Societal systems – complex or worse?
Claes Andersson
Complex Systems Group, Division for Physical Resource Theory, Department of Energy and
Environment, Chalmers University of Technology, 412 96 Göteborg, Sweden.
Anton Törnberg
Department of Sociology and Work Science, University of Gothenburg, Box 720, 405 30
Göteborg, Sweden.
Petter Törnberg
Complex Systems Group, Division for Physical Resource Theory, Department of Energy and
Environment, Chalmers University of Technology, 412 96 Göteborg, Sweden.
Abstract
Complexity science is widely seen as a source of key theoretical capabilities
that have long been lacking in the study of large-scale societal phenomena such
as sociotechnical transitions. But compared with the transformative impact
of complexity science in many other fields, few if any real breakthroughs have
materialized. We here address the question of why societal systems remain so
recalcitrant, and not only to complexity science, but to formal approaches in
general. We argue that societal systems combine at least two methodologically
troublesome systemic qualities – here referred to as complexity and complicatedness – and that the question of what consequences that an intermixing between
these two qualities may have has not been systematically pursued. We do have
powerful formal approaches for dealing with both of these qualities, but we argue
that the combination between these qualities is emergent; i.e. fundamentally
and irreducibly different from either quality in isolation. Noting a connection to
what has long been called “wicked problems” we outline a possible new class of
systems that we call “Wicked Systems” and discuss some implications of such
a construct for theorizing, modeling and policy.
Keywords:
Preprint submitted to Research Policy
July 26, 2013
1. Introduction
A mounting scale and frequency of societal and environmental crises has increasingly brought issues such as sustainability and resilience to the fore. There
is a realization that we must broaden the range of factors that affect the direction of societal evolution to include societal and environmental values, and not
least that we must break out of the hegemony of the present and near future.
But this has turned out to be more easily said than done. Societal change and
innovation has always been a process that basically unfolds spontaneously and
the dynamics of societal systems is also more and more being identified as highly
complex. This clearly changes the landscape for policy (see e.g. Scoones et al.,
2007; Leach et al., 2010). On the one hand it raises serious questions about the
efficacy of many of the standard policy tools, most of which are designed under
entirely different assumptions about how societal systems work. But it has also
opened up the promise of entirely new types of policy tools whereby the causal
mechanics of social and societal dynamics is the target of policy; for example
bottom-up approaches such as the management and design of social networks.
A number of different approaches have been applied, often in combination,
without really managing to tame the complexity of societal systems. For the
purposes of a brief exposition, we will refer to these as macroeconomic, systems
theoretical and narrative-based approaches. Systems theories (or more generally “systems thinking”) make mesoscopic societal organization explicit, which
mainstream macroeconomic models do not, and they are thereby capable of going into details. But systems theories rely on the ability to pre-define system
structure and – apart from obvious empirical challenges – such an approach still
has inherent limitations if we want to understand qualitative change.
Theories of society in general, and sociotechnical transitions in particular, are
therefore to a large extent based on narratives (see e.g. Geels and Schot, 2010).
Narratives are more flexible than both macroeconomic and systems theoretical
tools and they are capable of seamlessly bringing together theoretical elements,
stylized facts and data from many sources and in many formats (Langley, 1999;
Geels, 2010; Geels and Schot, 2010). This capability, however, comes at the
obvious cost of analytical power and formal preciseness. The narrative approach
has thus been criticized by multiple authors for its heuristic and descriptive
nature and lack of attention to agency (Smith et al., 2005; Genus and Coles,
2008).
The application of complexity science, a field that has proven highly capable
of analyzing complex systems that have otherwise been impenetrable to formal
approaches, is today increasingly suggested as a solution to these problems (see
e.g. Scoones et al., 2007). Epstein (2007) refers to the simulational approach that
is central to complexity science as a “generative social science”, a description
that clearly brings out one of its definitional traits: the investigation of whether
proposed hypotheses generate the behavior that they purport to explain. Having
the ability to do this today, he argues, also charges us with doing it. This
approach has come to be widely important in science generally, also in relation
to the design of mathematical models, and as Bar-Yam (2003) puts it, “currently,
2
simulations play such an important role in scientific studies that many analytic
results are not believed unless they are tested by computer simulation.”
Complexity science basically promises the ability to internalize emergence1
into models of large-scale societal phenomena. The hope is that, like narrativebased approaches, it could deal with historical path-dependent change but in a
more formal and analytically powerful way. It would thereby be able to address
a widely recognized need for what we might call a higher theoretical level of
resolution (e.g. Malerba, 2005; Geels, 2010; Holtz, 2011). But despite what
looks like an obvious match, and despite a good deal of effort in this direction,
the success of complexity science has been mainly limited to rather simple social
systems, such as crowds, traffic or evacuation, where the complexity of human
behavior is reduced to simple rule-following. Success in the study of societal
systems in their full complexity has been much more limited, and in many - if
not most - branches of social science, complexity science remains rarely or at
least superficially used.
