Using Complexity Theory to explore a Community of Philosophical Enquiry: A
Rationale and Implications for facilitation and research.
ABSTRACT
In this paper, I propose that Complexity Theory is relevant to the study of effective
communities of philosophical enquiry (CoPI). I first offer a rationale for this by
exploring the compatibility between the underlying tenets of CoPI practice and
Complexity Theory. I will argue that Complexity Theory provides a useful framework
for the analysis of change within a CoPI over time since it is closely aligned to the
learning theories and philosophical positions which underpin CoPI practice. I then
briefly outline the key features of Complex Learning Systems (CLS). CoPIs have been
described in Complexity Theory terms elsewhere so I then move on to discuss
implications for facilitation of such communities and for research into their effective
conduct.
The above considerations have consequences for the facilitation of CoPI. Some are
already built into the practice. In other cases, a greater concentration on the movement
between consolidation and novelty, for example, could be seen as shifting the CoPI
towards a critical state where significant change can occur. I will argue that such
change can be characterised by the emergence of learning outcomes at whole-group
level.
There are important implications for research if a Complexity viewpoint is adopted. I
suggest that the involvement of facilitators and students as participant, reflective
researchers is crucial and explore some models of action research and instruments that
are most likely to capture the necessary data for a complexity analysis. The ‘Dialogic
Inquiry Tool’ developed by Alina Reznitskaya and colleagues may be one such
instrument.
Introduction
Philip Cam (2011) argues that there is a strong alignment between the ideas
underpinning philosophical pragmatism and those of the community of philosophical
inquiry (CoPI) approach practiced in philosophy for children. He also suggests that it
might be difficult, therefore, for a CoPI to remain open to the full range of
philosophical ideas and arguments. In the first part of this paper I want to extend Cam’s
argument by suggesting that pragmatism and the CoPI can be considered as operating
under a distinct paradigm (in the Kuhnian sense) which is emerging within philosophy,
learning theory and ways of describing change and development in more general terms.
This paradigm (which I will refer to as the P-paradigm) is not yet well articulated. I
make the claim that it includes what has been called ‘Complexity Theory’. This
alignment is proposed as a rationale for describing CoPI in Complexity Theory terms.
Furthermore, following a Kuhnian argument, workers within the P-paradigm and the
traditional paradigm which underpins western philosophy (which I refer to as the Tparadigm) have difficulty communicating with each other because many of the concepts
used have a different meaning in each paradigm. This supports Cam’s conclusion about
the ‘restricted’ range of CoPI discussions. A Kuhnian position would suggest that there
is no neutral vantage point from which to compare discourse within the T-paradigm
with that of the P-paradigm. Philosophical communications are contained within one or
1
other of these paradigms. Hence the perception that the CoPI is restricted in its
application would appear to be an inevitability.
The second part of this paper attempts to describe the CoPI in complexity terms. I argue
that the advantage of this is that it can lead to the generation of hypotheses about CoPI
operation and effectiveness based on the success of Complexity Theory in other
contexts. A final move will be to discuss some possible hypotheses in the areas of
facilitation and of research into CoPIs if a Complexity Theory framework is invoked.
Of course the value of such an exercise (judged from within the P-paradigm) would
depend on whether or not it leads to better understanding of how and why an effective
CoPI operates and how to facilitate for such effectiveness. In other words, can these
hypotheses become ‘warrantable assertions’? I suggest that such an exploration would
now form part of a useful research agenda.
Part 1: The Case for the P-paradigm
A Different Paradigm of Learning?
Biesta and Burbules (2003) suggest that Dewey views knowledge as being in the form
of ‘warranted assertions’ which are intersubjective, reconstructive and transactional. I
suggest that these elements are indicative of a much wider network of understanding
about the status of knowledge and about learning and development in ‘open systems’1
reflected in a range of writing, not just philosophical. This network of understanding
could be referred to as a distinct ‘paradigm’ as described by Thomas Kuhn. Kuhn’s
work itself comprises a new view of the process of science is strongly resonant of
Philosophical Pragmatism. To follow Kuhn further, the switch to this P-paradigm (now
possibly in its early stages) could be characterised as amounting to a ‘revolution’ as
Kuhn himself describes. In this section I begin by summarising aspects of Kuhn’s work.
