Critical Emergence:
Complexity Science and Social Policy Will Medd Do we really need to look at
complexity science to understand and practice social policy?
‘Surely it’s just a fad’, I hear you say,
‘there’s nothing really new’. There may be
some truths here. There may be a sense in which complexity
science is a fad. It may well be that some of what is
offered has been seen before. However, as the articles in
this special edition demonstrate, there are a range of
questions that complexity science invites us to ask,
questions about our underlying assumptions as well as
questions about how we think of future possibilities for
social policy as an academic discipline and field of
practice. We are not offering an uncritical translation of
complexity science onto the world of social policy. The aim
is to explore and develop the insights of complexity science
in the specific area of social policy, to explore the
specific implications of complexity science for social
policy which goes hand in hand with questioning the
relevance of some of those claims. What, then, is on offer?
What is at stake? Let’s consider the
extreme position. Douglas Kiel, in ‘Managing Chaos
and Complexity in Government’ (1997), argues we are
living in times of rapid change and complexity, times which
create increasing demands and pressures on public managers.
He suggests the traditional assumptions of public
management, based in particular on ideas of bureaucratic
organisation, assume processes of incremental change and
goals of stability and order. These are no longer feasible,
Kiel argues, and public sector managers need to find ‘a
new worldview, a new intellectual framework, a new
paradigm’ for ‘creating government organizations
capable of qualitative and transformational change in
performance and service delivery’ (p.4). And where
should this worldview come from? He argues it is to be found
in the natural sciences where ‘the new scientific
paradigm teaches us that uncertainty, instability, and
unpredictability are essential to the creative processes of
nature’ (p.4). This is the ‘sciences of
complexity’, that is ‘complexity science’
about which there are a range of commentaries (Brockman
1995; Capra 1996; Casti 1994; Cohen and Stewart 1994; Glieck
1987; Hall 1992; Lewin 1993; Prigogine and Stengers 1984;
Waldrop 1992). This is the extreme position in so far as the
assumption is that research in mathematics and the natural
sciences which has generated a paradigm called
‘complexity science’ offers a way of understanding
the social world and the world of organising social policy.
This position privileges particular sites for understanding
complexity (namely mathematics and natural science), and
down plays, as Hayles (1989) has argued, the ‘cultural
field’ in which ‘chaos’ has emerged as a
positive force, a shift, in Thrift’s (1999: 53) words
to ‘a great sense of openness and possibility
concerning the future’. Nonetheless the extreme
position has been powerful and it is hard to overestimate
some of the claims made upon complexity science,
particularly by popular science writers. Brockman, for
example, suggests these insights from complexity science
'will affect the lives of everybody on the planet' (Brockman
1995: 19). In a similar vein, Capra argues the developments
of complexity science offer 'a new perception of reality
that has profound implications not only for science and
philosophy, but also business, politics, health care,
education, and everyday life' (Capra 1996: 3). But it is not
just popular science writers who celebrate in this way.
Within social sciences, Kiel and Elliot (1996) argue that
the gap between the disciplines of science and social
science can no longer be understood in terms of the
differing complexity of phenomena under investigation.
Khalil and Boulding (1996: xii) wish to add to what they see
as the 'expanding choir calling for the relevance of natural
sciences (both physical and biological) to the study of
human action, cultural institutions, and social
organization', though they argue equally for the relevance
of the social sciences to understanding the natural world.
The opening must be, they say, both ways (see also Byrne
1998). The 'expanding choir' has even been taken up in more
formal initiatives, for example through the Gulbenkian
Commission on Restructuring the Social Sciences
(1996). The claims of complexity
science can, it seems, be totalising. We are offered a way
of seeing the world that will transform our understanding of
phenomena while transforming our ways of being, acting and
organising. This carries a number of assumptions. Not only
does it assume some sort of coherent and ordered body of
knowledge which forms a ‘worldview’, but it also
positions other possibilities as coherent, problematic, as
not up to the job. This is the extreme assumption:
complexity science can explain everything and replace all
that has gone before. Not only might we explain how the
dynamics of, say, a neighbourhood are working, but we can
also explain the dynamics of the policy organisations which
interact and intervene in that neighbourhood. To explain
everything is, of course, the appeal of complexity science,
but it can also serve to repel. For the discourses tend to
posit not a way of looking at the world to be
compared with other possibilities, but the way of the
world. We are invited not to explore possibilities for
understanding the world but to see how the world really
works. We are invited into, in Kiel's (1997 p.16) words
‘a nonlinear paradigm for a nonlinear
world’. The assumptions of this
extreme position are problematic but this need not lead to
absolute, totalising, rejection for there are possibilities,
possibilities beyond either form of totalising claims.