The motivation for this paper is to dig deeper into the question of why
complexity science would be so hard to apply to societal systems in a broadly
convincing way. We think complexity science is crucial for improving our basic
understanding of how societies work, but that to really make a difference it must
be more carefully placed into the context of other basic approaches. Through
concepts like path-dependency, attractors, tipping points and chaos we have
already learned basic lessons that transform deeply seated ideas about causality
in society. But these are still highly general lessons that have proven hard to
operationalize and they are not strongly represented in policy work. If this
potential would be better realized, it could lead to far-reaching changes in how
we conceive of societal systems and by that also of policy. It could lead us to
challenge and reconsider fundamental conceptions about what it even means to
understand, do science and control society.
2. Complexity science in context
By complexity science we here mean: (i) A set of fundamental concepts for
dealing with non-linear dynamical systems, such as bifurcation, path-dependency
and chaos etc, most of which originate from chaos theory (see e.g. Ott, 2002;
Cvitanovic et al., 2005) but also from other traditions such as dissipative systems theory (Prigogine and Nicolis, 1977) and synergetics (Haken, 1977). These
fundamental mathematical concepts and models however mainly pre-date what
1 Emergence is a “venerable concept in search of a theory” (Corning, 2002). In brief terms it
has to do with how a combination of components come to generate new levels of organization
and have qualitatively new properties that are not present in any of the parts in isolation, and
that often do not even make sense on that level. Emergence therefore has a distinct quality
of being surprising (Ronald et al., 1999). Emergentist reasoning has been central to sociology
since its inception as it was used by Durkheim (1972) for arguing that sociology ought to be
a scientific discipline in its own right: its objects and their interactions were argued to not be
reducible to for example psychology or biology (Sawyer, 2002).
3
we might call complexity science proper, which became possible as a consequence of cheap computer technology, and is strongly based on (ii) a largely
simulation-based modeling methodology that includes for example agent-based
models, network models and cellular automata.
To further chart the scope of complexity science, a comparison with systems
theories can be useful since the two partly share the same roots and since the
the fields partly overlap and the boundaries between them are not clear. When
we here speak of “systems theories” we will basically mean any theory that
conceptualizes society as a hierarchical system composed of linked macroscopic
components (objects and processes) that due to emergence cannot be reduced to
some more fundamental level (in principle or in practice; see e.g. Bedau, 1997;
Chalmers, 2006).
Complexity science is related and in many ways similar to systems theories
and the former is even often seen as an extension of the latter2 . Their overlap risks obscuring some important differences and pointing these out will help
us better define what we mean by both. Both are concerned with non-linear
dynamical systems (e.g. feedback dynamics), both deal with complex systems
(although, as we shall see, in two different senses) and both are concerned with
emergence. But while systems theory assumes emergence – macroscopic formulations must be used because the systems cannot be reduced to some microlevel
– complexity science studies emergence. So while systems theory typically deals
with high-level systems of heterogeneous and functionally complementary components, complexity science typically deals with systems on lower levels of organization and it focuses strongly on mass dynamics, i.e. the dynamical interaction
between large numbers of more or less similar components. Although clearly
an idealization, one could say that if systems theory concerns the dynamics of
macroscopic systems, complexity science concerns the emergence of macroscopic
systems. Society, clearly, is as much about the latter as it is about the former,
which is something that we will return to.
3. Complicated and Complex Systems
Complexity and complicatedness (sometimes referred to as dynamical and
structural complexity; see e.g. Érdi, 2008) are two system qualities that are
often juxtaposed and contrasted for the purpose of explaining what complexity
science is really about; a Google search for “complex vs complicated” yields
2 From the perspective of social science it might seem as if complexity science originated
in systems theory (Sawyer, 2005; Tainter, 2006; Vasileiadou and Safarzyska, 2010). But the
strongest and most formative impetus behind mainstream complexity science today is clearly
the tradition that formed around the Santa Fe Institute (SFI). The SFI was the first dedicated
research center for complexity science, founded in Santa Fe, New Mexico in 1984, in large part
by researchers at the nearby Los Alamos National Laboratory with roots in the Manhattan
Project and thereby also in the origins of scientific computing and dynamical systems theory in
general; see e.g. Galison (1997). The main root from which complexity science keeps drawing
its nutrition must therefore be sought in physics, chemistry and computer science.
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a wealth of examples. When opposed in this way, complexity is associated
with bottom-up self-organization3 – like the behavior of a school of fish or a
crowd – while complicatedness is associated with top-down organization such
as in engineering4 . By being reminded of the distinction between these two
qualities we are to better understand what complexity science is about. But
complexity and complicatedness are not typically separated in this way. They
tend to be conflated and this leads to some confusion that easily passes below the
radar. For example, when Ball (2012) speaks of society as a complex system, he
means this in both senses, but the models that are covered deal with complexity
only in the more narrow sense used here. When Tainter (2006, 2011) speaks of
societal complexity, on the other hand, he clearly means what we here refer to as
“complicatedness”. So would we say that societies are complex or complicated
and in what senses?