I then indicate some of the range of work which appears to subscribe to the P-paradigm
including that of Darwin, Bateson and Complexity Theorists.
Kuhn’s view of ‘paradigm’.
Thomas Kuhn’s book The Structure of Scientific Revolutions, published first in 1962,
argues that scientific activity is not about the gradual uncovering of the secrets of nature
through a painstaking work of observation and theory building. It is rather, a two-part
process where periods of ‘normal science’ are interrupted by the replacement of one
‘paradigm’ by another. A new paradigm, in Kuhn’s view, comprises a radically
different world view and a vocabulary which sets it apart from that which it replaces.
Such revolutionary change is relatively rare. Kuhn cites the move from a Newtonian
framework to an Einsteinian as one such. During the normal science phase the
implications of a particular world view are explored and articulated within the limits of
the current paradigm.
1
An open system is one which is in continuous interaction with its environment. The special type
of open system which is of interest here has been called a ‘Complex Adaptive System’ or a ‘Complex
Learning System’. This is one which displays a particular structure and function which is fully
explained in the next section.
2
Kuhn (1970) challenges the traditional view of the development of scientific
knowledge. He suggests that scientific truth rests in part on the consensus of the
community of scientists and is therefore intersubjective. Not that such truth is arbitrary
but rather that it depends on a network of concepts and understandings and that this
network can undergo radical revision. The triggers for such revision are problems
which arise during the articulation and extension of the prevailing paradigm. Kuhn’s
description is different from the traditional notion that science is a gradual lifting of the
veil, a process of an ever-more-accurate description of reality. Scientific knowledge
building can therefore be seen as reconstructive. The Kuhnian view is that scientific
hypotheses are created through numerous and diverse ways and are subjected finally to
the test of use in prediction and description, that is, it is transactional.
Dewey’s pragmatism
Turning now to Dewey, I follow Biesta and Bubules (2003) and identify the
intersubjective, reconstructive and transactional elements in his work. Taking the third
point first, according to Dewey, obtaining knowledge is about understanding the
relations between actions and consequences. This comes about through a continuous
interplay between mind and the natural and social environment. Mind, according to
Dewey, is not, as in classical Western Philosophy, to be seen as separate from matter
but, ‘Mind and the world are contained in one unanalysed totality.’ (Dewey, 1929, p18),
hence the reference to transaction. Without action, feedback and reflection no knew
knowledge is obtained. Knowledge building, under this description, is reconstructive.
We are always in contact with the environment and act according to frameworks of
understanding. This state is adequate until a problem arises with our present framework,
in which case a process of inquiry is triggered. The details of this are described
elsewhere.2 Rorty summarises the pragmatist view of inquiry:
pragmatists …cannot regard truth as the goal of inquiry’. ‘the purpose of
inquiry is to achieve agreement among human beings about what to do, to
bring about consensus on the ends to be achieved and the means to be used
to achieve those ends. (Rorty, 1999 , pxxv )
As indicated by Rorty, such consensus results from intersubjective engagement. Since
we are always immersed in a social milieu, our transactions are irrevocably social and
involve negotiations with other human beings. Over time this intersubjective web of
understandings has developed but is always open to the possibility of review and
reconstruction. Dewey prefers the phrase ‘warranted assumptions’ to ‘truth’. Warranted
in that they are backed by reasons and contained within a web of relations with other
such assumptions. Dewey does not restrict his analysis to the physical world. As Cam
(2011, p103) points out ‘Moral values are to be regarded as working values that face the
2
In short, Dewey Inquiry stages from Biesta and Burbules, 2003, p57 are as follows:
The occurrence of a problem
Its specification
Occurrence of a solving suggestion or supposition, hypothesis
Elaboration of suggestions or reasoning
Experimental testing
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tribunal of experience in much the same way as hypotheses in science’. Furthermore,
scientific activity is not restricted to justification based on ends only. Why, what, how
and when are all important aspects of inquiry no matter what the context.