Indeed, we need to pay particular attention to the arguments
that non-linearity implies that knowledge is local. Hence
for complexity science, there can be no universal and
totalising laws in a complex world in which what is real is
founded on non-linear interaction. All knowledge is local
and contextual and so complexity science becomes a
meta-account, a general description of how processes work.
It is not a reductionist story with first principles from
which everything else can be derived. This opens up
possibilities. First, ‘complexity science’ is not
given. What complexity science is, is open to question, as
recent contributions to Emergence highlight
(www.emergence.org). And so, when we look at the
implications of complexity science to social policy, as in
other fields, we can maintain recognition of the important
specificities of that world (Hayles 1989). Second, it is not
surprising then that authors from within the social sciences
(a foundational discipline for social policy (see Byrne,
this edition)) dispute what the implications are.
Shared, to a degree, is the rejection of an
anti-foundational relativist postmodernism and agreed is the
essentially local character of knowledge, but disputed
– though arguably a matter of semantics - is whether
instead we have a reconstruction of science and social
science (Eve et al 1997), an ‘absolute and unregenerate
progressive modernism’ (Byrne 1998: 158), or ‘the
postmodern condition, characterised by a multiplicity of
representation and heterogeneous discourses which represent
a recognition of complexity, but a condition which is
relational, which is self-organised, one far from
‘anything goes’ (Cilliers 1998: iix). In the
traduction from the natural sciences to the social sciences,
then, we are not dealing with a simple move, a simple
translation. Rather, when we explore the implications of the
complexity sciences for the world of social policy, we
cannot fix either complexity science or social policy. Both
are negotiated, are relational, both are open to
possibilities. It is an exploration of
possibilities that motivated the workshop at which the
articles in this contribution were first presented. But why
should we even enter in? Why should we be interested in
complexity science at all? Well, sticking with the extreme
position for a moment it may be the case that, in the
context of such totalising claims, there is a need to voice
resistance, to offer alternatives and, which is important,
to demonstrate the limits of those claims. In the worst
scenario, no critical engagement with complexity science
renders the possibility of being disempowered when faced
with claims by advocates of complexity science about how
social policy should be understood, organised and evaluated.
A critical engagement also offers other possibilities. To
dismiss the value of any engagement means to dismiss the
opportunity afforded by, on the one hand, an interrogation
of the deeply embedded assumptions of social policy and its
methods, and on the other, to examine what useful insights
might be on offer. And yes, it may be that in exploring
these possibilities we go over old ground, where nothing new
appears, and we have heard it all before. However, it seems
more than ever before that we need to explore the old
ground, to find it again, to explore and challenge the
assumptions of foundations which may be deeply buried and
out of sight. In this editorial, then, I
neither want to lay down the assumptions of social policy
(in practice and as an academic discipline) nor the insights
of complexity science. To do either of these would be to
deny what motivates this engagement: complexity. It would be
to deny the complexity of social policy and the complexity
of complexity science. It would be to deny the many ways in
which both social policy and complexity science are
performed and the many relations they form. It would be to
deny that the world is open. Instead then, let’s keep
social policy and complexity science and the assumptions
they carry with them, open. They remain open in the sense
they can both be shaped. What I want to do then is overview
the contributions of this volume in order to highlight the
ways in which the relationship between complexity science
and social policy can meet, to explore what they tell us
about complexity science, about social policy, and about the
relationship between them. Complex
Contributions Dave Byrne calls for social
policy to move beyond its use of ‘a ragbag of
methods’, not adopting ‘solipsist
relativism’, which he attributes to postmodernism, but
instead taking a second option, namely, complexity science.
