A Note on the Difference Between Complicated and Complex Social Systems

Abstract

The distinction between complicated and complex systems is of immense importance, yet it is often overlooked. Decision-makers commonly mistake complex systems for simply complicated ones and look for solutions without realizing that ‘learning to dance’ with a complex system is definitely different from ‘solving’ the problems arising from it. The situation becomes even worse as far as modern social systems are concerned. This article analyzes the difference between complicated and complex systems to show that (1) what is at stake is a difference of type, not of degree; (2) the difference is based on two different ways of understanding systems, namely through decomposition into smaller parts and through functional analysis; (3) complex systems are the generic, normal case, while complicated systems are highly distinctive, special, and therefore rare.

Full text

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CADMUS, Volume 2, No.1, October 2013, 142-147
A Note on the Difference Between Complicated and
Complex Social Systems
Roberto Poli
Department of Sociology and Social Research, University of Trento;
UNESCO Chair in Anticipatory Systems
Abstract
The distinction between complicated and complex systems is of immense importance, yet it
is often overlooked. Decision-makers commonly mistake complex systems for simply compli-
cated ones and look for solutions without realizing that ‘learning to dance’ with a complex
system is denitely different from ‘solving’ the problems arising from it. The situation
becomes even worse as far as modern social systems are concerned. This article analyzes
the difference between complicated and complex systems to show that (1) what is at stake is
a difference of type, not of degree; (2) the difference is based on two different ways of under-
standing systems, namely through decomposition into smaller parts and through functional
analysis; (3) complex systems are the generic, normal case, while complicated systems are
highly distinctive, special, and therefore rare.
1. Introduction
During the past ve or six decades, ‘complexity’ has been dened in many different
ways.* As a consequence, the difference between ‘complex’ and ‘complicated’ problems
and systems has become unclear and difcult to trace. The following is possibly the golden
rule for distinguishing ‘complex’ from ‘complicated’ problems and systems. Complicated
problems originate from causes that can be individually distinguished; they can be ad dress
ed piecebypiece; for each input to the system there is a proportionate output; the relevant
systems can be controlled and the problems they present admit permanent solutions. On
the other hand, complex problems and systems result from networks of multiple interacting
causes that cannot be individually distinguished; must be addressed as entire systems, that is
they cannot be addressed in a piecemeal way; they are such that small inputs may result in
disproportionate effects; the problems they present cannot be solved once and for ever, but
require to be systematically managed and typically any intervention merges into new prob
lems as a result of the interventions dealing with them; and the relevant systems cannot be
controlled – the best one can do is to inuence them, learn to “dance with them”, as Donella
Meadows aptly said.†
* Here I use “complexity” with regard to both nonlinear phenomena (complexity proper) and innite sensibility to initial and boundary conditions (what
is usually called “chaos” or “deterministic chaos”). Both are based on an internal machinery of a predicative, algorithmic, i.e. mechanical, formal nature.
† The following are some further aspects that a less cursory analysis will have to consider: (1) the “complicated” perspective point tends to work with
closed systems, while the “complex” perspective point works with open systems; (2) the former naturally adopts a zerosum framework, while the latter
can adopt a positivesum framework; (3) the former relies on rstorder systems, while the latter includes secondorder systems, that is systems that are
able to observe themselves (which is one of the sources of their complexity).

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Unfortunately, the vast majority of decisionmakers ask their consultants to give them
‘solutions’ that can solve problems once and for all. That is, they ask their consultants to
treat complex problems as if they were complicated ones. Complexity and the nature of con
temporary science show that the claim to ‘solve’ (complex) problems is often ungrounded.1
‘Learning to dance’ with a complex system is denitely different from ‘solving’ the problems
arising from it.
The situation becomes even worse as far as modern social systems are concerned – not
the least because “most modern systems are both hideously complicated and bewilderingly
complex”.2 According to the golden rule above, the difference between ‘complicated’
and ‘complex’ systems is a difference of type, not a difference of degree. In this sense, a
complex system is not a system that is remarkably more complicated than a customarily
complicated system. A complex system is a system of completely different type from a com
plicated system. This understanding is apparently at odds with the quotation from Mulgan
and Leadbeater. According to that quote, a system can be both complicated and complex.
The apparent contradiction vanishes as soon as one recognizes that the qualities or properties
that make a system complicated are different from the qualities or properties that make a
system complex. The properties used to classify a system as complicated are different from
the properties used to understand a system as complex. This difference explains why the
same system can be classied as pertaining to two otherwise different categories – and ex 
plains also why decisionmakers tend to keep their focus on the side of complicatedness and
downsize or misinterpret the issue of complexity. Many contemporary problems are made
worse by trading one type of problem for the other, because the problems arising from what
makes a system complicated can eventually be solved, while those arising from what makes
a system complex can at best be transformed or modied, but not solved once and for ever.
This is precisely the meaning of Meadows’ learning to ‘dance with them’.
In this regard, reductionism is the thesis that the typedifference between complicated
and complex systems is only apparent because the properties that make a system complex
are based on the properties that make a system complicated. Or that the latter can simulate,
or approximate, as far as one likes, the former. On the other hand, a nonreductionist position
maintains that the difference between complicated and complex systems is a typedifference
that cannot be bridged, and all simulations of the latter from the former miss relevant infor
mation.
This observation introduces the theme of ‘adequate’ models. In short, one can always use
physical models in nonphysical contexts. This does not mean, however, that these models
are able to capture the proprium of different situations. One can measure the weight and
volume of a cat – and these measures provide authentic information – but neither the weight
nor the volume of a living being properly characterizes the human being’s nature. Similarly,
it is always possible to quantify psychological and social phenomena, without being able to
capture their nature.
It is our claim that the difference between complicated and complex systems is of the
same kind: one can always exploit complicated systems to understand complex ones – e.g.