There are a number of important features of Dewey’s pragmatism that place it in the Pparadigm. First, human actors are seen as an integral part of a dynamic, evolving
reality, the understanding and control of which they achieve through inquiry. The
Deweyan claim, according to Biesta and Burbules, is that ‘Inquiry doesn’t solve
problems by returning to a previous, stable situation but by transforming the current
situation into a new situation’. ( Biesta and Burbules, 2003, p57) Problems arise
through interaction with the physical and social environment in an unpredictable if not
random way. The inquiry process comprises a powerful method of increasing
coordination through hypothesis formation and selection. It is important that a range of
hypotheses can be subjected to experimental test. There will therefore be a large
number of ‘redundant’3 possible solutions. This serves the purpose of greatly increasing
the chance of real innovation. Decisions about which solution to adopt are made
collectively. They are continually open to revision. There are no final solutions since
solutions must reflect an ever-changing context. There is no form of external perfection
towards which subsequent solutions are approaching. A further important feature of the
P-paradigm following Dewey is that logic ‘originates in the operation of inquiry.’
(Dewey, 1938, p4) and is not therefore to be seen as feature of an external world.
Evolution and learning in open systems
I move now to briefly consider other works which appears to operate within the Pparadigm. These are focussed on the description of open systems. An open system is
one which is in interaction with its surroundings and such interaction is a key feature of
the P-paradigm. Living organisms are part of open systems and so are some non-living
entities. Examples of P-paradigm writing are becoming more common. Some suggest
that Darwin’s Theory of Evolution was a key marker in the development of such
thinking. For example, Dewey (1910), claims that Darwin’s work on the theory of
evolution overthrows the notions of fixed external goods and guided or designed
change. More recently in this vein, Gregory Bateson (2002) suggests that change in an
open system is driven by two important features. A form of random generation and
some selection process which operates on the products of this randomness. Charles
Darwin showed that the evolution of biological species adheres to this model. Mutation
of genetic material provides the random element and natural selection provides the
sifting mechanism. For the bulk of biological time evolutionary development has been
driven by the laws of physics and not by intentional agency. Such a mechanism
operates at the level of genetics.
With the advent of consciousness, according to Bateson, a second selective mechanism
becomes available. This so-called ‘somatic’ process can provide advantage for a
particular organism when it makes a decision to develop an attribute or skill. The
advantage gained, however, is not genetically transmitted to the next generation
3
The term ‘redundant’ is used because it features in Complexity Texts. Theorists in this area view
the multiple possibilities as essential to progress hence redundant does not in this case mean
‘unnecessary’.
4
(although the desire and opportunity to independently develop such attributes may be
inculcated and culturally transmitted). Bateson further suggests that learning , like
evolution, has a random and a selective element. Basic, trial and error learning involves
selection by successful outcome. With the development of consciousness, and in
particular of language, scenarios can be acted out or run through as thought
experiments. Furthermore, successful strategies can be stored and revisited. The
selective element is to a greater extent under the control of the learner. The move to
somatic selection parallels the Deweyan view that intelligence is essentially about the
development of inquiry.
