accessible, powerful computer graphics and scientific visualisation
brought more sophisticated ways of exploring data (Chambers et al., 1983; Cleveland, 1994;
Zhang et al., 2010). Multivariate statistical methods, e.g. factor analysis, cluster analysis and
multi-dimensional scaling (Krzanowski and Marriot, 1994; 1995; Everitt and Dunn, 2001),
offer ways of transforming or representing data differently so that patterns might be more
visible; and data-mining, knowledge discovery and innovization techniques provide
potentially interesting and semi-automated ways of identifying patterns (Hand et al., 2001;
Klosgen and Zytkow, 2002; Deb and Srinivasan, 2006). Problem structuring methods offer
ways of exploring less quantitative data and perceptions (Rosenhead and Mingers, 2001;
Mingers and Rosenhead, 2004; Franco et al., 2006; Shaw et al., 2007; French et al., 2009).
However, whether one uses pencil and paper or the latest computer visualisation techniques,
initially sense-making involves looking at past experiences and seeking patterns in them. As
Gelman (2003) emphasises, even this requires some modelling, some understanding in order
to identify appropriate data. The first step of sense-making is necessarily an intuitive,
creative one involving recognition of putative cause and effect mechanisms; intuition is
something that generally cannot be proceduralised. Exploratory methods offer tools to help
me form my perceptions of behaviours sufficiently that I can construct models and
hypotheses to be evaluated against data in subsequent confirmatory analyses.

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The distinction between exploratory and confirmatory analyses is important, though hardly a
clear one. Sometimes confirmatory analyses can be used as ‘quick and dirty’ methods for
exploration: e.g., analysis of variance may be used to identify potential cause and effect
relations for more detailed investigations, taking significance levels with a ‘pinch of salt’,
indicative but not conclusive. Equally, an exploratory plot sometimes so clearly indicates a
relation that confirmatory statistical analysis is barely necessary. The point is that in the
complex space exploratory approaches must dominate. When and only when some regularity
in behaviour becomes apparent, can confirmatory techniques be applied. When such
regularity does become apparent, then the behaviour is close to being classified within the
knowable rather than the complex domain.
Looking back several decades, there was much discussion of whether Bayesians could adopt
exploratory analyses without compromising their ability subsequently to construct
appropriate prior distributions. A prior distribution represents knowledge before looking at
the data, but an exploratory analysis means that one does precisely that: look at the data. An
immediate response is that once I have sufficient understanding to build a putative hypothesis
or model, I should express my growing understanding within a prior distribution, design a
new experiment or survey, collect new data and analyse those to form the posterior. A more
subtle response, however, is to remember that I see Bayesian analysis as providing an
idealised model of inference. I learn from the analysis as one learns from a metaphor. It
brings insight, but that process of gaining insight is a tacit one, one that we can acknowledge
but not articulate explicitly. How I learn from the analysis will be moderated by how closely
I feel the idealised model of inference reflects the actual situation. When the prior is truly
independent of the data, I may feel a little more confident in my learning; when there is no
such independence, I should be less confident, but surely I should not entirely reject the
relevance of the analysis (see also Good, 1983; Gelman, 2003).
I have focused on sense-making as my understanding grows taking behaviours from the
complex to the knowable spaces. For the chaotic region, my understanding is so meagre that
even designing an exploratory analysis may be beyond me; even the simplest exploratory
analysis needs some prior understanding (Gelman, 2003). Thus any move from the chaotic to
the complex spaces is likely to be driven by more intuitive, tacit processes. Conversely in the
known space, my understanding is so substantial that exploratory analyses are usually
unnecessary. Indeed, confirmatory analyses are seldom needed; perhaps only to drive the
recognition stage of recognition primed decision making.

