Title: Wickedness and the anatomy of complexity
Authors: Claes Andersson, Petter T¨ornberg
Reference: JFTR 2252
To appear in:
Received date: 4-1-2017
Revised date: 22-9-2017
Accepted date: 2-11-2017
Please cite this article as: Claes Andersson, Petter T¨ornberg, Wickedness and the
anatomy of complexity, Futures https://doi.org/10.1016/j.futures.2017.11.001
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Wickedness and the anatomy of complexity
1 Complex Systems Group, Division for Physical Resource Theory, Department of Space, Earth and
Environment, Chalmers University of Technology, 412 96 Göteborg, Sweden
2 Department of Sociology, University of Amsterdam, PO Box 15508, 1001 NA Amsterdam, The
1. We break down the catchall term "complexity" to a map of ontological categories.
2. Wickedness, complexity and complicatedness in “Spectrum of Overwhelming Systems" (SOS).
3. SOS is useful for aligning ideas and actions across fields and backgrounds
4. Innovation is suggested to be the causal basis of wickedness.
Traditional scientific policy approaches and tools are increasingly seen as inadequate, or even counter-
productive, for many purposes. In response to these shortcomings, a new wave of approaches has
emerged based on the idea that societal systems are irreducibly complex. The new categories that are
thereby introduced – like “complex” or “wicked” – suffer, however, by a lack of shared understanding.
We here aim to reduce this confusion by developing a meta-ontological map of types of systems that
have the potential to “overwhelm us”: characteristic types of problems, attributions of function,
manners of design and governance, and generating and maintaining processes and phenomena. This
permits us, in a new way, to outline an inner anatomy of the motley collection of system types that we
tend to call “complex”. Wicked problems here emerge as the product of an ontologically distinct and
describable type of system that blends dynamical and organizational complexity. The framework is
intended to provide systematic meta-theoretical support for approaching complexity and wickedness in
policy and design. We also points to a potential causal connection between innovation and wickedness
as a basis for further theoretical improvement.
Wicked problems; Future; Sustainability; Sociotechnical systems; Complexity; Innovation
Out of discontent with the performance and adequacy of traditional approaches, which may be described
as embodying a top-down rather than a bottom-up approach to understanding and acting, and that are
largely based on prediction, planning and control (e.g. Leach et al. 2010; Loorbach 2010; Hasnoot et al.
2013; Castree et al. 2014), an alternative view of socio-eco-technological systems is taking shape. This
view emphasizes qualities related to ideas about complexity, such as multidimensionality, path-
dependency and unpredictability (e.g. Rip and Kemp 1998; Gunderson and Holling 2002; Berkhout 2002;
Beddoe et al. 2009; Folke et al. 2010; Byrne and Callaghan 2013; Bai et al. 2015). These qualities are seen
as irreducible root causes of problems – not least ones related to sustainability – and of our persistent
inability to predict, prevent and deal with them. They are also seen as key to the development of a new
generation of approaches to understanding and tackling these problems.
These approaches are based on partially overlapping sets of ideas, which is promising for a future
integration and synthesis, and deep new insights into the workings of societal systems. Such a
development is, however, hindered by a lack of shared understanding of foundational concepts, arguably
most importantly complexity and wickedness. Depending on whether a person has a background in social
or natural science, whether he or she is trained in quantitative or qualitative methods, a person’s idea
about what complexity and allied concepts mean is often strong, intuitive and treacherously different
from other people’s ideas. This may be less of a hindrance for productive work within the fields where
these ideas emanate, but it becomes a real problem in inter- and transdisciplinary settings. General
foundational knowledge about the meta-theoretical nature of these concepts, and the systems that they
concern, would aid the formation of the shared understandings that are necessary for productive and
cumulative work on a larger scale.
Toward this goal, we here sketch a map of ontological categories as an open-ended and flexible meta-
analytical tool. Our focus lies on furthering our understanding of “wickedness” which denotes a certain
flavor of complexity in societal problems seminally described by Rittel and Webber (1973). An attribution
of wickedness to a problem illustrates a feeling that the problem almost seems to avoid resolution and/or
that attempting to solve it keeps generating hosts of other and seemingly unrelated problems. Within
this “Spectrum of Overwhelming Systems” (SOS), we find, however, not only wicked systems. We also
find complex and complicated systems (Érdi 2008), as well as additional now-discernable sub-classes. All
of these are critical to understanding and delimiting wickedness as a distinct type of complexity
(Andersson et al. 2014a), but they are also important in themselves. In describing these sub-categories,
we will discuss how they are related, what may cause systems in these categories to arise, what their
characteristic properties, problems and potential functions are, how they may arise, what theories and
methodologies that are suitable for dealing with them, and so on.
We propose that the SOS-diagram is useful for enabling a more focused and specific debate by
structuring, visualizing, for revealing points of fundamental contention, and by raising concrete questions
about how different types of systems and problems interact. We have found this useful in particular in
trans-disciplinary settings when partners with different backgrounds and experiences must collaborate
and align their thinking and actions. Our results are thereby intended to contribute on two different
levels: (i) Methodologically by enabling detailed debate and alignment between people and ideas, in
general and specific settings, and (ii) theoretically by providing some initial and provisory insights gained
by our own application of the framework. Most importantly, we argue that innovation – in a broad sense,
and understood as a distributed process of competitive diversification and adaptation – may describe
the generation of the qualities associated with of wickedness.
2. Worse than complex
We continue in the direction taken by Andersson et al. (2014a) and develop wickedness by mining its
super-category of “complexity” (as ubiquitous as it is elusive) for internal structure and tensions that can
be used to organize the picture.
No single definition of complexity has attracted a majority of followers (e.g. Erdì 2008) and this anarchy
is reflected also in how the concept is used in the literature. “Complexity” usually does not point at any
particular idea about complexity, nor at any particular generating process, but works mostly as a catch-
all term for problems that overwhelm us in some sense; things like massive parallelism, multi-level
hierarchization, heterogeneity, tangled “seamless webs”, emergence, non-linearity and sensitivity to
disturbances, or combinations thereof.
Complexity thereby lumps together a motley collection of causal processes and types of organization.
But can we find a representation that usefully separates and clusters this space? Is there an inner
anatomy to the set of all things that are complex? To make a first cut we employ a distinction that is
often used to explain the scope of complexity science
(Andersson et al. 2014a:146-148): that complexity
is not complicatedness.
More specifically, we observe that if we take the popular understanding of “complexity” (i.e.
“overwhelmingness”), and factor out the complicated, the residue corresponds quite closely to the type
of systems that complexity science is best adapted to deal with. We henceforth refer to this residue as
complexity, referring to the broader “folk-category” of complexity by the more descriptive term
This seems to provide a separation of the sought kind: complexity is something like a shoal of fish
complicatedness is more like a computer. Indeed, these categories correspond to whole distinct
paradigms in systems thinking (Andersson et al. 2014a:149).
In Fig (1) we illustrate this move by splitting “complex/overwhelming” into complex and complicated (Fig.
1), expanding thereby an axis between simple and complex/overwhelming into a plane (the SOS diagram).
The most immediate effect is that systems that “work” similarly now cluster similarly, forming the basis
of a potential causal taxonomy.
Figure 1: We obtain the “Spectrum of Overwhelming Systems” (SOS diagram) by splitting “complex/overwhelming”
into (i) a stricter remainder that retains the label “complexity”, and (ii), complicatedness, which is a different quality
Or more precisely of what Andersson et al. (2014a) term “mainstream complexity science”.
A Google search on ”complex vs complicated” will provide an ample selection of examples.
“Complexity” thereby corresponds closely to what Morin (2007) refers to as “restricted complexity”, and to
what Erdì (2008) calls “dynamical complexity”; see also Andersson et al. (2014a).
altogether. Although placing examples remains hard and potentially contentious, the strong feeling of comparing
apples and oranges dissipates, and the task becomes much more straightforward and potentially interesting.
Wicked systems and problems now become separated into a specific part of the SOS diagram, namely
the upper right-hand part where both qualities are mixed. Societal systems are something like Necker
cubes in this respect: they can be described both as somewhat like a shoal of fish, and somewhat like the
organization of a computer,
depending on how we are primed to look at them.
Since we may not only place systems, but also problems, methods, models and so on into this diagram –
also together if we like – we also see this as a possible generalization of wickedness as a general quality
of systems, just as we are accustomed to apply complexity and complicatedness.
So what processes and circumstances generate these combinations between complexity and
complicatedness? Is wickedness an emergent and irreducible category, possible to study and develop
methods for dealing with in its own right?
3. Wickedness in context
To consider wickedness in the context of systems that it may resemble, be mistaken for, or that it
partakes in, generates and interacts with, we now postulate some more highly resolved categories; see
Figure 2: The resolved SOS Diagram is intended to facilitate differentiation between problems, systems and
approaches on the basis of how degrees-of-freedom are organized in different types of systems. The basic relevance
The former has been argued by complexity scientists (e.g. Sawyer 2005; Castellani and Hafferty 2009; Ball 2012)
and the latter view of society is ubiquitous, embodied in countless “traditional” methods and theories.
Small human societies
is that this organization determines what tools we need for designing, governing and understanding systems. In
brief, the idea is to move beyond a tacit and very vaguely differentiated concept of “complexity”.