Towards a definition of the P-paradigm
The P-paradigm highlights interaction, cultural and intersubjective processes and the
importance of self-regulation rather than the imposition of control from outside. Biesta
and Burbules suggest that pragmatism is:
limited to a paradigm of humans as living organisms continuously
adapting to an ever-changing environment in which their interactions with
other living organisms and their environment results in the release of new
potentialities, most notably consciousness and self-awareness. (Biesta and
Burbules, 2003, p112)
Bateson’s discussion of change in open systems requiring a random and a selection
element provides a starting point for an articulation of the P-paradigm. In the spirit of
this paradigm, he and others (noteably Daniel Dennett, 1993) suggest that
consciousness and somatic selection can emerge, over a long period of time, out of such
a mechanism. According to Stuart Kauffman (1993) and others, this is more than can
be accounted for by natural selection alone. They introduce the idea of spontaneous
emergence. The Darwinian evolutionary aspects of this paradigm are now well
accepted. What is more controversial, however, is the idea of the spontaneous
emergence of more sophisticated structures out of those of lower complexity. There is
not space to deal with this adequately here, however there is a substantial and growing
database of instances of order arising spontaneously. This is essentially the province of
Complexity Theory. I argue that this is a central principle of the P-paradigm and is
implied in an account of the development of intelligence such as Dewey’s.
Complexity Theory focuses on the conditions under which this spontaneous emergence
occurs and hence acts to complement the change mechanisms described by Bateson.
The rest of this paper begins by describing some of the development of Complexity
Theory and its application within in a range of open systems both organic and
inorganic. I then focus on the possibility of utilizing Complexity Theory as a
description of CoPIs and possible implications for doing so.
Part 2: Complexity Theory and the CoPI
Emergent behaviour and Complexity Theory
Complexity Theory (CT) arose from a study of physical contexts such as weather
systems but has been applied to a range of physical and biological, open systems. It
5
maintains that higher order effects can spontaneously emerge out of activity at a lower
level. The Complexity Theorists Prigogene and Stengers (1984) suggest that Self
regulation and transformatory change are predominant features of the universe.
Systems, whether they be living or non-living can exhibit spontaneous jumps to a new
level of increased order at a critical point. 4 Stuart Kauffman (1993) argues further that,
under the right conditions order arises naturally from an interaction of forces. He
suggests that this is driven by the interplay of constraining structural stability and
liberating forces operating within and challenging those structures. Such emergent order
is in addition to the mechanism of natural selection. Leaving the history of Complexity
Theory to one side I wish to explore how it has been utilised in the description of social
groups and further, how it might have application to CoPIs. To better understand
Complex Systems I offer two descriptive analogues. The first helps to describe the state
of systems in which emergence is likely to occur. The second provides an example of
events which are unpredictable in principle and yet part of a causal process. This second
point highlights the need to describe complex systems using P-paradigm vocabulary
and techniques rather than those of classical western science.
Two descriptive analogues
Tight/Loose structure
A key idea in CT is that important events tend to happen at the edge between order and
disorder. The structure here can be described as tight/loose. When ice melts there is a
point where clumps of ice crystals are free to move past each other. There is still order
but not tightly bound. This semi-liquid state has properties which are different from
either the liquid or solid form. It is unstable in that a slight change in temperature can
move it into either the solid or liquid state. This idea of being ‘far from equilibrium’ has
been labelled ‘the edge of chaos’ and indicates the productive situation brought about
by a certain tension between looseness and control. The insight of CT is that the
tendency to disorder within complex systems is matched by a tendency for order to
emerge. Complex emergence is ‘on the edge.’ (Waldrop 1992 p12)
Davis and Sumara (2006) maintain that complex phenomena are often linked together
in what they call scale-free networks. These are networks ‘whose organization might be
described in terms of the nested, scale independent qualities of fractal forms.’ (Davis
and Sumara, 2006, p49). Such networks are decentralized where nodal groups are
nested within larger networks. The nodal groups operate according to local rules. They
are not controlled centrally but can be viewed as part of a larger framework where
novel behavior emerges (hence the label ‘scale-free’). Returning to the melting ice
analogy. The nodal groups in this network are relatively tightly bound but capable of
taking part in a larger, collective behavior. Scale-free (decentralized) networks have the
advantage of efficient interchange of information, reasonable redundancy and yet
retaining local diversity. These are characteristics of systems that learn. The tight/loose
structure of an effective CoPI appears to fit this description, moved towards this state
through appropriate facilitation.
4
This might be considered as a counterpoint to the second law of thermodynamics which
highlights the decent to disorder of naturals systems.