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These remarks cause me some concern
when I look at some of the literature today.
While I have explained my position from a
Bayesian perspective, many of the issues
also need to be addressed by those of other
persuasions. In particular, it is only in the
known and knowable spaces that one has
sufficient understanding to undertake
confirmatory analyses fully. In the complex
and chaotic spaces exploratory methods are
needed: see Figure 6. I agree that one may
use confirmatory methods as exploratory tools so long as significance levels are taken as
informative, but imprecise indications. Bayesians may even use frequentist methods in EDA.
In the complex space and certainly in the chaotic space, one has too little knowledge to build
probability models with any confidence; and any results from estimation, confidence intervals
and hypothesis testing are at best suggestive (Roberts, 1979; French, 1986). So I am
continually concerned by the prevalence of confirmatory statistical analyses, especially
hypothesis testing, in articles in many management and social science journals, including
such stalwarts as Management Science and the Academy of Management Journal. In most
cases the complexity of the issues in the Cynefin sense is beyond question. Yet precise
statistical analyses are conducted using complicated methods such as analysis of covariance
or structural equation methods, and the superficial implications of quantitative indicators such
as p-values followed mechanically. I repeat that I am not saying that all such analyses are
inappropriate; but, for me, many are.
To close this section, let me turn briefly to experimental design. Formal quantitative
experimental design methods should be used to design data collection, be it from experiments
or surveys, for confirmatory analyses. What concerns me comes prior to that. Before
beginning any investigation, I would use the Cynefin framework to locate the situation and
issues that I wish to investigate. If I conclude that my present understanding is such that they
lie in the complex space, I will lean to using exploratory methods to gain a handle on what is
going on, sufficient, I would hope, to bring them into the knowable space and allow me then
to conduct more confirmatory analyses. If, on the other hand, I already have sufficient
understanding to form a statistical model, i.e. if I perceive the situation and issues to lie in the
Figure 6: Cynefin, exploratory and confirmatory
analyses

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knowable space, then I may begin with a confirmatory analysis. Cynefin thus provides a
formalism in which to frame what we might call ‘qualitative experimental design’.
6 Decision analysis in the Knowable and Complex Spaces
I have always seen decision analysis and statistical inference as inexorably connected (French
and Rios Insua, 2000). As indicated in Figure 5, knowledge management is closely linked to
both: consider decision analysis in the context of Cynefin. We shall build on the discussion
of Figure 4 in Section 3.
To make a decision I need to form, draw together and balance two sets of judgements: what I
believe might happen and how much it matters to me if it does. In saying this, I realise that I
am not emphasising sufficiently the entire process from an initial, possibly ill-defined feeling
that I do need to take a decision, through formulation, analysis and appraisal to an ultimate
choice and implementation: see French et al. (2009). Here I simply want to focus on:
 forming and representing my uncertainties, i.e. what I believe might happen; and
 forming and representing my preferences, i.e. how much it matters to me if it does.
In doing so, I will concentrate again on situations in the complex and knowable spaces. In
the chaotic space decision making is, inevitably perhaps, intuitive and perhaps relies not a
little on luck until I have acquired enough understanding to move the issues into the complex
space. In the known space, decision making is mainly recognition primed, choosing
automatically along rehearsed lines without recourse to much analysis.
Following Bayesian prescriptions of decision making based on the subjective expected utility
model (French and Rios Insua, 2000; French et al., 2009), the analysis requires that I
construct subjective probabilities to represent my uncertainties and similarly construct (multi-
attribute) utilities to represent my preferences. In some cases, uncertainty may not be a
driving issue and I might represent and explore conflicting objectives in my preferences
through a multi-attribute value function (Belton and Stewart, 2002; French et al., 2009).
Whatever the case, I can only follow either of these routes in the knowable space, since they
require that I have sufficient understanding both of myself and the external world to build a
model, in which I have some trust. In the complex space, I might be able to use simple multi-
attribute models and perhaps very simple decision trees or influence diagrams in exploratory
ways, much as confirmatory methods of statistical inference can be used in exploratory ways.
But it is important that in doing both that I interpret the analysis informally and do not
myopically follow its prescription. Thus strategic decision making in complex situations may