In the following Sections (3.1-3.3) we take a closer look at each of these sub-categories. Each non-wicked
sub-category will be described: (i) generally in Tables, and (ii), specifically with respect to features of
particular importance for understanding wickedness. The two wicked sub-categories will be described
more in detail. We will then use this image of the structure of the space of a “spectrum of overwhelming
systems” to analyze how we might go about better understanding and intervening in wicked systems.
3.1 The basic qualities: complexity and complicatedness
Complexity and complicatedness represent the two principal ways in which large numbers of degrees of
freedom can become stably organized into large systems. Systems close to either ideal class are
dominated by either one of these two organizational principles, enabling strong simplifying assumptions,
and thereby powerful formal theory.
We will make reference to concepts from Herbert Simon’s (1962) model of Near-Decomposability.
Readers unfamiliar with Near-Decomposability are referred to Appendix A, and (Andersson et al. 2014.)
3.1.1 Complicated Systems
Central examples: Technology, organisms.
Main signifying features:
1. Scale-separated level hierarchies.
2. Potentially very tall hierarchies, spanning from small to large scales.
3. Components have relatively few sub-components.
4. About as many component types as component instances.
5. Sub-components are co-adapted to specific complementary functions
in a whole with emergent affordances and functions.
6. Low redundancy: components cannot generally take over the roles of
7. Sub-components are “slaved”: they often make no sense separately.
8. Near-Decomposability essentially resets the number of degrees of
freedom between sub-component and component.
9. Phased lifecycle:
o Assembly: System assembled/developed with high precision
in protected space, free from functional demands.
o Use: Systems expresses intended set of functions, may
undergo diagnostics and repairs to maintain function.
o Transition between phases may be gradual, as in organisms.
Simplicity hook: The full system may pack very large numbers of components
into delineable compartments organized in a level hierarchy. This strongly
structures the patterns of permitted interactions and enables strong simplifying
assumptions; see Appendix A. We hardly need any knowledge about the
embedding system to operate locally on its components.
Desirable adaptive affordances: Allows systematic exploration of design
spaces: innovation and assembly may act in a strongly distributed and layered
fashion; detailed designs (strong specialization), controllability, repeatability,
scalability, precise and economic assembly, division-of-labor.
1. Controlling and predicting the External Environment.
2. Alignment of goals and aims of components (“slaving.”)
3. Fine-tuned, non-redundant organization causes sensitivity to
breakdowns and is an obstacle to dynamic use-phase adaptation.
Main approaches: Engineering, early “waves” of systems theory (cybernetics,
operations research, control theory etc.; Sawyer 2005:23), overall the
“standard way” we think about design and governance.
Generation/maintenance: Complicated systems are assembled or, in biology,
developed in morphogenesis (Slack 2005).
An easily overlooked pre-condition for the construction of stable adapted systems is that their
components must be “slaved.” Perfectly symbiotic components lack any incentive to undermine the
function of the systems that they form parts of. This makes them malleable and enables the design,
assembly and governance of delicately fine-tuned systems. Open-endedness in possible designs is the
main adaptive affordance of complicatedness, and the basis for adaptation in biology as well as human
Slaving applies so automatically to technical systems (try to imagine the exhaust manifold competing
with the engine) that in order to illustrate its general role and significance we will consider three
recursively linked biological examples where we may trace the genesis of new complicated design spaces
from the establishment of alignment and slaving among initially autonomous components. In all three
cases, whole new universes of adaptive complicate designs resulted.
All three examples describe a full such transition: first from wickedness (competitive interactions) to
trans-complicatedness via increasing cooperation, then on to complicatedness via co-adaptation of
1. The “endosymbiont hypothesis” (Margulis 1970; Archibald 2011) explains the complicated
organization of eukaryotic cells as the result of increasing symbiosis between autonomous
bacterial precursors. As symbiosis deepened, these bacteria mutually adapted to form the
eukaryotic system of organelles within a single physical enclosure. Entirely co-dependent – also
for reproduction – they collectively constituted a much more versatile component on a new level
2. This versatility importantly included the potential for forming a yet higher level of organization:
somatic cells of multicellular organisms are co-adapted differentiated forms of unicellular
precursors. Incapable of separate existences, they can procreate only via germline cells (e.g. eggs
and sperm), and so there is no possibility for internal competition (Michod et al. 2006; Johnson
et al. 2011; Hanschen et al. 2015).
3. Finally, social insects (e.g. bees and ants) take this symbiotic principle yet one more step: their
organism-level components are slaved under a colony-level Interface in an equivalent manner
(e.g. Oster and Wilson 1978; Moritz and Southwick 2012).
But there is also a tradeoff between delicate fine-tuning (optimality) and robustness. Complicated
Systems must have dedicated sub-systems or external scaffolds to constantly guard and repair them in
the face of internal and external disturbances; e.g. diagnostics, repairs, materials with high durability,
and so on (e.g. Michod and Nedelcu 2003).
3.1.2 Complex Systems
Central examples: Herds, traffic, social networks.
Main signifying features:
1. Many (even immensely so) components on same organizational level.
2. Many components but few component classes.
3. High redundancy: components may step in for other components of
the same class (compare removing an ant with removing the liver).
4. Loose exogenous constraints on formation and dissolution of
interactions between components. Exogenous structuring constraints
apply to interactions between types of components; e.g. how do cars
and trucks behave in traffic.
5. Strong endogenous structuring of component interactions (emergent
patterns) may arise from the dynamics (shoals, traffic jams, paths,
Simplicity hook: If we deal successfully with emergence among very large
numbers of interacting entities (which e.g. simulation helps us do) then, from
the view of component classes, complex systems are much simpler than they
may appear. Emergent patterns can be explained in those terms.
Desirable adaptive affordances:
1. Resilience (dampening of disturbances, redundancy)
3. Distributed action, monitoring and processing provides affordances
unavailable to complicated systems.
4. Self-assembly/organization as path to building adapted systems.
1. Chaos in massively parallel dynamics: (i) unpredictability; (ii)
amplification of disturbances.
2. Emergence (e.g. macroscopic patterns) in strongly parallel and
distributed dynamical systems.
3. Harnessing complex systems for adapted purposes invokes the same
demand for “slaving” components as for complicatedness.
Main approaches: Computation and dynamical systems theory (e.g. chaos
theory, synergetics). Simulation crucially allows mass dynamics to play out
explicitly “in silico”.
Generation/maintenance: Generally, emergent complex patterns arise
“suddenly” as interacting components come together, and dissolve if
components seize to interact.
Complexity has two strong sources of relevance for wickedness: (i) As a source of adaptive affordances
that correspond to classical Achilles’ heels of complicated systems. (ii) As sources of uncertainty and
emergent problems as large numbers of adapted systems interact (e.g. vehicles, or people walking,
Natural selection is a prominent example of complexity-based adaptation: sets of competing
components (“populations;” e.g. Mayr 1993; Hodgson and Knudsen 2004; Aldrich et al. 2008; Andersson
2011) that are mainly similar but that, crucially, exhibit minor variations that affect competitive success
and carry over to derivative versions. “Distributed computation” is another example, seen e.g. among
social insects, in biologically inspired optimization methods (Wahde 2008), “crowd wisdom” (e.g.
Surowiecki 2004) and peer problem solving (e.g. in web forums; Liu and Tsai 2008).
Self-assembly/self-organization affords a powerful and economic method for assembly of microscopic
components and the potential to realize designs that are not feasible with traditional assembly (e.g.
Sacanna et al. 2013). Described as “…the autonomous organization of components or structures without
human intervention” (Whitesides and Grzybowski 2002:2418), a particularly interesting potential is that
of designing microlevel components such that they dynamically assemble “themselves” to realize some
intended functional macrolevel Interface.
Problems caused by complexity emanate chiefly from chaos and emergence.
1. Chaos is the flip-side of the resilience coin: non-linearity may dampen disturbances but may also
amplify them (e.g. Cvitanovic et al. 2005:146-149). Responses of Complex Systems to
interventions are therefore often unpredictable, both quantitatively and qualitatively, which has
been conceptualized in sustainability contexts as e.g. “attractors”, “tipping points”,
“bifurcations”, “basins of attraction” etc. (e.g. Holling 2001; Lenton et al. 2008; Helbing 2013).
2. Emergence is macroscopic qualitative novelty arising from interacting components (e.g. Bedau
1997; Holland 1998; Corning 2002); summarized in the adage “more is different” by Anderson
(1972.) Although not unique to complex systems, emergence in complex systems is particularly
“surprising” due to our inability to intuitively follow complex dynamics. Although by no means
inherently maladaptive, they are hard to foresee, and they therefore often appear as negative
externalities, such as congestion in communication networks (e.g. Yan et al. 2006.)
3.2 The trans-qualities
Trans-qualities arise as adaptive responses to facticities, most importantly the construction of
complicated systems using components that are hard to align (humans and organizations thereof), but
also to reap adaptive advantages. Generally, we seek complicatedness to build adapted organization and
impose control, and complexity to achieve resilience, adaptability and low management overhead.
3.2.1 Trans-complicated Systems
Central examples: Organizations with human components, or biological
individuals (e.g. of different species) with separate channels of procreation.
Adaptive rationale: Tapping into adaptive affordances of complicatedness for
systems whose components have “an agenda of their own.”
Main approaches: Organizational and political theories and practice. In
general, the art of organizing humans.
1. Alignment must be actively maintained (monitored, policed, enforced)
by dedicated systems. This is costly and carries the risk of failure.