6
Figure showing comparison of centralized, diverse and distributed control, reproduced
from Davis and Sumara 2006, p 52.
Causal Triggers and Unpredictability
A second analogy alludes to a different view of causality from that in classical science.
An exemplar of this is the sand pile. Pouring sand in one place creates a cone-shaped
pile which, once it reaches a critical size, starts to experience slippage. Small slips
occur frequently, large slips infrequently. Roughly speaking 10 times larger slips occur
10 times less frequently over a period of time. There is no way to tell exactly when and
where a slip will occur or whether it will be large or small but the power-law above
appears to be remarkably consistent across such scale-free phenomena. The ‘causes’ of
slippages are best described as triggers in a situation where something is ready to
happen.5 The slippages are caused and yet their occurrence is inherently unpredictable.
This illustrates another CT insight, that effects at one level correspond to the emergence
of effects at others. In terms of education these levels might include individual learners,
groups within a class, the whole class, the school, the school district. All of these could
be classified as complex learning systems (CLS), (the teacher/facilitator also is a CLS).
All of these interact and often the effects of behaviour at one level emerge at another.
The unpredictable nature of CoPI outcomes at individual and whole-group levels shows
similarity with this physical model.
5
The distribution of events in complex contexts often does not follow a ‘normal’ curve since
such events are connected to and interact with each other. For example, the occurrence of
earthquakes depend on pressures and weaknesses in the earth’s crust. A power law describes
how larger events occur considerably less frequently than smaller events
7
Key features of CLSs.
Given that Complexity Theory holds some promise for the description of CoPI, I now
turn to one such application in a social context. Specification of detailed characteristics
may suggest areas to observe as a CoPI develops. Davis and Sumara use Complexity
Theory to describe social groups. I reproduce their description of Complex learning
System below.

They are; Self-organized – complex systems/unities spontaneously arise as the
actions of autonomous agents come to be interlinked and co-dependent,
 They exhibit; Bottom-up emergence – complex unities manifest properties that
exceed the summed traits and capacities of individual agents, but these
transcendent qualities and abilities do not depend on central organizers or overarching governing structures,
 Are characterized by; Short-range relationships – most of the information
within a complex system is exchanged between close neighbours, meaning that
the system’s coherence depends mostly on agents’ immediate
interdependencies, not on centralized control or top-down administration,
 They exhibit; Nested structure (or scale-free networks) – complex unities are
often composed of and often comprise other unities that might be properly
identified as complex – that is, as giving rise to new patterns of activities and
new rules of behaviour,
 They are; Ambiguously Bounded – complex forms are open in the sense that
they continuously exchange matter and energy with their surroundings,
 They are; Structure determined – a complex unity can change its own
structure as it adapts to maintain its viability within dynamic contexts; in other
words, complex systems embody their histories- they learn- and are thus better
described in terms of Darwinian evolution than Newtonian mechanics, and
 They are; Far from equilibrium – complex systems do not operate in balance;
indeed, a stable equilibrium implies death for a complex system.6
(from Davis and Sumara, 2006, p5
What promotes emergence within complex systems?
In the Complex Learning Systems (CLSs) described by Davis and Sumara, progress can
be measured by the emergent patterns and undertandings seen as a product of their
activity. Davis and Sumara summarise the factors affecting the likelihood of emergence
in a CLS which will also provide useful pointers to CoPI facilitation and practice. I
paraphrase the factors below below as:
 the levels of redundancy and diversity appropriate to the situation the CLS finds
itself in; redundancy in this context means summarizing, restating and
generating alternatives. Diversity involves the variety of novel ideas and
6
James Nottingham (2010, p188) following Butler and Edwards, suggests that during a
problematizing phase, dialogic enquiry enters ‘the pit’ and then ascends to a level of fuller
understanding. Complexity Theory suggests that this ‘far from equilibrium’ state is more of a peak
or series of peaks where dialogue can lead to a forward holding state or backwards to previous preunderstandings.