He outlines key ontological (how the world is) and
epistemological (how we understand it) features of
complexity science, emphasising that complex systems are
characterised by non-reducible properties, recursive
interactions, emergence, nonlinearity and evolutionary
dynamics. Byrne identifies three key implications of
complexity science for social policy. First, systems are
nested and intersect and interact with each other, not
hierarchically but with ‘flows in all possible
directions ‘. Second, understanding complex systems
means recognising agency matters and that agency is
recursive, humans change ‘social’ and
‘natural’ systems and they do this with an
understanding of them. Third, complex modelling can
contribute to such understanding by exploring ‘the
range of future possibilities understood as discrete,
different and multiple but not limitless’. Note though,
modelling here is not taken in the sense of mathematics or
simple agent (game theoretical) simulations, nor are the
models being seen as the ‘real’ thing, but rather,
as a way of representing the world, one in which
‘textual description and interpretation should form a
part’. Developing appropriate methodologies has been
important to Dave’s work generally (e.g. Byrne 1998)
and this article is no exception where he argues that
complexity requires multi-dimensional approaches, for
example ‘cluster analysis’. His argument is
situated in examples of welfare regimes, mental health care
and measurements of poverty. He concludes stating that
complexity is ‘a frame of reference – a way of
understanding what things are like, how they work, and how
they might be made to work’. Malcolm Williams suggests
that recognition of complexity challenges traditional
approaches to explanation and prediction. Drawing on the
example of homelessness, he suggests we need to explore how
‘homelessness’ is not one thing, for there are
‘a range of heterogeneous characteristics that give
rise to a wide range of symptoms that we term
‘homelessness’’. Further, those symptoms are
‘a manifestation of social complexity and that the
emergent properties of that complexity are real’. For
the individual, the outcome of homelessness is an emergent
and real property of a set of ‘nested probabilities of
outcomes’. Rejecting multivariate methodology which
assumes variables occupy fixed co-ordinates in time and
space, Williams argues there is a need to ‘make sense
of a plethora of antecedent conditions giving rise to a
complex range of outcomes’. Drawing upon Poppers’
idea of single case probabilities, Williams argues that the
‘notion of nested probabilities has enormous
implications for the way we think about reality and
complexity’. Highlighting the role of the individual
and the emergent system, Malcolm then explores the
possibilities of understanding ‘homelessness’. He
suggests three: homelessness is simply a taxonomy (in which
individual cases have interactions quite independent from
each other); homelessness has the properties of a complex
system (where individual cases interact producing emergent
properties, for example a set of housing policies); each of
the first two possibilities may operate under different
conditions, and their difference may depend on the
antecedent conditions (there may be a mixture of both).
Williams concludes stating how ‘a propensity to
interpretation of probability is not only conducive to a
complex approach, but also suggests a quite different
methodological approach’, an approach in the early
stages. Phil Haynes paper focuses on
the possibilities of using quantitative data and argues
there is a need to reassess ‘the relationship between
quantitative methods and the management of complex policy
environments’. Complexity science, he argues, calls for
a multidisciplinary approach, it challenges traditional
approaches to causality and association, encourages holistic
thinking, and permits
policy analysts to honestly face the limits of their
discipline. Haynes then
makes the case for methodological pluralism which requires
‘judgement’ to ‘describe social
complexity using a range of mathematical tools’. As a
starting point in our search for appropriate methods, Phil
notes that complexity already implies ‘two
dimensions of quantitative security’,
namely ‘time and space’. Critiquing linear
cross-sectional models (e.g. regression analysis and factor
analysis), he suggests that models should be re-run in
different circumstances, ‘to test their robustness with
different techniques, over time and with different
data’. Such ‘explanatory models’ can be used
inductively, as ‘an extension of data exploration, to
explore the possible interconnections’. The
answers will always be partial, he writes, because
‘micro and localised study needs to be reconnected with
the wider macro picture’. Rather than analysis, the
researcher’s task is synthesis, in order to take
account of the holistic nature of complex systems. Haynes
explores the implications of his argument for the management
of policy with a discussion of the Standard Spending
Assessment and Performance Management in the United Kingdom.
He argues that, because numbers are always ideological and
subjective, the selection of measures ought to involve
‘those at the bottom of the policy process, front line
workers and service users’. In my own paper, I am
concerned to examine the role of ‘complexity’ in
the policy process itself. My argument is that a sensitivity
to complexity also leads to a sensitivity to the role of
‘’ignorance’ as something which is
part of the constitution of policy. The article briefly
examines what the policy process would be like if we
described it in terms of complexity science, as a complex
adaptive system. However, drawing upon three
‘episodes’ from an ethnographic study of
collaboration in social welfare, the article examines the
assumptions that are made if we are to use the language of
complexity as a language to describe the policy process. The
problem is what connections between events do we make if we
want to assume those events are part of a policy process?