2. Insufficient alignment brings “component rebellion”, breaking Near-
Decomposability if components adapt to their own aims and goals at
the expense of the whole (e.g. corruption; “defection” in game
3. Controlling and predicting the External Environment is hard, expensive
and faces decreasing returns to investment in terms of effectiveness.
4. Duplication/assembly much harder than for complicated systems since
component innovativeness generates “tacit” processes and
organization, which become crucially necessary for function (e.g.
Trans-complicatedness represents the complicated organization of components with separate agendas.
Complexity enters as an increased density, and lower regularity, of interactions: while an exhaust
manifold is precisely an exhaust manifold, a human component will connect a system to just about all
sectors of society and in a wide variety of ways (a seamless web; Hughes 1986.)
Alignment (Sec. 3.1.1) here becomes a salient issue, and human organizations are highly preoccupied
with the problem of internally aligning interests and actions. By contrast with the biological cases cited
above (Sec. 3.1.1), however, the problem is never solved
here. Alignment is an ongoing and often highly
costly effort of negotiation, persuasion, monitoring, punishment, reward etc.: a struggle to pull
organizations away from wickedness, toward the complicated regime where design and governance is
Trans-complicated systems also face the threat of unexpected (even hostile) change from the outside.
They are, however, inherently poor at adapting to external changes since they are prone to breakdown
if their strongly patterned internal interactions are disturbed. One response is to try, as far as possible,
to balance the needs for control and flexibility (e.g. “loose coupling”; Orton and Weick 1990) and another
is to exert control over the Outer Environment (an option whose availability varies with power; see also
Niche Construction, Laland et al. 2014).
Trans-complicated systems are rarely assembled, but develop/evolve historically; a property they share
with the wicked systems that they inhabit. The taller the hierarchies, the tougher the problems outlined
here become. First, the taller and broader the hierarchy, the harder it becomes to align its components
under directions entering from the top-down, making their internal dynamics more and more ecosystem-
Many utopian visions imagine precisely a state of society where all forces of dis-alignment are eliminated and
where alignment becomes automatic.
like and less and less like an organism. This poses a problem to political control and, more generally, to
scaling up organizations. Second, while components within nations and global corporations may be under
the control of integral systems of alignment (e.g. institutions, shared languages, cultures and narratives),
nations and global corporations themselves do not interact under a similarly strong force of alignment.
3.2.2 Trans-complex Systems
Central examples: “Sharing economy” (e.g. AirBnB, Uber), smart grids, forums,
social media movements (Arab Spring, Avaaz, etc.), guerillas, terrorist
networks. Organizations based on disseminated designs, shared views, norms
etc. (e.g. in religion and politics).
Adaptive rationale: Tapping into adaptive affordances specific to complex
systems; e.g. organizing with scarce resources, organization in
hostile/repressive environments; designing, or increasing the level of control,
specificity and alignment of, an adaptive complex system.
Main approaches: Two (often combined) main approaches: (i) designing micro-
component classes such that a desired feature emerges as many components
interact; (ii) dynamically scaffolding the behavior of components (“herding the
1. Hard to achieve detailed designs due to highly non-linear mapping
between specification and resulting system.
2. See corresponding points 1-2 or Table (3).
Trans-complex systems represent the harnessing of affordances of complex systems by adding elements
of persistent complicated organization to complex systems.
Two main strategies are: (i) designing micro-components to collectively behave in a certain way, as
described under complexity (Sec. 3.1.2); (ii) “herding” – dynamically monitoring and perturbing micro-
These strategies are, however, typically combined since herding can be improved if components are
primed to respond more appropriately; e.g. the emergence of pastoralism (which, literally, involves
herding) may have involved not only herding strategies, but also selection-induced morphological change
of animal behavior (e.g. Marshall and Weissbrod 2011) to improve responses to herding.
Religion, politics and marketing would provide examples of priming micro-components to be responsive
to “herding”. Two factors have, however, limited our ability to accurately design detailed outcomes: (i)
high cost and low bandwidth of the required mass communication; (ii) lack of theoretical understanding
of non-linearity and emergence in complex systems.
Complexity Science, and Information and Communication Technologies (ICT), allowing large-scale
dynamical monitoring, information sharing and processing of large amounts of data, has alleviated these
limitations greatly. Coordinated collective action using ICT is today becoming increasingly commonplace,
varied and sophisticated; e.g. the emerging “sharing economy” (e.g. Hamari et al. 2015) and “smart grids”
(e.g. Clastres 2011.)
Wicked systems are arenas where adapting systems interact and compete over limited resources.
3.3.1 Wicked Systems
Central examples: Large human societies, ecosystems over evolutionary time.
Main signifying features:
1. Not adapted, but arenas of and for interaction between adapted
2. Components have own agendas and exhibit the full range of ecological
3. Components are heterogeneous, versatile multi-level interactors,
interacting under few constraints.
4. Strongly distributed and pervasive innovation/adaptation.
5. Strongly interconnected “seamless webs”: cascade effects and lock-ins
(e.g. w.r.t. interventions and technological innovation.)
Simplicity hook: No general avenue for formal simplification.
Desirable adaptive affordances: As arenas for adaptation, they are, hotbeds of
innovation: without wickedness, no creativity.
1. Intermittent, unexpected behavior: (i) lock-ins from jamming between
dependent entities; (ii) dramatic transitions as jams break up.
2. Uncertainty and unpredictability, not least “ontological uncertainty”;
emergence of qualitative novelty; game changers.
3. Cascades and entrenchment of effects makes for a potentially
unlimited horizon (both in time and scope) for consequences of
4. Uncertainty that grows rapidly with time and scope imposes a short
5. Short foresight horizon and long consequence horizon combine into a
propensity for unsustainability in the form of self-undermining
6. Innovation upsets any level hierarchical organization, ruining
prospects for Near Decomposability, constantly rewriting the “rules of
7. Control demands a global overview, but growth and change is local
and demands no such overview, so wicked systems may outgrow any
capacity for governing them.
8. No two subsystems or problems are likely to be identical: uniqueness
hampers learning and generalization.
Main approaches: Approaches based on complicatedness and complexity; the
former include “traditional” approaches. Narrative approaches with “thick”
historical descriptions and analyses. Harnessed Innovation approaches emerge
Generation/maintenance: Open-ended innovation – “creative destruction” – in
an “arena” where adapting systems interact ecologically. Wicked systems are
deeply historical: identifiable initial conditions may be ancient and qualitatively
Simply put, if self-organization generates complex systems, and assembly/development generates
complicated systems, then innovation generates wicked systems: wicked systems are arenas of and for
innovation. Note that innovation is here invoked, without its common positive valance, as a causal
process of change without regard to whether the change is good or bad, or with respect to whom or
Open-ended innovation generates powerful interactors, organized primarily as complicated or trans-
complicated systems. These are capable of maintaining vast and heterogeneous arrays of interactions
where every node is densely connected to just about all domains of the web (society as a “seamless
web”, Hughes 1986). Innovation both integrates the seamless web by weakly constrained interaction,
and separates it, through specialization.
Interactions have a strong enveloping competitive component but display the whole spectrum of
ecological interactions (competition, symbiosis, neutralism, parasitism, commensalism and amensalism;
see Sandén and Hillman 2011:407). Symbiotic interactions may give rise to self-organized systems toward
the trans-complicated and trans-complex regimes; e.g. bundles of value chains as described by Sandén
and Hillman (2011:404-406). Parts and levels may over time co-adapt to become increasingly co-
dependent; compare with examples of symbiotic origins of complicated systems; Sec. (3.1.1). The
boundary between wickedness and trans-qualities is thereby porous.
Components act and react within neighborhoods in the seamless web, and, since each is part of many
neighborhoods, change is liable to propagate across the system. Dynamically and macroscopically, this
leads to two dialectical dynamical regimes: transition and lock-in.
Transitions are self-propagating waves of qualitative “reconfigurations” of and by components, traveling
across neighborhoods in the seamless web (Geels 2002; Lane and Maxfield 1997). These may form
potentially system-wide cascades of change (Schiffer 2005; Lane et al. 2009; Lane 2011, 2016; Geels
2011.) However, if locally beneficial reconfigurations cannot be made, change will be resisted, and if such
criteria, posed by large numbers of strongly interconnected components, are combined, the range of
actually viable innovations will be strongly constrained and channeled. The result is a lock-in, such as by
a dominant design (Utterback and Abernathy 1975) or a sociotechnical regime (Rip and Kemp 1998; Geels
2002), or gene regulatory networks (e.g. Davidson and Erwin 2006.)
Historical case studies embodying compatible accounts include Geels (2005) on automobility, Rödl and
Andersson (2015) electrification, Geels (2002) on steamships and Lane (2011) on book printing. But, for
demonstrations of generality, see also Andersson et al. (2014b) on prehistorical cultural evolution, Erwin
and Valentine (2013) on the emergence of modern life forms in the “Cambrian Explosion” (~543 Ma),
and Laubichler and Renn’s (2015) on the emergence of eusociality.
Consequences of action in such a system is shrouded in deep uncertainty, described by Lane and Maxfield
(2005) as an ontological uncertainty: not about the truth or meaning of well-defined propositions but
about what entities that inhabit the world, how they may interact, and how interactions and entities
change through interaction (Lane and Maxfield 2005:9-10; Bonifati 2010:755.) Uncertainty keeps us from
aligning action to respond to future ill effects (game theory; e.g. Ostrom 1990; Gintis 2000), but it also
(and relatedly,) prevents us from designing effective interventions without high likelihoods of causing
unexpected troubles in other domains.