8



approaches developed through critical and creative thinking (Nadia Kennedy
calls this ‘emergent diversity’. 2012, p 16) ;
translevel interactions, decentralized control with a certain density of significant
interactions between neighbouring participants;
a tight/loose structure, ‘enabling constrains’, promoting self-reference;
rich connectivity and healthy information flow including positive and negative
feedback.
Other writers note similar features, Stacey does so in the context of management.
The three all-important control parameters that drive complex adaptive
systems are: the rate of information flow through the system, the richness of
connectivity between agents in the system, and the level of diversity within
and between the schemas of the agents.
(Ralph Stacey cited in Mason 2008 p10/11)
A caveat is that not only must there be neighbour interactions, there must be a sufficient
density of such interactions. This is not easy to achieve and rarely is in traditional
classrooms. ‘It would seem that conceptual possibilities are rarely crowded enough in
mathematics classrooms to give rise to rich interpretive moments.’ (Davis and Simmt
2003, p157). Given that this is the case a key question is how can such density be
achieved in a CoPI. This is followed up in the next section.
Implications for facilitation of CoPI
CoPI have been described as CLSs elsewhere7 so I will I will focus here on to what I
see as implications for facilitation and research. The subtle role of facilitation in a CoPI
is noted by many writers. From a Complexity Theory perspective, ‘the act of teaching is
a sort of emergent choreography’. (Davis and Sumara 2006, p100)8. Describing a CoPI
as an open system David Kennedy (2004) identifies three major characteristics as
ambiguity, contradiction and noise. He argues further that the facilitator acts as a bridge
between concepts and arguments and as a trigger for conceptual system transformation.
He suggests that, in a community of Inquiry discourse ‘the development of the
‘argument’ is chaotic but has a direction. It proceeds nonsequentially, relatively
unpredicatably and irreversibly.’ (Kennedy, D. 2004, p749). The facilitator has to ‘feel’
the system, according to Kennedy. ‘above all, paying attention to the elements of the
structure that are in contradiction, for it is these elements that represent its
transformational potential.’ (Kennedy, 2004, p 758). This can be seen as a clear
reference to the way in which the facilitator focuses the density of interactions and
guides the CoPI towards a far-from-equilibrium state using consolidation on the one
hand and problematisation on the other. As the CoPI develops these moves are made by
the participants with little intervention by the facilitator, the CoPI self-organises.
7
See Kennedy, D and Kennedy N 2010; Kennedy, N. 2012; Cunningham, 2012.
Teachers often say that their job is about ‘providing the environment for learning to take
place’. In essence, this is an acknowledgement that learning arises from a complex set of
interactions between the learner and other learners, the environment and various sensory
inputs. The act of learning something appears to be the archetypal behaviour of a CLS.
8
9
The source of stimulus material used to provoke inquiry can also be seen as critical. In a
recent book Haynes and Murris (2012) argue convincingly that selected picturebooks
promote ‘imaginative philosophising’ which integrally engages the emotions and
intellects of participants.
Exploring stories with others in an environment that actively nourishes and
encourages talk about thinking and emotions helps learners (and teachers) to
construct complex self-narratives and understanding of others. (Haynes and
Murris. 2012. P 98)
I summarise some of the important considerations for teaching and facilitation in order
to promote an effective CoPI utilising a Complexity Theory perspective:
1) The facilitator/teacher is an integral part of the network and is herself a CLS.
2) Learning goals and observations of learning cannot themselves form an agenda
for learning. Since learning is emergent there is a need to focus on the
conditions for effective emergence. William Doll suggests that a curriculum
designed according to Complexity Theory should be conversationally rich,
recursive, relational and rigorous. Following this, the scheme of work should
have the right amount of problematics, perturbations and paradoxes (Doll 2012
in Trueit, 2012 ed., p165). Dewey notes that learning is not a linear process but
the result of recursive and reflective activity. It is a common educational
mistake, Dewey suggests, to start with definitions. (Dewey. 1910a p98)
3) In a well structured and facilitated CLS the learning is about collaboration and
co-construction and about the individual as a respondent and co-constructor.