On the one hand societies are undeniably complicated with their multi-level
organization and bewildering array of qualitatively different and interacting entities. Systems theories for example clearly seize upon what is seen as an irreducible complicatedness of societal systems. Yet society is also often, and quite
convincingly, argued to be a complex system in the bottom-up self-organization
sense (e.g. Sawyer, 2005; Castellani and Hafferty, 2009; Ball, 2012) and it can
certainly be argued that much of its complicated structure arises from bottomup rather than top-down processes. We clearly see no reason why systems could
not be both complicated and complex at the same time, and societies would
appear to be an excellent example of such a type of system.
We think that the separation between complexity and complicatedness is
not just instructive in itself – we think that it represents a loose thread that
can be pulled further to unravel important details about systems and how we
should study them. The reason – surprisingly perhaps – is that the possibility of
systematically exploring the consequences of mixing these two qualities appears
not to have been explicitly pursued.
To begin understanding what this mix entails, we should first note that we
are mixing a structural quality – complicatedness – with a dynamical quality
– complexity – and that both of these on their own are theoretically challenging. To make matters even worse, the way in which they intermix cause these
qualities to fuse into something quite unlike either quality in isolation: this
is what we mean when we say that the combined quality is emergent. Complexity and complicatedness can be seen as mutually reinforcing in societies;
3 A minimal characterization of self-organization, which is a central concept in complexity
science, typically includes the emergence of order without any centralized or external description of the order that emerges. In particular, it is microscopic order that through a dynamics
gets extended to macroscopic scales. Although it has been developed mostly in physics, biology and for social systems, as part of a larger complexity science movement, it originally
arose in neither of those fields but in psychology (Ashby, 1947); for more on the history of the
concept of self-organization, see Shalizi (2008).
4 An earlier systems-theory era notion of this sort of complexity is that of interactional
complexity (Wimsatt, 1975) which is defined as the degree of cross-coupling in a systems.
5
self-organization generates, changes and maintains macro structure, and macro
structure creates a multitude of arenas for self-organization.
In Figs. (1-4) we chart out system types, problems and theoretical approaches on the basis of these two well-known system qualities. The corners of
the plane that is described give us four ideal system types. Systems that are
neither complex nor complicated (bottom-left corner) we call “simple systems”,
systems that are complex but not very complicated we call “complex systems”,
systems that are complicated but not very complex are labeled “complicated
systems” and, finally, systems that are both complicated and complex we call
“wicked systems”.
We choose the term “wicked systems” in recognition of a potentially deep
connection (whose exact nature remains to be worked out) between this class
of systems and what has been called “wicked problems”. The term “wicked
problems” was first coined in management research by Horst Rittel (briefly introduced by West Churchman, 1967) to characterize a class of problems that
failed to fit into the molds of the formal systems theoretical models that were
being applied across the board at the time with considerable confidence. Just
about any large-scale societal problem can in fact be confidently put into the
category of wicked problems: starvation, climate change, geopolitical conflicts,
social disenfranchisement, and so on. All these are problems that escape definition and where there is a constant feeling that the efficacy of proposed solutions
is called into question not only with regard to feasibility and adequacy but also
with regard to the risk of creating cascades of other problems that are impossible
to foresee and that may be even worse than the initial problem (see also Leach
et al., 2007; Scoones et al., 2007). Explicating the concept, Rittel and Webber
(1973) conclude that the domain of wicked problems in social systems is vast it includes just about any problem short of trivialities. In Churchman’s (1967)
words, what we do with wicked problems is to either tame them by creating “an
aura of good feeling and consensus” or by “carving off a piece of the problem
and finding a rational and feasible solution to this piece”; this would appear to
well describe also our generalization of “wickedness”. By considering “wickedness” as a system quality, we generalize to also be able to speak of things like
wicked dynamics, wicked phenomena and wicked systems.
In Fig. (1) we map three groups of formal theoretical approaches into the
complexity-complicatedness plane: “mathematical theory”, “systems based theories” and “complexity science”. Under the rubric of “mathematical theory” we
place theory and models mainly based on closed-form equations (whose scope
have later been expanded with numerical methods), most importantly in this
context neoclassical economic theory, such as mainstream macroeconomics approaches, which are characterized by strong assumptions about agent rationality
and equilibrium that serve to make models mathematically tractable. When we
refer to systems based theories we mean approaches that rely on holistic ontologies; systems theory is a central example of such approaches although the
category is wider than that. What can be more generally termed “systems
thinking” for example permeates science exceptionally widely today. Systems
based theories break with the reductionist tradition of mathematical theory by
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Complexity Science
Formal theorizing
Systems based theories
Figure 1: On a plane described by a complexity- and a complicatedness axis, we here may
schematically indicate what sort of problems that different approaches are strong at dealing
with.
