Corresponding author:Cliff Hooker.Cliff.Hooker@new-

castle.edu.au

e and wicked problems

Design, scienc

Robert Farrell and Cliff Hooker, School of Humanities and Social Sciences,

University of Newcastle, Callaghan, NSW 2305, Australia

We examine the claim that design is demarcated from science by having wicked

problems while science does not and argue that it is wrong. We examine each of

the ten features Rittel and Weber hold to be characteristic of wicked problems

and show that they derive from three general sources common to science and

design: agent finitude, system complexity and problem normativity, and play

analogous roles in each. This provides the basis for a common core cognitive

process to design and science. Underlying our arguments is a shift to a strategic

problem-solving conception of method in both disciplines that opens up new

opportunities for synergetic cross-disciplinary research and practice.

� 2013 Elsevier Ltd. All rights reserved.

Keywords: wicked problems, design methodology, scientific method, episte-

mology, complexity

An important class of argument intended to distinguish design from sci-

ence is focussed around the claim that design is characteristically faced

with wicked problems whereas science is not. The two kinds of prob-

lem are argued to require different skills and methods for their solution. There-

fore, design and science are distinct types of intellectual study and production.

An influential position of this kind was set out by Rittel and Weber in their

landmark paper:

. the classical paradigm of science and engineering e the paradigm that

has underlain modern professionalism e is not applicable to the problems

of open societal systems. . the cognitive and occupational styles of the

professions e mimicking the cognitive style of science and the occupa-

tional style of engineering e have just not worked on a wide array of social

problems. . We shall want to suggest that the social professions were

misled somewhere along the line into assuming that they could be applied

scientists e that they could solve problems in the ways that scientists can

solve their sorts of problems. The error has been a serious one.

The kinds of problems that planners deal with e societal problems e are

inherently different from the problems that scientists and perhaps some

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682

classes of engineers deal with. Planning problems are inherently wicked.

(Rittel & Webber, 1973, p. 160).1

Rittel and Weber label as ‘tame’, in contrast to wicked, problems they claim are

typical of science. “For any given tame problem, an exhaustive formulation can

be stated containing all the information the problem-solver needs for understand-

ing and solving the problem.” (p. 161) “. their mission [solution goal] is clear.

It is clear, in turn, whether or not the problems have been solved.” (p. 160).

Planning is a species of design, the design of some aspect of societal conditions,

broadly understood. The standard design or planning case starts with a brief

(from a client, employer, etc.); other, less structured, origins also occur but treat-

ing the standard case suffices for these as well. The brief sets out a characterisa-

tion of the problem that motivated it, typically some social imbalance,

dislocation or aggravation, and the kind of solution goal or goals normative

for the work, here the societal condition that is desired in place of the present re-

ality. Thus if planning, but not science, is characterised by wicked problems so,

clearly, is thewhole of design to the degree that it resembles a social planning sit-

uation. Pretty clearly this will be true of all those areas of design that involve any

serious formof innovation against client briefs, from advertising to architecture,

from software engineering to soft furnishings. So if planning is separated from

science bywicked problems so also is thewhole, or virtually thewhole, of design.

At the time of writing, Rittel and Webber were responding specifically to the

disappointed expectations aroused by the new systems approaches to problem

solving that would bring the social sciences within science and engineering,

and more generally to still broader claims for computational approaches to

mind andartificial intelligence, engineering and formalmanagement approaches

to problem solving, and the like that would permit subsumption of psychology

generally (thence economics, etc.) and so also design under the prevailing logical

conception of scientific rationality (cf. Cross, 2007). Thoughmuch has changed,

their negative response to such rationalising ambitions remains widely sup-

ported throughout the literature on design process. Cross, for example, claims

that one of the reasons for distinguishing design from science is that “design

problems are ill-defined, ill-structured, or ‘wicked’” (1982, p. 224) whereas sci-

ence problems are mere ‘puzzles’2 to be solved by applying well known rules

to the data given. In a similar vein Willem quotes Archer as saying that “if the

solution to a problem arises automatically and inevitably from the interaction

of the data, then the problem is not, by definition, a design problem”; Willem

goes on to explain that “it is not an ill-structured problem” (1990, p. 44).

In this paper we will examine the arguments presented for the claim that wick-

edness of characteristic problems divides design from science as cognitive prob-

lem-solving processes, that is, that the cognitive or problem-solving nature of

the two processes are importantly different. We will accept that it is legitimate

Design Studies Vol 34 No. 6 November 2013

Design & Wickedness

to make various distinctions between problems of the tame and wicked sorts,

and thus of the methods and skills required to solve them. But contrary to

common-enough talk where it is made to look as if a problem domain is either

all fully tame or all fully wicked, with nothing in between, the tame/wicked

distinction is not a unitary whole but is made up of a number of different fea-

tures each varying in its degree of tameness and wickedness across problems.

The effect of this is to undercut any argument that simplistically concludes

fromexhibiting somewicked problems in design and some tameproblems in sci-

ence that there is a principled division between design and science. Instead, we

shall argue that each of design and science will contain various problems that to

varying degrees are wicked across various features of wickedness, and comple-

mentarily for tameness. To defend a principled difference in cognitive process

between design and science it is then necessary to show that these multiple dif-

ferences of degree nonetheless support a principled difference of kind in cogni-

tive process. But Section 2 will show that this cannot be done.