4) The facilitator’s task is not simply to develop the learning of the individual but
to attend to the development of a community of learners within a learning
community. Care must be taken not to over-organize learners and the learning
context since the emergence of learning structures is as important as the
emergence of ideas. It is important that participants pursue a deliberative
democratic agenda and have control of decisions about the focus of enquiry.
5) The conceptual building blocks of learning are not simply linguistic but rest on
prior, internal state of a learner, activity and experience, social interactions,
emotional responses and other interactions with the environment.
6) Facilitators need to take account of factors that promote emergence such as;
diversity, redundancy, decentralised control, proscriptive rather than
prescriptive rules and the density of significant neighbourhood interactions. As
Kennedy (2004) suggests, facilitators need to focus on the interplay of diversity
and redundancy as a driver to a far from equilibrium state where the community
operates in a collective zone of proximal development. Supportive of these
ideas, Haynes and Murris (2012) point to the importance of fallibility,
imperfection, uncertainty and diversity as features of ‘imaginative
philosophising’.
7) The stimulus used to provoke inquiry is a critical consideration. (see Haynes and
Murris, 2012, p 120 for characteristics of suitable picturebooks)
10
8) An important motivational, facilitation and observational task to promote
development is to note triggers to the transformation of learning within the
CoPI.
9) On the matter of ensuring sufficient density of important interactions and ideas
it might be worth considering breaking up CoPIs into more small group activity,
coming back together at various stages (something like a CASE CAME model
of episodes of learning). We do spend time on question forming and choosing
but perhaps some research could focus on this issue. We also spend time on
work with concepts but perhaps more could be done to engage the whole learner
in concept exploration through activity, metaphor, visualisation etc. Do we need
to consider ways of freeing up the CoPI circle model? Or at least experimenting
with this?
10) The practice of CoPI is clearly distinctive and revolutionary. David Kennedy
notes that such practice may be counter to mainstream British/North American
education. I propose that this is because it operates within a different paradigm.
Implications for research
I propose that Biesta and Burbules define the importance of research from within the Pparadigm when they suggest that:
the point of research should be to help and support what takes place in
human practices, not in order to say what should or should not happen, but
rather, to enable those who engage in human practices – including the
practice of education – to achieve what they think should be achieved.
(Biesta and Burbules, 2003, p96)
As such, the practice of research appears to have much in common with a CoPI. Other
authors describe CT research in ways which resonate with CoPI practice. For example,
Kuhn and Woog in describing ‘coherent conversations’ as a research technique noting
that they have the following characteristics. They:
 Bring in information from inside and outside the system.
 Are permissive.
 Make the discursive process as evident as the thematic content.
 Are intuitive as well as logical.
 Have recourse to ethics.
 Are self-reflective.
 Seek to heal. (Kuhn and Woog 2007)
It will be useful to draw on the experience of those who have undertaken research into
social contexts underpinned by CT. Morrison claims that Complexity theory places:
Emphasis on networks, linkages, holism, feedback, relationships and
interactivity in context, emergence, dynamical systems, self-organization
and an open system (rather than the closed world of the experimental
laboratory). Even if we could conduct an experiment, its applicability to
ongoing, emerging, interactive, relational, open situations, in practice, is
limited. It is misconceived to hold variables constant in a dynamical,
11
evolving, fluid, open situation. What is measured is already history.
(Morrison 2008, P28)
According to Arrow et. al. ‘the implied research programme is that we examine
groups as embedded in and interacting with multiple contexts, over time, and under
varying conditions’ (Arrow et. al. 2000 p249). And further, ‘There is a need to
study the dynamics of the internal and global variables by tracking the trajectories
of both over time using qualitative and quantitative measures.’ (Arrow et. al. 2000
p45). A key research strategy proposed by Arrow, et. al. (2000) includes naturalistic
field studies comprising comparative case studies over time which:
 track evolution of emergent/global variables.
 describe overall system, requiring development of a richly articulated
database,
 involve contextual realism, that is, groups working on projects that have
real consequences for the members.