Uncertainty also forces us to be shortsighted by preventing us from building sufficient certainty for large-
scale alignment and action. A short foresight horizon, and virtually no bound on the horizon for
consequences of actions, makes wicked systems susceptible to self-undermining: what we typically refer
to as unsustainability. Societal evolution is thereby prone to spontaneously and collectively embark on
pathways leading to new dynamical regimes that may be arbitrarily disadvantageous (e.g. the
Anthropocene; Steffen et al. 2015a,b).
Innovation unfolds distributedly and locally in “the adjacent possible” (Kauffman 1996, 2000), which
consists of organization largely created by innovation. The game and the rules of the game are thereby
impossible to delineate in the general case (non-Near Decomposability; see Appendix A). Interactions
will cross any postulated Interface boundaries or levels of organization, building impenetrable “causal
thickets” (Wimsatt 1994) rather than the ordered level- and component patterns that adapted (and many
physical) systems exhibit. Wicked systems thereby cannot, generally, be simplified along either of the
two axes in the SOS diagram: simplicity is not just hard to find, it frequently simply is not there.
Innovation happens around and within (sandwiched emergence; Lane 2006) structures, which are
constantly in a state of linked construction and destruction (creative destruction; Schumpeter 1943;
Reinert and Reinert 2006). The organization of wicked systems thereby never settles down to persistently
stable or stationary states: regular and stable patterns of interaction (levels, components) are short-lived,
often more local than we think, and constantly threatened by dissolution. Wicked systems will therefore
rarely repeat themselves, with instances of what seems to be “the same” problem or system differing
This organization is rarely forged through consensus or completely aligned interests, but rather through
continuous conflict and negotiation. This can be related to the long-standing sociological tradition around
the idea of “negotiated order” (Strauss et al. 1963), as it challenges the notion of social orders as innately
stable, and instead proposes order and stability as social accomplishments that need to be explained
(Strauss, 1978). The central premise is that social order is an ongoing production of the actors involved,
and that order is thus temporary and in flux: "a universe marked by tremendous fluidity; it won't and
can't stand still. It is a universe where fragmentation, splintering, and disappearance are the mirror
images of appearance, emergence and coalescence" (Strauss 1978, p. 123). The structures are
furthermore important in setting the positions from which individuals negotiate and, in turn, give these
negotiations their patterned quality: the structures are created, but also create the context for action
While Strauss’ work focused on organizations, similar dynamics seem to play out on all levels of wicked
systems. In line with Byrne and Callaghan (2013), we can regard wicked systems in general “as negotiated
orderings at different scales, which have an assemblage character in that additions to and/or deletions
from the assemblage ‘rework’ the negotiated order” (Byrne and Callaghan 2013: 36).
Open-ended innovation demands high complexity and complicatedness:
Constrained to low complicatedness, innovation cannot be open-ended since we need
complicated organization to build powerfully adapted and specialized systems. Unstructured
system interactions would make for unmanageably high-dimensional spaces, preventing creative
processes from efficiently exploring the design space (e.g. Stankiewicz 2000; Erwin 2015:7).
Low complexity prevents the operation of chief mechanisms of adaptation, such as
distributedness, parallelism, multifaceted interactions to provide robust feedback, and
exploration of design spaces by testing multiple variations. Such systems are barren since the
patterns that their interactions are allowed to take are pre-determined.
But innovation likewise maintains high complexity and complicatedness:
Complicatedness is maintained since it represents our chief way of organizing design spaces.
While it is an open question whether complicatedness generally increases or not (e.g. Marcot
and McShea 2007; referred to as “complexity”), complicatedness is clearly maintained at high
levels; see Andersson (2013:90; also here referred to as “complexity”.)
Complexity is maintained because the rich interactive capabilities of adapted entities are
expressed distributedly in an arena setting. Intense, dynamic and weakly constrained interaction
creates “seamless webs” where any node will be in close interactive contact with just about the
entire web. This gives us mass dynamics and the phenomena of complexity.
Complicatedness-based approaches do not work well since complicated organization in wicked systems
is constantly changing, and complexity-based approaches do not work since interactive populations are
strongly heterogeneous and changing in wicked systems.
3.3.2 Sub-Wicked Systems
Central examples: Small societies; local social contexts, e.g. relatives, close
friends or workplaces; early human societies.
Differentiating features from wicked systems: Smaller in scope.
Simplicity hook: Smaller scope allows them to fit into the range of human
cognition. They exhibit wicked problems, but ones small enough for us to
Significance: Sub-wickedness is attractive as a basis for dealing with wicked
problems since it (as opposed to approches based on complexity or
complicatedness) fundamentally matches their ontology.
Sub-wicked systems are wicked systems that have not outgrown our capacity to design and govern them
– a capacity that it is no coincidence that we possess: we are adapted specifically for dealing with sub-
Human societies emerged out of the intricate politics of groups of versatile and strongly individualist
Great Apes (>10 million years ago; e.g. Moya-Sola et al. 2009). Acting in such a group demands the ability
to deal with constant social innovation: intrigues, new constellations, secrets, lies, and the relations
between others and between others and oneself (Read 2012.) An important aspect of the unique
evolutionary history of the Homo genus (<2.5-2.0 million years ago; e.g. Antón et al. 2014) seems to have
been the innovation of ways of organizing ever-larger groups of such individualists to simultaneously
reap the rewards of individualism (resourcefulness, intelligence, initiative etc.), large numbers (emergent
team functionality, robustness, etc.), and the capability of combining individuals with culturally
developed complementary function into an emergent group-level functionality (Read 2012). This
represents the emergence of small seamless webs, wickedness and innovation in the cultural sphere
along the lines presented in Sec. (3.3.1).
Sub-wickedness thereby becomes a route – alternative to theories of complicatedness and complexity –
to the crucially important simplicity that we need to build and understand adapted systems. Narratives
embody this capability, and so does the capacity to dynamically manage innovation processes – stories
under construction; what we with minimal expansion of the concept may refer to as negotiation.
We may now identify a number of general conclusions – to be read as a sequence of very short aphorisms
– about the constraints that exist on understanding and intervening in wicked systems. We will offer
suggestions about future pathways for developing such capabilities, as well as integration and
confirmation of some existing pathways and insights.
1. Wicked systems are so strongly and heterogeneously connected that it is impossible to exhaust
even small portions of them empirically to produce a “realistic picture”.
2. “Pictures” must therefore be perspectives, rarely subject to universal agreement.
3. Even if we could obtain a “realistic picture”, this would frequently not help much since the system
changes unpredictably over time – including as a direct result of us interacting with it.
4. Uncertainty includes not only foresight but also e.g. what the problem consists in, what tools are
available, what actors to include.
5. “The game” and its rules frequently change dynamically on similar time scales.
6. The usefulness of models and theory hinges critically on whether, how, and to what extent it is
realistic to decouple the game from its rules; see “short run” Appendix A.
7. Since this is more likely to be realistic for basic, slow-changing, features (e.g. physiology, logical
dilemmas, strongly locked-in features, etc.), useful general regularities tend to be highly abstract.
8. Every wicked problem, however, is critically unique in its details. Interventions to address wicked
problems must therefore be designed in the form of meta-solutions that scaffold the generation
of actual solutions.
9. Navigating innovation pathways in everyday sub-wicked systems is congruous with doing so in
wicked systems: an iterative and reflexive process of alignment, integration and problem solving.
10. Policy can be formulated in the likeness of this capacity rather than of our capacity to design
complicated artifacts (designed, assembled and launched).
11. Reducing wickedness to sub-wickedness is attractive since this preserves more of its ontological
and epistemological features.
12. What we need to pay particularly attention to in such a reduction is:
a. Incomplete and biased perspectives on the wicked system from sub-wicked perspectives
that reflect how we are embedded into the seamless web (culture, education, roles,
b. Wicked systems exhibit more complexity than we can handle: we have an eminently
poor – even outrightly misguiding – intuition for complexity.
13. The suggested response is to:
a. Prioritize the integration of different perspectives.
b. Integrate the use of models as crutches for understanding complexity.
14. Also sub-wicked systems are constantly under the threat of misalignment. We need cooperation
for aligned and directed action and so alignment should also be prioritized.
15. Alignment is also important normatively (deciding what we want to achieve) since, by contrast
with engineering problems, goodness cannot be integrated uniquely at a top level with respect
to external functions. Wicked systems are good or bad in relation to the components that they
contain – components that are, in many ways, in competition – and a “good arena” might have
qualities such as sustainability (inequity and other problems do not amplify) and a balance
between goodness from local perspectives that is acceptable to most.
16. Narrative and negotiation have strong aligning and integrating functions and can form the “glue”
in iterative cycles of sub-wicked approaches.
17. Due to uncertainty and dynamics any propositions and goals should be treated as tentative.
18. Dynamic exploration must include components that are actually or potentially part of the
a. Components in a seamless web are subject to substantial uncertainty; they cannot be
sufficiently declared in mission statements, CV’s etc.
b. We cannot know in advance what parties to include or leave out, nor what roles they
should or will play.
19. Large black-box models (such as detailed predictive planning models) are hard to integrate into
seamless webs: they cannot intermix with the viewpoints, knowledge and experiences of the
participants (e.g. Klosterman 2012).