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Complicated and Complex Social Systems Roberto Poli
by developing simulations of the latter that come as close as possible – but in doing so,
something essential is systematically lost.
To see what is at stake, I shall now dig deeper into the difference between complicated
and complex systems.
2. The Difference between Complicated and Complex Systems
If, as we claim, the difference between complicated and complex systems is a difference of
type and not of degree, suitable reasons should be provided. As a matter of fact, quite a few
reasons can be proposed. The following are the three most obvious reasons for the difference
between complicated and complex systems:
1. The primary way to understand complicated systems is
through their structural decomposition – that is, through the
segmentation of the whole system into disjoined structural parts
and their relations, and the further subdivision of these parts
into smaller subparts and their relations. On the other hand,
the primary way to understand complex systems is through
functional analysis – that is, through the activities exerted by
the system. Structural and functional analyses mirror each other
only in very special cases. In general, they are different, and the relations among them
are far from trivial. One way to see their difference is to note that the same structural
part can perform different functions, and the same function can be performed by
different structural parts. The ‘one structureone function’ assumption works only in
very rare cases, which implies that it is a highly nongeneric assumption.
2. Whilst systems have a denite number of structural parts, the functions that a system
is able to perform are potentially unlimited. The primary way to constrain the range
of functions that a system can perform is to delimit its environment, e.g., by allowing
the system to interact with only selected types of systems. That is to say, functions can
be delimited either by closing the system (no interaction) or closing its environment
(limited or constrained interactions).
3. The above two reasons show that the complexity of a system is not directly connected
to the amount of available data or knowledge. Collecting more data or developing better
theories will not transform complex systems into complicated ones. This introduces the
third reason for the difference between complex and complicated systems. Complicated
systems can be – at least in principle – fully understood and modeled. They can be
entirely captured by suitable models. Whilst it may not be feasible to build these
models with all the necessary details – e.g. because it will be too costly or because
some information would be missing – in principle they can be constructed. Complex
systems, on the other hand, are such that they are never fully graspable by any model
whatsoever: models of them – even in principle – are always incomplete and diverge
over time.
“Everything
changes, but
not everything
is creative. ”

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The main reason why complex systems have these apparently strange features is that they
are creative. Being creative includes the capacity to change, learn, and over time become
different from what one was before. But it is more than this. Everything changes, but not
everything is creative. To mention but one component of creativity, the capacity to (either
implicitly or explicitly) reframe is one of the dening features of creativity. Creativity also
includes some capacity to see values and disvalues, and to accept and reject them. Therefore,
it is also the source of hope and despair. None of these properties are possessed by compli
cated systems.
3. Which Systems are Generic?
The proposed acceptance of complexity (and complex systems) is far less trivial than it
may at rst appear. According to our understanding of complexity, almost everything that
falls under the heading of complexity pertains instead to the science of complicated (even
extremely or ‘hideously’ complicated, as Mulgan and Leadbeater put it) systems. Complex
ity is an entirely different matter. The irony is that complex (in the proposed acceptation)
systems are not rare. Complex systems are the usual, normal case. All living systems, all psy
chological systems, all social systems are complex. It is complicated systems that are highly
distinctive, very special, and therefore rare.*
Two obstructions block our capacity to acknowledge that complex systems are the generic
– i.e. the usual – type of system. The rst is the idea that “physics is the queen of science”
– meaning that the other sciences are authentic sciences only if they force themselves into
the straitjacket of the physical framework (the positivist or reductionist attitude). This is not
meant to be a criticism of physics, not even an implicit one: physics deals with complicated
systems, not with complex ones, and its methods have proven exceedingly successful in
yield ing an understanding of complicated systems. There is no reason, however, to believe
that its methods can be used to understand complex systems as well. When the objects are
remarkably different, this may happen, and it should not be surprising that different view
points and methods are required.
By further developing this train of thought, one arrives at an idea of science that is more
general than the competing mainstream acceptance of science presently available: to wit,
instead of distinguishing between the Queen (physics) and the pawns (all the rest), the new
vision distinguishes between the general framework underlying all sciences (what Rosen
called the modeling relation) and a variety of different concretizations of that framework
* During the past fty years or so, many scholars have tried to contribute to this body of ideas, including Bateson, Capra, Hofstadter, Luhmann, Maturana,
Rashevsky, Rosen, and Varela. The clearest and most complete treatment, however, is Rosen’s (1991).
“The rst is the idea that “physics is the queen of science” – meaning that
the other sciences are authentic sciences only if they force themselves
into the straitjacket of the physical framework (the positivist or reduc-
tionist attitude).”