These days this last claim is the more important one. This is because many more

designers todaywould nodoubt allow that there is no simple division of problems

between design and science, yet nonetheless want to hold that there are important

cognitive differences between the two. For instance, Zeisel (2005) and Koskinen,

Zimmerman, Binder, Redstrom, andWensween (2011) hold that research occurs

within, and about, the design process, but the latter remains distinctive. Our con-

traryambitionhere is to show that, at least in respectof their coreproblem-solving

processes, there is no arguable difference between design and science. (The differ-

ences, which remain real, must lie in their external pragmatic conditions and their

cognitive consequences in turne see below.) It is crucial to doing this that we also

shift our conception of science, in particular of scientificmethod, from thatwhich

prevailed in the 1970s when Rittel and Webber published their paper to a more

adequate one which gives a prominent place to strategic problem solving directed

at multi-valued knowledge acquisition. Such a notion was not always available.

For instance, behindCross’ notion above, that science problems aremere puzzles

to be solved by applyingwell known rules to the data, lies the assumption that sci-

entific research is fixed by logic, that scientific method is a logical machine that

takes data as input and generates true or most probably true theories as output

using only sound logical inferences. But this once dominant conception of scien-

tific method is now generally agreed by scholars of science to be fundamentally

flawed3 and we are free to shift conceptions. Then, as we shall show, design and

science turn out to share the same core cognitive process. Thus, rather than

dividing design from science cognitively, the distinction helps unite them.

The analysis to follow claims to reveal wicked problem resolution as having a

core cognitive process at its heart deriving from agent finitude e manifested in

limited capacity, resource scarcity and ignorance e together with complexity

and normative constraint. The kind of cognitive process we have in mind is

indicated by that found in Goel’s study of design process (Goel, 1995): (I) an

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initial phase of problem space formation where the context of the design situ-

ation (e.g. location, interested parties), the general possibilities of the design sit-

uation, the putatively desired outcome of the design process and the normative

constraints applicable to it are each initially characterised, followed by (II) a

development/exploration phase where a trial space of various potential partial

solutions (e.g. in sketch or model form) is developed and explored and are al-

lowed to mutually interact and are modified, often in interaction with negoti-

ated modification of any and all of the elements of the initial phase (the

problem re-definition aspect) so as to access new resolution pathways and real-

isation of value, and (III) a final production phase where the design outcome is

produced and normative value realised. In abstract, this is the core cognitive

process through which design problems are resolved and its 3-phase sequence,

with their distinctively different internal interactions, provides the cognitive

structure of the process. The characterisation is abstract and general, which al-

lows many differently detailed instantiations to suit individual circumstances,

e.g. between design and science and within each. This process is one among

many possible characterisations of an underlying core cognitive process and

while sketched sufficiently abstractly it has found widespread acceptance

among design theorists (cf. Zeisel, 2005, p. 20; also e.g. Cross, 2006; Lawson,

2005). Nonetheless we use it here only as illustration.

In addition to such a core process, there is a set of pragmatic factors character-

ising wicked problem situations that essentially manifest as defects in problem

specification (vagueness or incoherency) and tightness of normative

constraint, including normative conflict, and these further complicate (and

can even prevent e maximum tightness) obtaining a resolution, though

without altering the underlying core problem-solving process. Though our

analysis to follow does reduce the features of wickedness to these fundamental

elements, cognitive and pragmatic, it does not reduce the practical wickedness

of wicked problems, rather it identifies its origins. This analysis is valuable in

its own right as understanding and valuable practically because it provides a

principled framework for developing practical methodologies for resolving

each of the many different kinds of wicked problems.

Understandably, the great majority of the huge literature on wicked problems

is devoted to general methods for addressing them: integral thinking, morpho-

logical analysis, design analysis, cognitive mapping, and the like, sometimes

complemented by brief initial discussions of what makes for wickedness.4

This literature typically acknowledges at the outset that wicked problems

have cognitive as well as social, political and other aspects, but does not

attempt to extract and characterise their cognitive character.5 But doing that

is precisely what is required to address the issue of whether wickedness divides

design from science as cognitive processes. It is obvious that the problems of

design and science differ in all sorts of ways, for instance in the prevalence

of private clients, the focus on commercial and political versus epistemic

Design Studies Vol 34 No. 6 November 2013

Design & Wickedness

norms, and so on. And addressing those features as part of problem solving

certainly has an important cognitive component to it; for instance, diversity

of clients often requires negotiation among conflicting outcome expectations.

But it is not obvious, and we shall argue groundless, to infer from those differ-

ences that design and science use different core cognitive problem-solving pro-

cesses. Analogously, the sciences have obvious differences between them e

compare, say, cosmology with geology with cellular biology e but they are

all considered sciences, we do not infer that they therefore differ in their

core cognitive process. In this paper we focus our attention on extracting

the cognitive dimension from the original characterisation of wickedness set

out in the Rittel/Webber paper, as an exemplar for analyses of other character-

isations. In the upshot, this will serve as something of an original analysis of

wickedness and provide good grounds for arguing that wickedness does not

divide design from science on the basis of core cognitive process.