7
Systems
Herds,
schools,
crowds
etc.
Social
insects,
simple
human
social
etc.
Ecosystems,
Societies
Organisms
Organizations
Simple
artifacts
Technology
Figure 2: We here map different types of systems onto the complexity-complicatedness plane
to roughly indicate where many or most of the problems encountered in different types of
systems fall. Near the “complex” and “complicated” corners we find paradigmatic examples,
such as schools of fish and technological artifacts, respectively. In the “wicked” corner we find
examples, such as ecosystems and societies, that are probably straightforward to accept as
partaking in both complexity and complicatedness at the same time. As we move away from
the corners, the placement of the examples in the diagram becomes more contentious and it is
well to point out that the idea here is not to precisely classify the systems that we have used
as examples. There is considerable room for argument about where they could be placed, how
they should extend across the diagram and what exceptions that may exist. It can be viewed
as a strength of the diagram that it can serve as a basis for such discussions. By “simple human
social” we mean social sub-systems that are relatively unstratified in their organization and
that, although part of a greater societal system, may be considered to some extent in isolation
and in terms of agents and environments on a single level of organization; say for instance
the social interactions between children in a schoolyard. These would be examples of social
systems that can be straightforwardly studied using agent-based simulations.
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Complexity Science
Casting systems
Systems based theories
Figure 3: We here illustrate the casting of wicked problems as either complex, simple or
complicated problems so as to enter them into formal machineries.
”Descriptive” Simulation
Complexity Science
Adjusting formal theories
towards ”realism”
Systems based theories
Figure 4: Here we illustrate how descriptive, or agent-based, simulation represents an attempt
to expand complexity science from the complex corner towards the wicked corner. This
demands models that are more complicated and that will share features of what we call
systems theories.
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making the structure of societal systems explicit on a larger scale, deeming
them to be irreducible: this has expanded the reach of formal methods from
the simple corner towards the complicated corner, generally following the example of engineering. Finally, complexity science has revolutionized science on
a fundamental level by covering an important flank that used to be so hard to
deal with that it was nearly entirely unexplored before the 1980s. It mainly expands from mathematical theory, but also to an important extent from systems
based theories (see Sec. 2) with which its shares a strong attention to feedback
processes. As we illustrate in Fig. (4), agent-based simulation, or more generally “descriptive simulation” (see Edmonds and Moss, 2005), expands from the
simple-and-complex toward the complicated-and-complex side, and in doing so
it comes to share features with systems based approaches; more on this later in
the paper.
Societal systems would have their center of gravity near the wicked corner
of the graph (see Fig. 2), but since formal approaches are unable to access
such problems they will selectively address sub-problems that happen to fall
into their domains or transplant problems from near the wicked corner to the
corners of their preference (see Fig. 3). In the former case we obtain important
but limited “snapshots” of the system in question, in the latter case we may get
spurious results where strong assumptions mean that the benefit of accessing
formal methods of analysis does not warrant the price in realism. In the former
case we are, as we will discuss later, faced with the problem of how to combine
such snapshots; see also Wimsatt (1975).
Most complexity scientists will readily admit that their influence is concentrated to the region near the complex corner. But since complexity science
defines itself more generally as dealing with systems of high complexity (falling
toward the top of the complexity axis) there is no systematic recognition that
something about complex systems may change qualitatively along the complicatedness axis. In summary, it is easy to identify powerful scientific approaches
for dealing with all parts of the plane except for the wicked corner.
So it is true that society is a complex system, but since it is also a complicated
system it is still poorly accessible to complexity science. Complicatedness is
highly problematic for complexity science to internalize and the question of
why can be addressed both specifically and more generally. We will start with
the former and then move to the latter.
Complex systems models tend to start from simple (typically random disordered, regular or nearly empty) initial states to avoid biases that are poorly
empirically founded. But wicked systems such as, for instance, cities or technical systems, never organized from such a simple starting point. They have
contingent histories that stretch absurdly far back into the past – so that is not
realistic either unless we are investigating very robust and general properties.
Finding realistic initial states would seem like an empirical problem first and
foremost, but what appears to be an overlooked point is that since complexity
models (e.g. multi-agent simulations) imitate the dynamics of complex systems
they are themselves also complex systems. This means that they are subject
to the whole panoply of problems of non-linearity described in chaos theory.
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To illustrate this problem we need mention only one: that of sensitivity to initial conditions (e.g. Ott, 2002). Any little error or inexactness – quantitative or
qualitative – in model formulation or parameter values risks exploding and dominating whatever effect that we intended to study. With a simulation model full
of interacting assumptions and tuned parameters, we have no way of knowing
what is really due to what.