20. Many wicked problems are so unique and contingent that modeling makes no sense. Complexity
remains important, however, and simple, pedagogical models could be important for building a
better intuition for complex dynamics.
To make these linked points easier to overview, we will now boil them down to three main themes:
1. Uncertainty is intrinsic to wickedness and the issue should not primarily be how we reduce it but
how we deal with it. Dealing with uncertainty is at the core of what dealing with wickedness is
2. Integration of interests, models, tools, viewpoints, expertise, capacities for action (e.g.
authority), and goals is essential, both instrumentally and for normative reasons.
3. Alignment is tightly tied to integration and is essential for maintaining the direction and integrity
4. Dynamics/emergence is at the core of innovation and wickedness, giving rise to uncertainty and
other wicked phenomena. Interventions must therefore be dynamically intermeshed with the
In Table B.1 (Appendix B), we discuss the ten points presented by Rittel and Webber (1973) to describe
wickedness from the here developed perspective.
In Appendix C we review, through the lens of SOS, a set of emerging approaches that generally match
the description provided in the Introduction (Section 1), and suggest understanding such approaches as
sharing a common aim of Harnessing Innovation: there is an emphasis on integration and alignment
between disciplines, actors and perspectives,
and intervention is increasingly conceptualized as
directing and supporting iterative processes of innovation. Such an approach finds a strong meta-
theoretical support in our understanding of the wickedness of socio-eco-technological systems.
Needless to say, a two-dimensional plane representing something “as overwhelming as
overwhelmingness” must necessarily be incomplete in numerous ways. But, as Box and Draper (1987)
famously stated: “all models are wrong; some models are useful,” and the cases where models break
down may be exceptionally useful to the extent that they force us to think along new constructive paths
These themes are widespread but specifically in focus in research tracks such as Post-Normal Science; Funtowicz
and Ravetz 1993; Turnpenny et al. 2010, and Inter- and Transdisciplinarity Research (Darbellay 2015; Lawrence
2015; Ledford 2015.)
(Wimsatt 2002). The point is not to find a “correct model”, but a “useful model”, which we interpret as
a model that helps us make sense of and organize our imagination about systems and problems than
otherwise overwhelm us.
Our hope and intention with the SOS model is that, in furnishing a meta-ontology – a map of ontologies
– it may serve as scaffolding for our imagination: a way of interrogating systems, processes and goals. In
which ways is this a complex problem? Is it complexity placed under control? How does that control work?
How could it work? What are controlled complex systems like? What can be achieved? What are the
trade-offs we’re facing? When disagreements arise over where to place a system, this will bring points
where understanding is not shared to the surface, allowing their resolution. The SOS model hopefully
offers some guidance for answering such questions, and, not least for refining and branching the model
itself. Wicked problems are never solved in the abstract, and above all we aim to help posing them.
In our experience, the SOS model (and in particular diagram) has a particular value as a basis for
discussions and exchanges of different experiences with wicked problems. We think the reason is that it
allows – and invites – participants to think about their issues, cases, problems, goals, and so on, on a
more abstract and domain-free level than the typical wicked-problem-solver is typically inclined to. This
inclination to go into detail is quite natural: anybody working with wicked problems must spend a lot of
time and energy uncovering, understanding and keeping up-to-date with massive amounts of contingent
details. The downside, that we hope to mitigate, is that useful congruencies between cases – a
precondition for the ability to compare and learn – risk drowning in these details along with insufficiently
elucidated differences in terminology and types of goals.
Theoretically, the SOS mapping suggests commonalities among meta-level generating processes of
ontological categories: if self-organization is the causal origin of complexity and assembly the causal
origin of complicatedness, then innovation would be the origin of the wickedness. This points toward a
possible unifying theme among many emerging approaches to sustainability (including the Pathways
approach, e.g. Leach et al. 2007, 2010; Haasnoot et al. 2013; Wise et al. 2014, Transition Management,
e.g. Loorbach 2010, and adaptive governance, Olssson et al. 2006). This theme is their recognition of the
vanity of trying to predict, control or plan-away wickedness, and their shift of focus to embracing and
harnessing these troublesome qualities of wickedness instead (see Appendix C). This also means a shift
towards seeing humans (and their tools) increasingly as fallible as agents and knowers - the future
becomes a historical process where problems, and the tools at our disposal for tackling them, are
constantly changing as part of a wider societal innovation dynamics.
Innovation is essentially unpredictable and cannot be understood in the same way as we may understand
systems where the rules of the game remain fixed, such as in the design of a technological artifact.
Realistically, we may however hope to understand innovation and wickedness on a meta-level, similarly
to how evolution is understood. For example, even if we cannot understand what the consequences of
our actions will be, we may understand what types of consequences may arise, and we may use this
knowledge to build mechanism for detecting, learning, and handling them specifically as they arise. We
see establishing a theoretical connection between innovation and wickedness as a promising future
direction of research.
In closing, we propose (Fig. 3) six rough mappings of typical generating processes, governance
approaches, directionalities of design and governance, and types of organization into the SOS diagram.
These mappings are based on the preceding analysis in this paper, and they can all bear elaboration and
debate. Indeed, they are there as much to stimulate thinking about how they (and other similar
mappings) can be revised and refined as to communicate conclusions about “how things work.”
Figure 3: The SOS diagram allows us to chart system features to relate and differentiate between them. (a)
Generating processes; (b) Governance approaches; (c) Directionality of design and governance; (d) Style of system
organization; (e) Characteristic sources of risk and uncertainty; (f) Relation between structure and relations.
Funding was provided by the Technology Governance in Energy Transitions Profile of the Energy Areas
of Advance at Chalmers University of Technology, by European Commission H2020 FETPROACT-2016
Action ODYCCEUS (grant no. 732942), and by the Swedish Research Council Formas, grant #942-2015-
124. The work has benefitted greatly from discussions in forums provided by the Chalmers Initiative for
Innovation and Sustainability Transitions.
Appendix A: Wickedness and Near-Decomposability
Herbert Simon’s (1962) concept of Near-Decomposability, and Rittel and Webber’s (1973) concept of
wicked problems represent takes on the problems of “designing complex systems”. The opposition
between the two accounts could, however, hardly be stronger (Coyne 2005). Simon’s story is about
systematically conquering overwhelming problems following a top-down procedure. Rittel’s and
Webber’s (1973) story tells us that precisely this strategy is doomed to fail in many of the most important
cases – in particular in front of the types of sustainability problems that we are primarily interested in
While we agree with the diagnosis of Rittel and Webber (1973), we note that Simon did something that
they did not: he provided a generative design space for an important class of problems. Rittel’s and
Webber’s work, by contrast, was essentially negative (a critique) and does not structure the design space
in a similar way. They tell us that the design spaces of the paradigm that Simon represents do not work
for wicked problems, but do not provide much in way of an alternative.
In our quest to provide new design spaces also for wicked systems we may, however, still benefit from
Simon’s explicitness: we may use his concept of Near-Decomposability to understand why wicked
problems are not like the “tame problems” for which Simon’s prescriptions work so wonderfully. After
all, Rittel and Webber (1973) define wickedness more or less precisely as a stubborn recalcitrance to the
type of approach that Simon (1962) proposed.
Near-Decomposability (see Fig. A1a) is a type of patterning of interaction pathways that allows for strong
simplifications. Essentially it means that the rate of interactions between sub-components within a
component (Inner Environment) is much higher than the rate of interaction between the component and
other components on its own level of organization (Outer Environment). The Inner and Outer
Environments are separated by the component Interface, which can be seen as the emergent (designed
or evolved) totality of the component: its interaction modalities and pathways of interaction.
The archetypical way in which components are separated is physical distance and/or enclosure (usually
in technological components for example), but the separation may be maintained in any manner that
achieves the sought structuration of interaction patterns.
Apart from a difference in density of interactions within and between components, the Interface also
tends to channel interactions so that they occur in forms that the Inner Environment is adapted to deal
with. For example, humans may accept energy from the environment, but exposing us to heat or pouring
nutrients over us will not work: energy must enter in very specific forms along very specific pathways if
we are to properly make use of it.
The Interface can, for many purposes, be used as a shortcut to everything below its own level of
organization: we may use a smart phone or an automobile with virtually no knowledge about its inner
workings. The Interface cuts short potential system-wide cascades effects of changes, and the process of
creating representations of such systems (on some level, e.g. by gathering empirical data) will converge:
more effort yields less and less relevant details to add. Innovation or assembly may therefore focus on
one small part of a system at a time.
Figure A.1: Illustration of Simon’s (1962) the concept of Near-Decomposability. Interaction in the Outer
Environment happens only via component Interfaces. If we nest this style of organization hierarchically we obtain
a neat level hierarchy where each level may be understood with only summary knowledge about the levels above
and below. This is an ideal situation for building models as it allows for strong control and powerful assumptions,
and it also allows us (or any adaptive process) to erect an arbitrary number of hierarchical levels,
compartmentalizing in principle any number of degrees of freedom, behind a simple interface.
This mechanism of simplification-by-compartmentalization is so drastic that it may entirely reset the
number of degrees of freedom of a system on each level of organization. In principle, we may go on
nesting systems in this hierarchical manner forever (see Fig A.1b). What prevents us from actually doing
so is simply that we run out of scales over which to operate. Indeed, opening up new scales to occupy
with levels of organization is a premier cause of major transitions in engineering (see e.g. Feynman 1976).