This research agenda conflicts with the classical model of scientific research. The
alternative model is further articulated by Hatt (2009). Hatt describes the importance of
analysing positive and negative feedback loops in complex systems, the first tending to
expand and the second to contract the system. According to Hatt
Analysis … proceeds by:
1) identifying key activities or components;
2) establishing the way they are linked in mutually causative loops;
3) identifying whether the loop is equilibrating (negative feedback) or
escalating (positive feedback); and
4) assessing the overall pattern of the system.
(Hatt, 2009, P336)
The above begins to point to ways of developing research into CoPIs based on
Complexity and P-paradigm principles. I begin to summarise these principles
below:
 Research into complex systems cannot rest on predictability and replicability. A
different paradigm is required which focuses on nodes, linkages locally and
across multiple levels, all which interact and influence on another.
 The collective is a learning system and so are the constituent elements.
 The researcher is a CLS and an integral part of the field of study. She/he can be
described as ‘complicit’ in the outcomes.
 The focus is more on enabling conditions and on effects rather than causes.
Effects are caused and yet strict causal sequence is indeterminate due to
complex interaction of elements. Thus patterns can be identified over time but
not predicted in the way of classical science. Context is crucial and it is more
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



appropriate to talk in terms of the ‘narrative of development’ and ‘emergent
features’ rather than cause and effect.9
Such effects need to be studied at the level of their emergence rather than
‘reduced’ to a lower level.
Experiments involving matched and stabilized factors are unlikely to be
appropriate.
More appropriate will be multiple, pluralistic, participatory and partnershipbased approaches based on case studies, interactionist and interpretive accounts.
Utilising action research techniques which have strong connections with
complexity theory, particularly approaches which highlight double-loop
learning. (see Davis and Sumara 2005). Techniques of ‘noticing’ (Mason, 2002)
and ‘philosophical listening’ (Haynes and Murris, 2012) developed within the
practice of CoPI are likely to be applicable also to the research of the practice.
Looking to a Research Agenda
The development of the P-paradigm may be triggered by dissatisfaction with Tparadigm philosophy and the applied fields educational study and curriculum design. A
key implication of this view of paradigm shift as revolutionary change is that Tparadigm educationalists and politicians do not understand the new vocabulary. For
them there isn’t a problem of underlying aims and processes, perhaps only of efficiency
in their terms. The ball is in the court of P-paradigm thinkers since they are
experiencing the cognitive dissonance. This underlines the importance of a
comprehensive research agenda to produce warrantable assertions. There are many
useful techniques which have been applied in the study of CoPIs. I suggest that it would
be valuable to explore these in relation to the use of Complexity Theory in other
contexts particularly social contexts. For example, Alina Resnitska (2012) is developing
a ‘Dialogic Inquiry Tool’ which analyses group interactions and may offer a rubric for
capturing the Complexity development of a CoPI. Alina is currently using the DIT in
research into CoPI.
It is possible that the practice of CoPI is itself a research agenda, using philosophical
moves to develop and critique the basis of learning as an emergent process. To this
extent we all need to become philosophers, but not in the academic sense. To become a
community of learners about learning within learning communities, might be our aim.
The CoPI as practiced by the Philosophy for children/communities movement is one
way of making progress with this and brings us closer to the aim John Dewey himself
had for philosophy.
Philosophy recovers itself when it ceases to be a device for dealing with
the problems of philosophers and becomes a method, cultivated by
philosophers, for dealing with the problems of men.( Dewey, J. 1917 The
Need for Recovery of Philosophy. quoted in Talisse and Aikin, p138.)
References
9
Note that it is not unusual for descriptive techniques to differ at different scales. Hence
reasonable that classical causal models can work well for billiard balls but not when describing the
behavior of conscious beings.
13
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