This means that the problem with complicated complexity models is in principle not fundamentally one of empirical exactness. No realistic levels of realism
beats chaos for very long. The most paradigmatic illustration of this effect is
probably that of weather forecasting; although it is indeed different in an instructive way. Initial high hopes of being able to use computers and high-fidelity
data to predict weather deep into the future were quickly dashed by the discovery of chaos and the realization that no level of data exactness would suffice (see
Shukla, 1998). In the case of weather prediction the problem is not the model
ontology. In fact what was surprising in that story was that prediction would be
impossible in principle even if we know exactly how a system works. In the case
of wicked systems few would nurture any hopes of predicting detailed future
states, and we may think that we can escape the effects of chaos as long as we
are content with predicting, or at least understanding, the general outlines of
the future. But we must be aware of an important added complication: the
rules of the system are here not only unknown, they change as a result of the
dynamics itself. This is what Lane and Maxfield (2005) refer to as “ontological uncertainty” and it is also the reason why wicked systems are “worse than
complex”.
In a sense, complicated complexity models fail to convince because they
model one wicked system using another wicked system. But the usefulness of
complicated complex systems models must in the end be judged with the purpose of the study in mind. Such models may well be useful for a range of other
purposes (such as for exploration, visualization, games and so on) where the
concept of realism can be more liberally interpreted. in fact, better identifying the weaknesses of “wicked models” can be a starting point for also better
identifying their strengths.
We will now propose one way of understanding why wicked systems are
so recalcitrant, not only to complexity science but to formal modeling more
in general: their organization and dynamics makes them poorly accessible to
approaches that rely on what Simon (1996) called near-decomposability, which
is to say just about any conceivable formal theorizing.
4. Wicked Systems as poorly decomposable systems
Like any formal scientific approach, complexity science and systems theories
strive to isolate systems for independent study such that model properties can be
fixed and formally defined. Formal approaches are desirable since they promote
clarity and are amenable to powerful analytical tools based on mathematics and
computation. But what really makes a system suitable for such an approach?
Under what circumstances more precisely will formal methods run into trouble.
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Simon (1996) introduced the concept of “near-decomposability” to explain
in a clear and systematic way what conditions that need apply for a system to
be possible to study in a formal and controlled manner. The central observation
is that if a system is to be possible to study in isolation its dynamics cannot be
importantly disturbed by outside influences. In Simon’s parlance we should be
able to identify an internal environment where the dynamics that we study takes
place, and an external environment that can be assumed to be static or at least
to be variable only in highly regular ways. The boundary between the internal
and external environment is the delimitation of the model and is referred to as
the interface. What we study with a model is then an internal environment.
Hierarchical system organization is important here: our internal environment
constitutes the external environment of the objects that populate it and that
we deal with only via their interfaces. We deal, therefore, with objects only in
the form of interfaces, for example their interactions with other objects in the
studied system. The beauty of all this is that it makes the world manageable:
we declare our system as autonomous from external complexity and we hide any
complexity residing on lower levels of the hierarchical organization.
We may study this internal environment during what Simon refers to as the
short run: a time scale that (i) is long enough that our objects’ interfaces are
meaningful5 and for important dynamics to have time to happen and (ii) short
enough that our assumptions about the interface remain valid. The greater
the separation of scales between the internal and the external environment, the
greater will the difference in size and speed of the dynamics on these two levels
be, and the more generous will the short run be; i.e. the more interesting things
will have time to happen. For example, models of particle physics can gainfully
be formulated in this way because those systems exhibit a clear and clean scale
separation. Engineered systems, as Simon (1996) points out, are designed to fit
into above description.
We can surely make such assumptions for societal systems in many important
cases, and when we can we are able to bring powerful scientific approaches to
bear. For the purposes of complexity science it would seem reasonable that
certain subsystems, such as traffic or crowd behavior, can be argued to fit this
description. The dynamics of cars and people play themselves out over much
shorter time scales than that on which urban systems, roads, traffic regulation
and so on, change. Such phenomena are also often ephemeral, which bounds the
problem even further. For example, at night the traffic jam dissipates and leaves
no traces that affect tomorrow’s traffic. Similarly may be argued for certain
highly abstractly conceived phenomena that depend on persistent features such
as network dynamics, geography, basic resource constraints, strategic dilemmas
and so on.
But what about societal phenomena more in general? For example, what
about sociotechnical transitions or other wicked problems? Sociotechnical sys5 A human can for instance make decisions (a typical interface feature) over a time scale of
minutes, but hardly on a time scale of milliseconds.
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tems in general are open systems, in which many and far-flung social, technical
and natural processes co-exist, co-evolve and have an impact on each other on
overlapping timescales and levels of organization. In many cases these problems unfold across time scales of decades or more. They involve discontinuous,
qualitative change as well as cascade effects (e.g. Lane, 2011) whereby change
strongly and rapidly feeds back into the conditions for further change. Such
systems are, to say the least, hard to contain in a Simonean compartment with
a “short run” over which transitions can be studied against the background of
an unchanging external environment. The fundamental problem for complexity
science in this context, and really any approach that relies on these ontological assumptions, is that on the time scales of sociotechnical transitions almost
everything in society really is “changing with everything else” (Malerba, 2005).