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be guided by multi-attribute value analysis to set a broad framework for a way forward.
There are many examples of this. For instance, my work on long term decision making after
Chernobyl (French et al., 1992; French et al., 2009) involved only six attributes and no
modelling of uncertainty. I make no claims that it faithfully modelled either the world as it
was or the preferences of many decision makers and stakeholders. But it did serve to
articulate discussion and build a shared understanding among the decision makers of their
broad objectives in adopting long term remedial strategies. Often decision analysis within the
complex space may involve no quantification but relies on problem structuring methods and
soft OR (see, e.g., Friend and Hickling, 1997; Eden and Ackermann, 1998; Checkland, 2001).
Recently Theo Stewart, Jesus Rios and I have been exploring the interface between scenario
planning and decision analysis to explore issues in the complex space (French et al., 2010b;
Stewart et al., 2010). The approach seems to have potential to extend the application of
quantitative modelling. Essentially the idea is to recognise that while one may not have
sufficient understanding of the way that the world is working to build a single model on
which to base the analysis, one may construct a few scenarios, each embodying sufficient
assumptions to enable a decision model to be built within the scenario. If the scenarios are
chosen in an interesting way, then conducting decision analyses within each may be helpful
in bringing understanding and informing the decision. What might be interesting ways is a
moot point. Some methods have been suggested in the scenario planning literature (van der
Heijden, 1996), to which we have added some further suggestions. As in the scenario
planning literature, we would seldom make any claim that the scenarios ‘span’ or ‘partition’
the future; nor that they be equally likely in any sense. Rather they are a backdrop for
strategic conversations in which one may investigate the relative merits of alternative
strategies. For instance, one might choose one of the scenarios to represent a highly unlikely,
but totally catastrophic future to explore the robustness of different strategies.
Cynefin can also provide a general indicator of whether we might expect the decision makers
to be clear on their preferences a priori. Repeatability does not just lie at the heart of Science:
it has helped us think through and form many of our values – but far from all. It has always
concerned me that some decision analysts have sought to measure preferences, whereas I
have always sought to help decision makers think through, evolve and articulate their
preferences. I have always seen value and utility elicitation as a constructive, reflective
process not simply measurement. Cynefin has given me new insights into this distinction.

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In the known and knowable spaces, familiarity with similar circumstances means that I will
have explored and thought through my preferences: my judgements will be well rehearsed. I
will know what I want to achieve in any particular decision simply because I ‘have been there
before’. The same will be true of other decision makers. For analysts whose work relates to
contexts in these spaces, perhaps because they have tended to work with artificial intelligence
techniques, expert systems, recognition primed decision making and some of the more
operational areas of OR, it is perhaps not surprising that they think of preferences as
predetermined, waiting to be measured. Such is not the case in the complex or chaotic
spaces. Novel issues require decision makers to reflect upon what they want to achieve
(Slovic, 1995). The methods of value focused thinking and the exploration, evolution and
elicitation of values, weights and utilities (Keeney and Raiffa, 1976; Keeney, 1992; French et
al., 2008) lie at the heart of decision analyses in the complex space. As decision analysts we
need to work with our clients to help them deliberate on what their values are or, perhaps it
would be better to say, to help them contextualise their fundamental values to the
circumstances that they face.
Scenarios can be helpful in exploring preferences in the complex space, just as they can
support the exploration of gross uncertainties (Stewart et al., 2010). Firstly, their use can
help individuals address questions such as: “How will I feel about this in the event that that
happens?” But they can also help structure deliberations between different stakeholder
groups who may hold very different fundamental values. Cultural theory (Douglas, 1992)
suggests that in a society facing a complex issue, several distinct sets of fundamental social,
political, economic and ethical values may be observed: e.g.
 entrepreneurial values favouring appropriate risks if there are sufficient potential
benefits;
 hierarchical values seeking to control and regulate behaviours and risk;
 egalitarian values seeking to conserve the environment for future generations;
 fatalist values which tend to accept what the future brings.
By constructing scenarios, each reflecting one such set of fundamental values, analysts can
help stakeholder groups explore and debate how different strategies might play out against
worlds consistent with their own ideals and against other worlds consistent with other sets of
ideals. Such explorations may be carried out entirely qualitatively as in scenario planning or
with some simple quantitative decision analytics support.

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7 Conclusion
It is necessary to explore context much more than sometimes we do if we are to adopt an
appropriate form of statistical or decision analysis. Cynefin seems to offer a very catalytic
and supportive framework for achieving this. It encourages us to seek an appropriate balance
between qualitative sense-making and quantitative analysis. I hope that this paper stimulates
further discussion on this.
Acknowledgements
I have gained much from discussions with Tim Bedford, Emma Carter, Angela Dale, John
Maule, Alec Morton, Nadia Papamichail, Jesus Rios, David Snowden, Theo Stewart and
Doug White; and Carmen Niculae who helped me see the subtleties that the Cynefin model
can articulate. Although these ideas have developed over many years, three recent projects
gave them impetus: Sustainability Assessment of Nuclear Power: an Integrated Approach, a
research collaboration funded jointly by the UK Engineering and Physical Sciences Research
Council and the Economic and Social Research Council; Algorithmic Decision Theory, a
programme funded by the European Co-operation in Science and Technology; and Investing
in an Uncertain Future, a programme of seminars and workshops funded by the Scottish
Institute for Advanced Studies. I am grateful to all.
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