This is important for ‘how we make those connections
has implications for what we count in – and out –
of policy, what we ignore, and, for example, who or what is
responsible for different outcomes. Illustrating the problem
with a consideration of what assumptions we would carry if
we wanted to model the policy process, I argue that there
are reductionist and deterministic assumptions in the models
from complexity science. Instead, I argue we need to look
more closely at ‘complexity’ itself, and the way
complexity means we ‘are forced to ignore both what we
cannot accommodate and what we do not know about’. This
refers to both a problem for analysis of policy but also a
problem in policy itself. To understand the policy
process, I argue, we need to look at the observations in the
policy processes, observations constituted in systems of
communications. Thus what becomes a part of the policy
process – what connections are made – is
determined by the communications of the policy process, an
important part of which will be ignorance. Tim Blackman’s paper
examines the possibilities of complexity theory for offering
new tools for social policy. He does this by reflecting on
the parallels between complexity theory and performance
management, particularly in relation to the role of
information and steering through feedback, but then
examining the way in which the emphasis of local
self-organisation in complexity theory potentially offers
alternatives to performance management. Blackman explores
his argument through a number of case studies arguing that
policy can ‘seek to define attractors by constraining
system behaviour’ through a combination of historical
‘lock-in’, policy selectivity and the actions of
other agents in the landscape. Using the example of his own
University, Teeside, Blackman uses the language of
complexity theory to describe dynamics of the University
student intakes in a complex environment of ‘determined
unpredictability’. One response for managers is to try
and impose order and stability, for example by dampening
down through internal negative feedback the possibilities of
‘exogenous shocks’. However, to develop a systems
more adaptable to new landscapes, he argues, there is a need
to pay close attention to the relationships between memory,
representation and communication. Blackman concludes with a
discussion of the implications of complexity theory for
democracy, arguing that complexity points to the
anti-democractic tendency of new public management thinking.
While they both share the need for regular monitoring, the
new public management tends to ‘see the results fed
back within a coercive and hierarchical audit culture’
while ‘feedback in complex systems goes directly to the
elements running relevant parts of the system and problems
are explored openly rather than in an atmosphere of blame
and sanction’. John Darwin’s concern
is with exploring complexity science in relation to the
activities of people working in organisations. He contrasts
three different metaphors of organisation -
‘clockwork’, ‘snakepit’ and
‘rainforests’ - examining the assumptions they
make about landscape (what we see), mindset (how we think),
language (what we say) and toolkit (how we act). Darwin
contrasts the language associated to these metaphors, for
example, the clockwork with ‘order’ and
‘control’, the snakepit with ‘disorder’
and ‘chaos’, and the rainforest
‘complex’ with ‘order within chaos’.
Darwin argues that there is a tendency to think in terms of
‘either/or’ logics when thinking about the
appropriateness of different models. Drawing on
Janssens’s ‘Four Roomed Apartment’ and
‘Richmond’s Energy wheel’ to describe states
in organisational life, Darwin uses fuzzy logic to argue
that the clockwork, snakepit and rainforest metaphors all
offer potential strengths for individuals at different times
in organisations and all are limited in certain
circumstances. With this framework, Darwin then identifies a
number of methodologies for working with organisation, each
useful for different situations in which organisations find
themselves. He focuses his discussion on ‘Immersive
Drama’, as a methodology to work with organisations in
transition to the ‘rainforest’ from the
‘snakepit’. Immersive drama is a
‘role-playing simulation technique’ which enables
an ‘affective and cognitive appreciation of the
potentiality of the ‘mess’ by simulating a
rainforst’ (complex system). He ends his article with
an example of using Immersive Drama to ‘review
perceptions of clinical governance by stakeholders across
the NHS’. He concludes emphasising his argument that
the rainforest (complex system) is not always the solution
and that different methods are appropriate in different
times and contexts. The final contribution to
the volume is by David L. Harvey who has been at the
forefront of thinking through the implications of complexity
science for social policy (see e.g. Reed and Harvey 1992,
1995). The piece is a response to the journal and offers a
contribution which addresses the ways in which we need to
develop our understanding of complexity science, and
questions that remain unresolved in the application of
complexity science to social policy. First he argues,
different aspects of complexity science need to be
understood in terms of understanding different aspects of
complex systems and, in applying complexity science to
social policy, we need to think carefully about this
relationship. Second, Harvey highlights the nonlinear
foundations of social policy and offers caution about the
extent to which social policy can deal with the problems of
populations, managing institutions and needs of individuals,
and thus the role of social policy research in relation to
policy formation. So, while it is clear that complexity
science can be important to policy studies, we need to
consider a range of issues, and Harvey leaves us with some
discussion questions to that end. The contributions to this
special edition offer, then, a critical introduction to some
of the emerging possibilities of social policy analysis
engaging with complexity science. Does so inevitably raises
a series of questions and, to emphasise the ongoing research
process, we have include a page of ‘questions’
which have been raised by those engaged in the first
workshop and to which future research needs to address.
These are not exhaustive but do highlight the critical
engagement we need to adopt in seeking to better understand
the complexities of social policy and its
possibilities. Acknowledgements Thanks to Dave Byrne for his
generous and constructive comments on this
editorial. BIBLIOGRAPHY Brockman, J., Ed. (1995)
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Editorial
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