5. The generation of Wicked Systems
Understanding how systems come into being is key to understand how they
work. We believe that one major type of Wicked Systems are those that act as
“arenas” for multiple interaction modalities between entities, and in particular
when we see competitive interaction. This tells us why societies and ecosystems
are highly wicked while organisms and technological artifacts are much less
so6 . The components of organisms and artifacts interact symbiotically: they
are evolved and designed, respectively, to all pull in the same direction when
it comes to overall system functionality. Most of their parts even make no
sense on their own (e.g. livers and computer keyboards). However, in the
interaction between such entities in “arenas” such as societies and ecosystems,
the predominant modes of interaction are competitive (although clearly not
exclusively; see Sandén and Hillman, 2011) rather than symbiotic.
When a system is organized symbiotically there is little to disturb the emergence of neatly compartmentalized organizations; something that is desired in
adaptive systems because such an organization greatly facilitates processes of
design, re-combination, variation and assembly (again see Simon, 1996). But if
components interact in a plethora of ways this is no longer the case. Hierarchial organization still emerges and the system still tends to settle into at least
temporarily stable self-regulating states. All sorts of cross-connections appear
in the absence of any affective organizing force acting to achieve overall functionality – one part of the system internally acts to outsmart every move that
other parts of the system makes, and the system is perpetually “boiling” with
change.
6 Organisms may seem strange bedfellows with machines near the complicated corner. But
although organisms are generally closer to the Wicked corner than machines they are in
fact quite strictly organized. Just like machines they generally do not change during their
lifetime: they have an assembly/ontogenetic phase followed by another phase during which
they have a nominal ecological function (in some instances this pattern will recur, such as in
metamorphosing species like dragonflies, but this happens in a highly pre-ordained way.)
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However, neither organisms nor engineered artifacts are in reality always
completely modularized into complicated systems according to the neat scheme
described by Simon (1996); many of them incorporate substantial degrees of
complexity and thereby wickedness. Wimsatt (1975) proposes that the reason
for this is that while the potential for design and adaptation is maximized by a
maximally decomposable architecture, functional performance is not necessarily
optimized thereby. So both natural selection and design will also act to favor
solutions that break up this neat structure to improve functionality.
In summary this means that we can expect to find wickedness in three types
of circumstances: (i) in systems that act as arenas for complex interactions
between mainly complicated systems – the main examples of this are ecosystems
and societies; (ii) in systems where the efficacy of evolution or design, but not
fitness/function, is maximized by modularization – examples include organisms
and most engineered artifacts; (iii) cases where we are trying to top-down design
a system with strong non-symbiotic bottom-up interactions – this would perhaps
be the case with large bureaucratic organizations where overall functionality
tends to leave much else to wish for despite a profusion of monitoring and
controlling measures.
6. Understanding Wicked Systems
We may crudely summarize the theoretical situation as follows. Macroeconomic models drag society towards the simple corner of the plane illustrated in
Figs. (1-4), which brings it under the sway of analytical or numerical mathematical methods. This makes for supremely formal and powerful models, but
it comes at the cost of moving society far from its “empirical home”. Systems
theoretical approaches emphasize the complicatedness of society and attempt
to understand, design and steer it as a complicated but not complex system,
approximately in analogy with machines and organisms. By doing this society
is moved from near its native wicked corner to near the complicated corner.
Although systems theories catch more of the structure and dynamics of society than macroeconomic theory does, they again bring society out of its right
element in order to subject it to certain tools of analysis and design.
The call of complexity science is that society is neither simple nor complicated – it is a complex system – so we should use these new complexity methods
for understanding it’ (see e.g. Epstein and Axtell, 1996; Epstein, 2007; Squazzoni, 2008; Ball, 2012, among many). In evidence that this is a promising track,
successes in understanding certain simple and well delimitable social systems,
that happen to fall closer to the complex corner, are brought forth. These are
often viewed as “steps along the way”; a way that is envisioned as perilous
but negotiable using the established methodology, either by linking the formal
models or by scaling them up. But by emphasizing complexity in this way, we
think that society is again moved from its wicked corner. This time toward
the complex corner and, although in a different direction, again out of its right
element.
14
If society is both complicated and complex, it would appear particularly
reasonable to use a combination between approaches that successfully deal with
complicated and complex systems to provide a more integrated view. For example to combine systems theoretical methods with a complexity approach. This is
also in practice what many are attempting to do. Multi-agent simulation, which
is basically an extension of simple complexity modeling (such as in physics and
chemistry), could for instance be described as such an approach: agents, interaction modalities and environments are designed a priori in a fashion that recalls
a systems approach, but they are subsequently let loose in a dynamics where
emergent patterns arise, so they are clearly also complexity models. One can
also imagine multi-agent and other models being combined in the form of modules in a more explicit hierarchical systems ontology along the lines proposed
by Ostrom (2007)7 .