Near-Decomposability is, notably, valid only over a time scale that Simon refers to as “the short run”
(Simon 1996). If the time scale is too long, then factors outside of the Inner Environment will begin to
disturb the dynamics, and assumption that the “enclosure” is constant will become invalid. For example,
a suitable “short run” for the study of traffic would be minutes and hours. Over time scales shorter than
minutes not much would happen, and if we move to several days, the dynamics would more or less
repeat itself. Moving to even longer time scales, roads, types of vehicles, regulations and so on would
begin to change. Short runs are not just hard to find in wicked systems, there is no guarantee that there
even exists a meaningful short run. Wicked Systems may be seen as systems that largely lack relevant
short runs and thereby also opportunities for powerful formal modeling.
Levels of organization have been described as “stable foci of regularity and predictability”, and as such,
the existence of levels of organization in itself must be expected to act as attractors to adaptive
processes: they should self-reinforce and self-stabilize over time (Wimsatt 1994) since adapting systems
evolve in such a way as to minimize uncertainty in their environment (Levins 1968).
However, as Wimsatt (1994) points out, this is only half the story. In a competitive situation (i.e.
wickedness,) entities under competition (be they organisms, organizations or humans) will themselves
seek to be as unpredictable as possible to their competitors, which would make it adaptive to also break
up level hierarchies.
Wimsatt (1975:181-185) furthermore argues that Simon’s principles take only ease of design and
assembly into account, not optimality of function. Optimality of function, of course, may be under strong
selection pressure, and when it is we should expect this to cause breakdowns in level-hierarchical
organization. The reason is that there is no convincing argument for why a style of organization that
simplifies assembly and design would also make for optimal function. Intuitively this expectation seems
to be carried out in reality. Technological artifacts that are mass-produced (strong pressure for
adaptability, cheap assembly and easy maintenance) contain more standard components, and are
simpler in their architecture, than ones that are highly specialized and produced only in very few
numbers. Compare for example a standard laptop to a space shuttle; see example under Point #5, Table
A2, Appendix B.
Simon’s (1973) own take on “Ill-Structured Problems” is worth mentioning as it illustrates the friction
between how different paradigms conceive of problems: their specifications and what counts as
solutions. He here deals with the fact that many important problems are very hard to specify in such a
way that they can be solved following prescriptions along the lines of his famous (1962) paper. He
concludes that a dynamical process is needed in cases where the problem cannot be posed up-front, and
what he describes is a process of dividing-and-conquering: even if the problem as a whole is Ill-
Structured, all actual problems fed to the problem solvers remain Well-Structured (i.e. Near-
The process that he proposes takes an initial broad problem specification and goes on to decompose and
dynamically explore what it entails (working out sub-problems, locating people with skills, permissions
and so on, locating and mobilizing assets, etc.) This includes going back to revise the initial specification
if necessary (Simon 1973:315). The process then slowly works its way into a complete specification that
can be agreed upon as a solution.
While this is certainly in the direction of a Harnessed Innovation approach, with elements of both
integration and alignment, there is a distinct difference between this process and Harnessed Innovation
with regard to what counts as solving the problem.
His example of designing and producing a new battleship clearly demonstrate how he misses the mark
when it comes to wickedness. The problem specification tells the problem solving system what the result
should be like in terms that are intrinsic to the solution. In this case that it should have such and such
armament, armor, propulsion, and so on. The process then realizes a solution that works out all the nasty
little details in how to actually go about doing this, and that can deal with contingencies such as that
some criterion is too expensive, impossible to implement and so on.
This is not what we mean by a wicked problem, but it can easily be transformed into one by specifying
the problem, instead, in relation to the wicked system in which the battleship is expected to act. For
example, if “ruling the high seas” would be the goal (which, in the end, it surely was) then the battleship
design problem (as specified in Simon’s example) could be successfully solved but still fail to properly
address the larger wicked problem. And even if it did solve the problem, it would still only be temporary
and it would be part of a much larger technological, economic and political process across historical time.
Appendix B: Analysis of Rittel’s and Webber’s ten points
Table B.1: Expressions and causal interpretations of wickedness
The ten expressions of wicked problems listed by Rittel and Webber (1973)
1: “There is no definitive formulation of a wicked
problem” (Rittel and Webber 1973:161-162).
According to Rittel and Webber, this has to do with
the relation between understanding a problem and
one’s ideas about how to solve it.
They take the problem of poverty as an example to
illustrate what they mean. “Does poverty mean low
income? Yes, in part. But what are the determinants
of low income? Is it deficiency of the national and
regional economies, or is it deficiencies of cognitive
and occupational skills within the labor force? If the
latter, the problem statement and the problem
"solution" must encompass the educational
processes. But, then, where within the educational
system does the real problem lie? What then might
it mean to "improve the educational system"? Or
does the poverty problem reside in deficient
physical and mental health? If so, we must add
those etiologies to our information package, and
search inside the health services for a plausible
cause… To find the problem is thus the same thing
as finding the solution; the problem can't be defined
until the solution has been found.”
They go on to critique the prevalent systems
approaches of that time…
“This property sheds some light on the usefulness of
the famed "systems-approach" for treating wicked
problems. The classical systems-approach of the
military and the space programs is based on the
assumption that a planning project can be
organized into distinct phases. Every textbook of
systems engineering starts with an enumeration of
these phases: "understand the problems or the
mission," "gather information," "analyze
information," "synthesize information and wait for
the creative leap," "work out solution," or the like.
For wicked problems, however, this type of scheme
does not work. One cannot understand the problem
without knowing about its context; one cannot
meaningfully search for information without the
orientation of a solution concept; one cannot first
understand, then solve. The systems-approach ‘of
Two important causal features of wickedness
are (i) the “seamless web” structure of the web
of associations through which causation
travels, meaning that entities are widely and
strongly interconnected across far-flung
domains; (ii) the dynamical cascades of
qualitative change that travel through and
transform this seamless web; and (iii) the fact
that those that act upon the system are
embedded into it, using their particular
embedding as a lens through which they see
the system (in effect mapping it to a personal
sub-wicked model that they can grasp; Sec.
These features pose several problems to any
effort to gather information about the system
first and to then produce a solution based on
The first is that any problem will be entangled
between a large number of domains (e.g.
economy, education etc.). We prefer to solve
problems from the standpoint of domains since
our training and expertise, and thereby
experience, are based on domains.
Information-gathering, almost necessarily, is
biased by the domain from the vantage point of
which it takes place.
Even if we spend a great deal of effort on
gaining an overview, our search outward for
more important factors will not converge as
neatly as a similar search in, say, a complicated
system: we keep finding more and more
important factors, across longer and longer
time scales, and eventually we may come to the
true but rather useless conclusion that
“everything is connected.”
But, as if this would not be enough, it would not
help even if we could obtain a perfect overview:
the system keeps changing qualitatively over
time, and it does so partly as a direct result of
us intervening in it. The world in which we
insert our cleverly designed intervention is not
the same world anymore: it has our
the first generation’ is inadequate for dealing with
…and then to point to more “argumentative
processes” where ideas about the problem and the
Approaches of the ‘second generation’ should be
based on a model of planning as an argumentative
process in the course of which an image of the
problem and of the solution emerges gradually
among the participants, as a product of incessant
judgment, subjected to critical argument.”
intervention in it, and we have very little of an
idea about how it will interact with the agents,
ideas and artifacts within it.
The first-generation systems approaches that
Rittel and Webber refer to treat wicked systems
as if they had been complicated systems: as if
the search for information would converge, as
if the problem would be delineable, as if the
problem would remain the same, and so on.
Efforts to accommodate wickedness in such an
ontological structure will maximally take us into
the trans-complicated regime: we remain
grounded in complicatedness; i.e. we project a
wicked space onto a simpler complicated
space. The big problem is not the simplification
per se but the ontological mismatch between
model and problem. No amount of empirical
detail can help us in that regard.
Their suggested move to second-generation
approaches describes a move that recognizably
is in the direction of what we refer to as
Harnessed Innovation (Sec. 5).
2: “Wicked problems have no stopping rule”
(Rittel and Webber 1973: 162).
They here compare wicked problems to the
problem of playing chess, where a problem solver
has definite criteria to determine when the
problem is solved. They state: “because there are
no criteria for sufficient understanding and
because there are no ends to the causal chains that
link interacting open systems, the would-be
planner can always try to do better.”
Termination, instead, happens “…not for reasons
inherent in the ‘logic’ of the problem. He stops for
considerations that are external to the problem: he
runs out of time, or money, or patience. He finally
says, ‘That's good enough,’ or ‘This is the best I can
do within the limitations of the project,’ or ‘I like
this solution,’ etc.”
This expression of wickedness has to do with
the fact that the time horizon of consequences
is potentially unlimited and wholly
uncorrelated with our short foresight horizon.
The result is that other factors must determine
when projects begin and end.
It may also be noted that wicked problems do
not only lack a stopping rule: they also lack a
“starting rule.” We always enter wicked
problems at what seems to be a too late point
The reason for this is that nobody wants to own
wicked problems and they are often hard to
attribute to somebody. They arise in an arena,
between rather than within systems that
“belong” to agents, and they are externalities
par excellence. Consequently, our first reaction
in front of them is to think “that’s not my
problem” and then try to figure out whose
problem it probably is instead. Wickedness is
likely to cause finger-pointing.