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Complicated and Complex Social Systems Roberto Poli
where each concretization depends on specic assumptions or
constraints. In this view, physics is a highly specic – that is,
nongeneric – science, while other sciences, notably biology and
all the sciences that rely on it (i.e. all the human and social sci
ences), will require less demanding constraints.
The foregoing is a highly compressed presentation of Rosen’s
ideas as developed in his groundbreaking trilogy (see references).
Needless to say, I have had to omit many otherwise necessary
details.
The second reason is that, willynilly, most decisionmakers are positivists, and they regu
larly ask their consultants to give them denitive ‘solutions’ to problems. What they have in
mind are (again!) complicated systems, and they want complex systems to be managed as if
they were complicated ones. Complexity and the nature of contemporary science show that
the claim that (complex) problems can be ‘solved’ is ungrounded.
To call attention to one of the major transformations exhibited by contemporary science,
I have found it helpful to contrast the present situation with the basic understanding of tra
dition al modern science. In a variety of papers I have presented the following summary,
according to which Newtonian science teaches us that natural systems are closed (only
efcient causality is accepted; bottomup, topdown, ‘nal’ causes are forbidden), atomic
(fractionable), reversible (no intrinsic temporal direction), deterministic (given enough
information about initial and boundary conditions, the future evolution of the system can be
specied with any required precision), and universal (natural laws apply everywhere, at all
times, and on all scales). By contrast, contemporary science shows that these claims are all
false, in the literal sense that they work only for some special kinds of systems (technically,
they are not generic).3, 4, 5, 6, 7 The framework currently under development in many scientic
quarters includes open, nonfractionable, irreversible, nondeterministic and contextdepen
dent systems.*
Since, as they say, the devil is in the details, this is the point to note: there is something
even more important than the static opposition between closed and open systems. It is the
opposition between the processes of opening or closing a system.8 More often than not, when
dealing with a system, we have to modify it in order to be able to understand its functioning
or develop a policy. The ways in which a system is opened or (more usually) closed is of
utmost importance. Science is for the most part a set of techniques for closing open systems
in order to scrutinize them. The problem is, it is in this way we study other systems, systems
that are different from the original ones.
* While the traditional, reductionist strategy has proved enormously successful and cannot be simply abandoned, the problems that prove refractory to a
reductionist treatment are growing, and this calls for complementary nonreductionist strategies. Reductionist methods work well when a system can be
decomposed (fragmented) without losing information. On the other hand, for many systems, any fragmentation causes a loss of information (Poli 2011b).
The most promising alternative strategy is to substitute analysis via decomposition (the reductionist mantra) with analysis via natural levels (i.e. the theory
of levels of reality), introduce indecomposable wholes and substitute Humean causation with powers and propensities. Note that, since indecomposable
wholes are not (entirely) understandable from their parts, manipulation of parts may engender unexpected consequences (Popper 1990, Rosen 1985,
Bhaskar 1988, Poli 2010a,b, Poli 2011a, Louie and Poli 2011, Poli 2012a,b).
“Science is for the
most part a set
of techniques for
closing open sys-
tems in order to
scrutinize them.”

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CADMUS Volume 2 - Issue 1, October 2013
Author Contact Information
Email: Roberto.Poli@unitn.it
Notes
1. Roberto Poli, “Complexity, Acceleration, and Anticipation,” ECO 14, no. 4 (2012): 124138
2. Geoff Mulgan and Charlie Leadbeater, “The Systems Innovator,” Nesta http://www.nesta.org.uk/library/documents/System
sinnovationv8.pdf
3. David Depew and Bruce Weber, Darwinism Evolving: System Dynamics and the Genealogy of Natural Selection (Cambridge:
The MIT Press, 1995)
4. Barbara Adam and Chris Groves, Future Matters (Leiden: Brill, 2007)
5. Robert E. Ulanowicz, A Third Window: Natural Life beyond Newton and Darwin (West Conshohocken: Templeton Founda
tion Press, 2009)
6. A. H. Louie and Roberto Poli, “The Spread of Hierarchical Cycles,” International Journal of General Systems 40, no. 3 (2011):
237261
7. Roberto Poli, “Overcoming Divides,” On the Horizon 21, no. 1 (2013): 314
8. Robert Rosen, Essays on Life Itself (New York: Columbia University Press, 2000)
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