But are such simple combinations necessarily suitable for providing a fuller
picture of these wicked systems? We would say that for systems as wicked as
societies they are probably not, and in particular if a more in-depth understanding of the mechanisms of innovation and transitions is what we seek. One
reason is, as we discussed in Sec. (3), that mechanistically linked sub-models
will give us a model that is as wicked as the system that we are trying to understand. Another reason is that the formal methods that we thereby combine
derive their power not from the presence, but from the lack of, complexity or
complicatedness in systems. They rely on simplifications of either the structure
or the dynamics of systems (or both), and for wicked systems we can frequently
do neither while maintaining acceptable levels of realism. For example, engineering does not work because technological systems are complicated. It works
because technology can be constructed such that it is not complex. For complexity science, even a cursory review of the literature clearly reveals that it excels
specifically in dealing with systems toward the top-left corner of this plot; i.e.
systems that are complex but not very complicated.
7. Moving forward
So what do we do about this? Can this theoretical lacuna near the wicked
corner be filled – just like the area around the complex corner is being filled
by complexity science? Perhaps the great challenge for the future lies precisely
in understanding wicked systems. If so, we must first identify such category
so that we may speak of it in a meaningful way: that systems can be both
complicated and complex at the same time; that such systems are not just a
simple concatenation between complicated systems and complex systems; that
7 This is also an approach typical to Integrated Assessment Modeling (Rotmans and Asselt,
2003) where systems of models are constructed to deal with problems involving large-scale, often global, dynamics; societal sub-systems are usually represented simplistically using general
equilibrium economics models. Agent-based models have frequently been used as modules in
such models for some time (see e.g. Pahl-Wostl, 2002).
15
the system quality that results from the combination between the qualities of
complicatedness and complexity is emergent.
There appears to be few options other than to aim for combined approaches,
but the question of how to combine them may be deeper and more interesting
than what tends to be envisioned. We will leave this task to future work and
here be content with offering some thoughts.
We think that narrative8 is the only mode of theorizing that is not obviously
married to a lack of either complicatedness or complexity and that this leaves
it as a natural option for a basis for combining formal approaches. While arguments to this effect are common, we think that our conceptualization can help
us understand more exactly why that would be the case, which in turn would
provide a better platform for moving forward.
Unlike formal approaches, narratives can at least begin to operate in “the
wicked zone” (see Figs. 1-4). For example – and probably by necessity – the
dominant approaches to sociotechnical transition research today are indeed narrative based appreciative frameworks (Nelson and Winter, 1982); most importantly the Multi-Level Perspective (Rip and Kemp, 1998; Geels, 2002, 2004;
Geels and Schot, 2007), Strategic Niche Management (Kemp et al., 1998; Schot
and Geels, 2008) and Transition Management (Loorbach and Rotmans, 2006;
Loorbach, 2010). Work on “pathways to sustainability” (see e.g. Scoones et al.,
2007) also call for a combination between “positivist and constructivist perspectives” to create a heuristic capable of dealing with problems that fall squarely
into the wicked category. Appreciative frameworks provide thick-description
discourses and imageries with the aid of which we can wrap our minds around
the structure – and to some extent the dynamics – of a complex and complicated
world.
A main role of the appreciative framework is also indeed one of integration.
Geels (2010) argues that using appreciative frameworks allows one to address a
specific topic while integrating ideas from different disciplines. Hence, the aim is
to form a bridge between different perspectives, disciplines and ontologies. The
framework is not regarded as an ontological description of reality, but rather as
“an analytical and heuristic framework to understand the complex dynamics of
socio-technical transitions” (Geels, 2002, p.1273).
Unless something new and truly ground breaking is developed, the narrative
appears to be irreplaceable, and its logical role is to coordinate focused applications of formal approaches. On the one hand, narratives allow us to grasp
wicked systems in an intuitive way and it allows us to recount their historical
development, quite possibly as a result of evolutionary cognitive adaptation to
a multi-million year history of living in communities that are simultaneously
8 Narrative is of course the oldest form of theorizing about social systems. There is quite a
deal of literature on the concept of “narrative” and how it is important for not only “wrapping
our minds around” society, but also to deal with its constant state of change from innovation,
re-interpretations and the potential for taking different perspectives on the same thing. There
is no room here for a review of this literature, so we point the interested reader to e.g. Geels
and Schot (2010) and Lane and Maxfield (2005) and further references therein.