3: “Solutions to wicked problems are not true-or-
false, but good-or-bad” (Rittel and Webber 1973:
Related to Point #2, they here describe how solving
wicked problems is very different from solving tame
problems: their quality is judged from the
standpoint of what different actors want (and
understand) rather than from objective and
“For wicked planning problems, there are no true or
false answers. Normally, many parties are equally
equipped, interested, and/or entitled to judge the
solutions, although none has the power to set
formal decision rules to determine correctness.
Their judgments are likely to differ widely to accord
with their group or personal interests, their special
value-sets, and their ideological predilections. Their
assessments of proposed solutions are expressed as
"good" or "bad" or, more likely, as "better or worse"
or "satisfying" or "good enough."
As we note in Sec. (4), and as many others note
in our examples in Sec. (5), societal problems
belong to their own constituent parts.
This is highly different from, say, a machine. The
notion that an automobile should be “good”
from the point of view of its spark plugs is
absurd. But imagine what the design problem
would look like if a car had to be good for its
parts as opposed to its users.
We want wicked systems to be good as arenas
of interaction for their constituent
components: they should be arenas in which
interactions do not lead to bad effects for the
agents, neither in the short run nor in the long
run; akin to a Pareto efficient state, but in a
dynamic rather than static sense.
4: “There is no immediate and no ultimate test of
a solution to a wicked problem” (Rittel and
Webber 1973: 163).
“With wicked problems, on the other hand, any
solution, after being implemented, will generate
waves of consequences over an extended – virtually
an unbounded – period of time. Moreover, the next
day's consequences of the solution may yield utterly
undesirable repercussions which outweigh the
intended advantages or the advantages
accomplished hitherto. In such cases, one would
have been better off if the plan had never been
The full consequences cannot be appraised until the
waves of repercussions have completely run out,
and we have no way of tracing all the waves
through all the affected lives ahead of time or within
a limited time span.”
This point arises due to ontological uncertainty
(Sec. 3.3.1), which, in turn, is due to the
cascades of qualitative transformation that
propagate and interact in the system; the first
quoted passage to the left also gives clear
evidence that this picture corresponds to how
Rittel and Webber understood the underlying
The consequence is that we must constantly
monitor effects and be prepared to alter our
strategies and goals according to how realities
change. If we remain committed to descriptions
of realities in the past, our actions will become
increasingly misguided as time goes on.
5: “Every solution to a wicked problem is a ‘one-
shot operation’; because there is no
opportunity to learn by trial-and-error, every
attempt counts significantly” (Rittel and Webber
Here, Rittel and Webber touch upon the reflexivity
of wicked systems, how we are immersed in them,
and how, since they cannot be covered by models,
This point underscores that wicked problems
are not engineering problems.
The difference may be illustrated with a limit
case where this problem applies also to
complicated systems, which happens when
they are particularly overwhelming and
expensive – at the limit of what we can pull off:
and are too large and slow to be replicated
otherwise, we cannot address them vicariously
(Campbell 1965); i.e. through an “offline”
controlled experimental representation.
Games of chess can be repeated, and we can
practice different strategies at little cost and
consequence. Not so for wicked problems:” every
implemented solution is consequential. It leaves
‘traces’ that cannot be undone. One cannot build a
freeway to see how it works, and then easily correct
it after unsatisfactory performance. Large public-
works are effectively irreversible, and the
consequences they generate have long half-lives.
Many people's lives will have been irreversibly
influenced, and large amounts of money will have
been spent – another irreversible act. The same
happens with most other large-scale public works
and with virtually all public-service programs. The
effects of an experimental curriculum will follow the
pupils into their adult lives.”
When the space shuttle Columbia first flew into
space April 12 1981, it was the first time the
entire system had been in motion. Indeed, no
system remotely like it had even been tested
The leap from unpowered atmospheric flight
(with a different prototype vehicle – the
Enterprise) to launch, space mission,
atmospheric re-entry and landing was not a
small one. Confidence in success was
sufficiently low that most involved were
probably highly nervous, but high enough that
the crew certainly did not consider it a suicide
mission: everybody fully expected them to get
home in one piece.
The fact that this was at all possible is a
powerful testament to the power of
complicatedness and Near-Decomposability as
a way of organizing design spaces. In most
cases, however, we do not have to forego the
powerful design feedback we get from testing
the entire system under realistic conditions (in
particular not with access to high-quality
Our inability to learn about wicked systems
from experience is due to ontological
uncertainty and the long time scales over which
wicked problems are addressed. Conditions
may have changed dramatically, and in
unknown ways, as we move to a new problem
instance. What we think we have learned may
just as well prevent success in the future since
the suitability of certain past actions may have
been contingent on conditions that no longer
exist, and that we may never even have been
The unlimited time horizon for consequences
also plays here in an important way:
experiments do not end when we think they do.
Rittel and Webber note that the effects of an
experimental curriculum will follow pupils into
their adult lives, but in fact, the effects can be
traced even further since the pupils will interact
with the rest of the system throughout their
An effect of complexity in this context is that
effects do not even necessarily abate over time:
at any time, a downstream effect may trigger a
powerful cascade effect.
6: “Wicked problems do not have an enumerable
(or an exhaustively describable) set of potential
solutions, nor is there a well-described set of
permissible operations that may be incorporated
into the plan” (Rittel and Webber 1973: 164).
“…normally, in the pursuit of a wicked planning
problem, a host of potential solutions arises; and
another host is never thought up. It is then a matter
of judgment whether one should try to enlarge the
available set or not.”
They go on to illustrate how ideas can direct us in
qualitatively different directions, each yielding
propositions that could not possibly have been
conceived within the framework of the other:
“What should we do to reduce street crime? Should
we disarm the police, as they do in England, since
even criminals are less likely to shoot unarmed
men? Or repeal the laws that define crime, such as
those that make marijuana use a criminal act or
those that make car theft a criminal act? That
would reduce crime by changing definitions. Try
moral rearmament and substitute ethical self-
control for police and court control? Shoot all
criminals and thus reduce the numbers who commit
crime? Give away free loot to would-be-thieves, and
so reduce the incentive to crime? And so on.”
This problem is about the overwhelmingness of
the design space of wicked systems. Contrary to
complicated (and to some extent complex)
systems interactions are not strongly patterned
and there is very little to guide us in a
systematic search for solutions or problem
The lesson that can be derived from our work
here is that the only viable way of structuring
wicked problems is by projecting them onto
simplified sub-wicked spaces that we may grasp
intuitively. We may then design scaffolds for
processes in which these sub-wicked
representations are developed over time. That
is the lowest level on which we can be
See also the analysis of Points #1 and #2.
7: “Every wicked problem is essentially unique”
(Rittel and Webber 1973: 164-165).
The conditions in a city constructing a subway may
look similar to the conditions in San Francisco, say;
but planners would be ill-advised to transfer the
San Francisco solutions directly. Differences in
commuter habits or residential patterns may far
outweigh similarities in subway layout, downtown
layout and the rest. In the more complex world of
social policy planning, every situation is likely to be
If we are right about that, the direct transference of
the physical-science and engineering thoughtways
into social policy might be dysfunctional, i.e.
positively harmful. "Solutions" might be applied to
seemingly familiar problems which are quite
incompatible with them.
The chance that two identical problems would
appear in a wicked system is slim. This does not
mean that we cannot learn about solving
wicked problems, but it does mean that we
must be wary about trying to learn on a too
As noted in the analysis of Point #5, learning on
a too specific level carries the risk of lock-in to
operations and strategies that no longer apply.
Moreover, even subtly altered conditions may,
in an environment of high complexity, produce
arbitrarily large deviations in outcomes due to
chaos (Sec. 3.1.2.)
8: “Every wicked problem can be considered to be
a symptom of another problem” (Rittel and
Webber 1973: 165).
Delimiting a wicked problem is a vain pursuit, and
Rittel and Webber here deal both with the multi-
domain and the multi-level nature of wickedness as
well as with how problems change dynamically if
we deal with parts of them.
Some observations that they make:
“The process of resolving the problem starts with
the search for causal explanation of the
discrepancy. Removal of that cause poses another
problem of which the original problem is a
"symptom." In turn, it can be considered the
symptom of still another, "higher level" problem.
Thus "crime in the streets" can be considered as a
symptom of general moral decay, or
permissiveness, or deficient opportunity, or wealth,
or poverty, or whatever causal explanation you
happen to like best.”
“Marginal improvement does not guarantee overall
improvement. For example, computerization of an
administrative process may result in reduced cost,
ease of operation, etc. But at the same time it
becomes more difficult to incur structural changes
in the organization, because technical perfection
reinforces organizational patterns and normally
increases the cost of change. The newly acquired
power of the controllers of information may then
deter later modifications of their roles.”
“…it is not surprising that the members of an
organization tend to see the problems on a level
below their own level. If you ask a police chief what
the problems of the police are, he is likely to demand
This is a direct result of the seamless web
organization of wicked systems combined with
cascades of transformation across this web. It
also connects to our observation that “the
game” cannot be delineated from “the rules of
the game” (Sec. 3.3.1).
9: “The existence of a discrepancy representing a
wicked problem can be explained in numerous
ways. The choice of explanation determines the
nature of the problem's resolution” (Rittel and
Webber 1973: 166).