16
complex and complicated; the so-called “Machiavellian intelligence hypothesis”
(e.g. de Waal, 1982; Byrne and Whiten, 1989; Whiten and Byrne, 1997; Whiten
and van Schaik, 2007). On the other hand, in the context of modern societal systems, our abilities – while impressive on a zoological comparison – may be quite
insufficient. Not least as we increasingly use computers in roles where we formerly had to rely on cognition and simpler external aids. Our ability to handle
complicatedness is quite limited (for example by a limited short-term working
memory; see e.g. Coolidge and Wynn, 2005, 2006; Read, 2008), our ability to
make strict inferences and abstractions without the use of mathematics is poor
and our ability to handle complexity is next to none. So narrative theory must
be complemented with formal theorizing to get past impasses where reliance on
intuition will get us no further, and complexity is perhaps the steepest of those
impasses.
So what we are suggesting is in one sense something that is already happening. Our contribution is to suggest a new way of conceptualizing why this is
happening – to provide a road map that allows us to be more deliberate, precise
and systematic. We think that the concept of wicked systems can be powerful
as a condensation, systematization and in the end a tool for alignment of wide
range of special arguments about the nature of a class of systems that are dispersed not only across the social sciences but also across biology and ecology;
for evidence that very similar problems are tackled also there see e.g. Developmental Systems Theory (e.g. Oyama et al., 2001), Generative Entrenchment
(e.g. Wimsatt and Griesemer, 2007) and Niche Construction (e.g. Odling-Smee
et al., 2003).
8. Conclusion
We propose that complexity science does have a tremendous potential for
deepening our understanding of how societal systems work but that applying it
may be a delicate problem that involves exploring and realizing the limitations of
that approach. Our starting point was the observation that despite a widespread
sentiment that complexity science is important, it is still facing considerable
problems. This goes perhaps especially when it comes to penetrating policy,
transition and innovation research, and the question we posed was why that
is the case. We identified a confusion between two qualities that are quite
different, but that are both commonly referred to simply as “complexity”, as an
important impediment to progress. Once properly identified, the distinctiveness
and orthogonality of these two qualities led us to the possibility of explicitly
asking what systems that combine these qualities are like, compared to systems
where either or none is dominant. This lead us to claim that society is “worse
than complex”: it is a wicked system and thereby qualitatively different from
the types of systems that complexity science has successfully been able to deal
with; see Sec. (3). Wicked systems, we propose, is a class of systems that can
and ought to be studied in its own right so that approaches to understanding
them can be more systematically developed.
17
Wicked systems combine a troublesome structural quality – complicatedness
– with a just as troublesome dynamical quality – complexity – and when these
qualities fuse the result is not just worse but also different. We provided one
way of conceptualizing what is so methodologically troublesome with this wicked
combination by using the concept of near-decomposability (Simon, 1996); see
Sec. (4). Formal approaches rely either on low complexity, low complicatedness
or low both and since wicked systems combine these qualities, formal approaches
are largely prevented from operating without being confined to special cases or
making strong assumptions that make models unacceptably unrealistic; see Sec.
(6). This is not to say that the ways in which wicked systems are accessible
formally are not important, quite to the contrary. Different formal methods
allow us to systematically address different aspects of wicked systems that cannot be intuited and the addition of complexity science over the past decades
means that aspects of mass dynamics and emergence can now be dealt with.
What we do want to say is that there is an important “residue” that cannot
be thus addressed, and that many important challenges, such as sociotechnical
transitions and other wicked problems, to a large extent reside in this residue.
This residue is experienced as an inability to model certain important phenomena – for example the innovation of qualitative novelty rather than incremental change – and as a problem with integrating the snapshots provided by
formal methods into a larger picture. Such a larger picture is necessary for “biting over” phenomena, such as sociotechnical transitions, that span across long
temporal scales and that involve many different types of entangled processes
that unfold across many levels of organization. An approach that almost suggests itself here is that of combining different formal approaches; in particular
systems-based approaches – which capture the complicatedness of systems – and
complexity science approaches – which deal with complexity in systems. But
our analysis leads us to suggest that combinations of such approaches will not
be overly successful in the core scientific role of posing and testing hypotheses.
They will combine, and quite possibly amplify, the limitations of their component approaches; see Sec. (3). We want to argue that such approaches –
including for example multi-agent simulation and linked systems of models –
are more limited than the current enthusiasm that surrounds them suggests.
In closing we suggest that narrative based approaches seem to be the best
choice as a framework for integrating formal models. Narrative is the only general methodology that is not strongly dependent on simplifying assumptions
regarding complexity and complicatedness and that is thereby capable of operating directly in the “wicked zone”. But at the same time, narratives come with
their own set of problems that need to be carefully minded.
Charting out the potentials and limitations of different approaches is highly
important for being able to focus research in fruitful directions and that is also
the direction in which the present contribution aims to take a step. We call for
a more careful charting of the methodological and ontological terrain: a more
explicit, in-depth and objective argumentation about precisely why and how
certain types of models would be useful and not useful.
18
9. Acknowledgements
We wish to acknowledge the support of the EU-FET grants MD (no. 284625)
and INSITE (no. 271574). The paper has benefitted from discussions with
David Lane, Björn Sandén, Duncan Kushnir and several others at our department.
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