“’Crime in the streets’ can be explained by not
enough police, by too many criminals, by
inadequate laws, too many police, cultural
deprivation, deficient opportunity, too many guns,
phrenologic aberrations, etc. Each of these offers a
This point underscores the importance of
alignment and integration in interventions in
wicked systems, and relates to discussions
above (Sec. 4; Point #3) about the nature of
“goodness” in solutions of wicked problems, as
well as to the irreducible co-existence of a
multitude of perspectives (Sec 4; projections of
wickedness onto sub-wicked mental models, or
formal models with complicated or complex
direction for attacking crime in the streets. Which
one is right? There is no rule or procedure to
determine the ‘correct’ explanation or combination
Rittel and Webber boil this point down to an
interesting observation: “The reason is that in
dealing with wicked problems there are several
more ways of refuting a hypothesis than there are
permissible in the sciences.”
Briefly put, it is hard to push anybody sufficiently
into a corner that they logically must abandon their
“The mode of dealing with conflicting evidence that
is customary in science is as follows: "Under
conditions C and assuming the validity of hypothesis
H, effect E must occur. Now, given C, E does not
occur. Consequently H is to be refuted." In the
context of wicked problems, however, further
modes are admissible: one can deny that the effect
E has not occurred, or one can explain the
nonoccurrence of E by intervening processes
without having to abandon H.”
The effect is that:
“Somewhat but not much exaggerated, you might
say that everybody picks that explanation of a
discrepancy which fits his intentions best and which
conforms to the action-prospects that are available
to him. The analyst's “world view” is the strongest
determining factor in explaining a discrepancy and,
therefore, in resolving a wicked problem.”
We cannot do much about the fact that
different parties will be linked into a wicked
system in different ways. In some cases, such
interests may be irreconcilable, but in many
cases, negotiation and mutual understanding
may open up for more inclusive resolutions
where losses in one area are compensated by
gains in some other area. If a problem is seen as
serious, if responsibility can be accepted, and if
trust can be established, willingness to strike
compromises will also increase.
What Rittel and Webber says here can be
interpreted as follows: compared to in science,
where we have a universally agreed-upon (if
not always perfectly functioning) system for
deciding who is right, we have nothing similarly
strong and forcing in policymaking. In other
words: we largely lack a crucially important
Unless alignment is pursued, the problem-
solving sub-wicked systems (e.g. consisting of a
collection of persons and/or organizations)
cannot be configured and directed. We note in
Secs. (4-5) that alignment is indeed among the
top concerns around which Harnessed
Innovation approaches are constructed.
10: “The planner has no right to be wrong” (Rittel
and Webber 1973: 166-167).
This point deals with the fact that wicked
problems are different than other scientific
problems. Referring to point #3, solutions are not
right or wrong but rather good or bad: they are
not just “hypotheses offered for refutation”.
“…the aim is not to find the truth, but to improve
some characteristics of the world where people
live. Planners are liable for the consequences of the
actions they generate; the effects can matter a
great deal to those people that are touched by
Rittel and Webber here compare wicked
problems to problems in other fields. This point
applies not only to wicked problems but to any
problem whose solution is “good or bad” for
somebody else (e.g. surgery.)
The unlimited time horizon for consequences,
however, makes this responsibility different in
wicked systems. Just like we cannot determine
when we are done solving a wicked problem,
we cannot determine when it has been
successful or not.
Historical interventions may have strong and
long-lasting downstream consequences that
can produce persistent ill effects and conflict.
These effects may have been unforeseeable or
not of moral concern (even seen as positive) at
the time they were caused. Colonialism,
eugenics and anthropogenic global warming
would be examples of this in different ways.
Appendix C: Harnessing innovation to deal with wicked problems
We now wish to argue that the type of modern intervention approaches that we initially (Sec. 1) referred
to as “Harnessed Innovation” evolves roughly along the lines that we just proposed (Sec. 4). We thereby
wish to find and establish links between the here developed foundation of wickedness in innovation and
ongoing work for which such a deepened causal understanding could provide integration, alignment and,
hopefully, new ideas.
In the SOS framework, Harnessed Innovation can be conceptualized as the design of a controlled sub-
wicked innovation process that interfaces with a wicked societal innovation process. It represents a move
from traditional complicated and trans-complicated systems-oriented approaches – with emphasis on
control and prediction – to a sub-wicked type of organization.
We here review a small selection of proposed examples of Harnessed Innovation to detect and
characterize unifying concerns and responses that can be tied to our causal and meta-theoretical
Nickerson and Sanders (2013) deal with collaboration between large numbers of governmental and non-
governmental organizations (an “alphabet soup” Nickerson and Sanders 2013:1) in the face of
emergencies (e.g. the Deepwater Horizon accident, hurricane relief etc.) that are highly urgent, unique,
fluid and multi-faceted. A central concern is that of integrating and aligning assets toward achieving a
common goal. They develop the concept of an “enterprise leader”: an integrating and aligning agent that:
(i) Spans the boundaries of many agencies through deep knowledge about how they work, what they do
and how they see the world. (ii) Can act without formal authority, on the basis of skillfully negotiated
commitments rather than command (formulating shared interests, a sense of common mission). (iii)
Builds and leverages boundary-spanning networks to establish communication channels, trust and
reputation. (iv) Dynamically steers the dynamics as it rapidly unfolds in an unpredictable manner.
Brown et al. (2010) aim to “stimulate our imagination” about how we approach wicked problems. Russel
(2010:56-58) kicks off the volume with a set of “guiding principles” based on thorough philosophical
considerations about epistemological, ontological and ethical issues. Of central importance is a view of
complexity (overwhelmingness in our terminology) as responsible for: (i) partiality – our inability to know
everything about the systems; (ii) plurality – of perspectives and ways of knowing; (iii) provisionality –
partiality and plurality causes fallibility, and so knowledge must remain provisional and open to change.
Normative prescriptions are formulated on this basis, e.g.: a “social process of critical deliberation”;
explicitness about underlying values, assumptions and interests; considerations should extend as far as
necessary; legitimization of knowledge and action. In summary, the principles focus strongly on action in
the face of intrinsic and multi-faceted uncertainty, and the prescriptions emphasize dynamics,
integration and alignment.
Transition Management (Loorbach and Rotmans 2006; Loorbach 2010) is in many ways representative
for how change is envisioned in the sustainability transitions community (e.g. Markard et al. 2012): a
transition (as opposed to lock-in) is a period where agency counts, so where it will go can be affected if
we manage the transition wisely: if we dynamically navigate and construct a feasible pathway to where
we want to go. The Transition Management Cycle (Loorbach 2010:173) summarizes the idea behind the
approach as four steps: (i) Problem structuring, envisioning and establishment of the transition arena;
(ii) Developing coalitions, images and transition agendas; (iii) Mobilizing actors and executing projects
and experiments; (iv) Evaluating, monitoring and learning.
The “pathways approach” (Leach et al. 2010) also has a transition focus and a signature feature is that it
ties normative value- and power-related aspects tightly to the instrumental aspects of navigating and
constructing transition pathways: it is not just a matter of integrating hard capabilities (models, expertise
etc.) but also of involving those that are affected as parts of the system. Three of the four main hurdles
to better approaches to sustainability that they list –dynamics, incomplete knowledge and multiple
framings – have direct bearing on the here-described causal structure of wickedness (Leach et al. 2010:3-
Adaptive Governance (Olsson et al. 2006) deals with transition pathways from a different intellectual
trajectory (e.g. resilience, Folke et al. 2010 and panarchy, Gunderson and Holling 2002), focusing on
socio-ecological rather than socio-technical systems. The overall view of transitions is, however, highly
congruent to that of the former two examples: a lock-in, a window of opportunity, and a swift and
tumultuous transition phase. The latter is characterized by uncertainty and must be managed to lead to
a beneficial state (“adaptive governance”, ensuring resilience of ecosystems threatened by collapse.)
Preparing for navigating the transition involves development around three key factors: building
knowledge, networking and leadership. The role of the leader is similar to that described above by
Nickerson and Sanders (2013).
Turnheim et al. (2015) point to the richness, yet lack of integration, among approaches for analyzing and
governing transition pathways, reviewing the current literature on this topic. Embodying different
methodologies and perspectives, they argue that these may be used as mutually complementary
components in more versatile synthetic approaches. What is proposed is an iterative process of
alignment and bridging to bring the components into conversation as they operate across the historically
unfolding innovation process to be governed and assessed. This recalls our biological examples of
symbiotic origins of high-level complicated systems (Sec. 3.1.1) which (perhaps notably) ended up in
wholly transformed components, entirely subservient to the emergent synthetic functions (although it
began that way, we do not think of eukaryotic cells as “combinations between bacteria”).
Other approaches share the same basic picture of innovation and wicked problems, but are more
planning-oriented, aiming to build foundations for change. One prominent such example is backcasting
(see Quist 2006 for a review). For example, Holmberg and Robért (2000) addresses the question of
“how can ecology and economy be merged together into one strategy that makes sense in the short
term as well as in the long term, and from a business perspective as well as for the common good?”
(Holmberg and Robért 2000:292.) Backcasting is organized around target pictures as tools of alignment
and integration (of actors, tools, etc.), and collective problem formulation-and-solving. Target pictures,
and ways of getting there, are reflexively refined to a point where they can generate agreement and
serve as a basis for future aligned and integrated action. Backcasting is argued (see also Dreborg
1996:817) to embody and formalize the principles according to which we solve problems in everyday
life: everyday problems are see as miniature versions of larger and more long-term society-level
problems Holmberg and Robért (2000:296).
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