Meanwhile, we recognise that addressing the methodological issues underlying

any proposed tame/wicked divide will still leave other asserted differences be-

tween design and science outstanding, in particular the natural/artificial and

descriptiveefactual/prescriptiveenormative differences. We have addressed

these issues elsewhere (Farrell & Hooker 2012a, 2012b) and will similarly

address other, derivative differences.6 While considering all these is beyond

the scope of this paper, we contend that the strategic conception of science

we advocate forms the proper foundation for also dealing in a systematic

way with these further issues e and comes out in favour of our position con-

cerning the sameness of the core cognitive processes of science and design.

In what follows we delineate three conditions in the problem-solving context

whose methodological consequences, either singly or in combination, consti-

tute wickedness. (For convenience, we take specifying the condition to also

include its methodological consequences.) We then analyse Rittel and Web-

ber’s paper and classify their wickedness-making features in terms of these

three conditions. Along the way we discuss how these three aspects of wicked-

ness are also fully present in science.

1 Wickedness-making features and the distinctionbetween design and scienceIn this section we show that the ten wickedness-making features identified by

Rittel and Webber can be reduced to the methodological consequences of

three conditions of the problem situation: 1) finitude; 2) complexity; 3) norma-

tivity. These are widely used notions, but because each can have subtly vari-

able meanings we first briefly indicate our usage:

Finitude. The finitude of our cognitive capacity and our resources, is a pro-

found limitation on our abilities e individual, social, and as a species e to

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acquire knowledge of the world and to achieve our other goals in the world.

An immediate expression of our finitude is our ignorance: if we had unlimited

cognitive capacities and resources then we would not be ignorant. As it is, we

are ignorant, not just of the facts and true theories, but of methods for validly

establishing these, the concepts required to specify them and the criteria for

correctly deciding such things. Consequently, whenever a problem situation

is characterised by such deep ignorance, or when a problem situation must

be resolved but the available resources (including time) are finite and insuffi-

cient for an optimal solution, to that extent the problem at hand can be

considered to be a wicked problem. This first condition is the most important;

we contend that it is a necessary condition for wickedness.

Complexity. Every aspect of our world is characterisable as interactions between

partially nested hierarchies of complex systems having multiple feedback and

feedforward loops where multiple interactions among systems typically have

far-reaching consequences across many functional levels, such interactions

causing others in cascades that spread in unpredictable, irreversible, history-

dependent ways throughout their domains. This complex-systems nature of the

world has two general kinds of consequences relevant here. (A) It will often be

impossible to disentangle the consequences of specific actions from those of other

co-occurring interactions. (B) The outcomes of processes are difficult to predict,

amplifying our ignorance and exacerbating the limits imposed by finite resources.

Normativity. Human values and norms can become inextricably intertwined

with problem formulation and problem resolution. Notoriously, values and

norms are often in conflict both between agents and even within an agent’s

normative commitments and require sufficient resolution through compro-

mise to permit a coherent and practicable problem resolution.

Eachof these features poses a challengingaspect of the fundamentalmethodolog-

ical problem: how it is possible to act intelligently and responsibly in aworld char-

acterised by deep limits on our problem-solving capacities? We contend that it is

the depth and extent of this methodological challenge that ultimately constitutes

the wickedness of a problem. The reduction to just three conditions produces a

muchclearer concept ofwickedness that, in turn, enables a sharplydelineated crit-

ical comparison of design and science problems. To demonstrate that, we turn to

Rittel andWebber’s discussion ofwickedness and show that the ten features iden-

tified by them can be reduced to the methodological consequences of the three

conditionswehaveproposed.Along thewaywewill showhowscientificproblems

fulfil both Rittel and Webber’s ten features and our three conditions.

1.1 Rittel and Webber’s ten wickedness-making features

i. There is no definitive formulation of a wicked problem.

To illustrate what is at issue here for Rittel and Webber consider a brief to

design a commercial bank. On which among the huge number of alternative

Design Studies Vol 34 No. 6 November 2013

Design & Wickedness

design conceptions should one initially focus? Perhaps customers should be

whisked up to higher floors, excitingly in clear-sided capsules, for services

differentiated by floor. Or should the entire traditional edifice and its functions

instead be virtual, available in secure digital capsules? Perhaps a good coffee

shop or children’s play area should be incorporated? What would need to

be known to begin with each design, about human psychology and sociology,

engineering possibilities, city planning, the future of digital media, and so on?

There is a plenitude of potential design options, each of as yet unknown merit

but demanding open-ended backing investigations to assess, the whole set in a

world of finite time and resources but demanding a timely, workable design

response.

We might take the view that, while in principle all potential solutions are know-

able and mutually comparable, the complicatedness of design situations ren-

ders the best solution very difficult to discover in practice. Rittel and Webber

say about poverty, for instance, that it has many partial, context-dependent

causes and we simply don’t, and practically can’t, know enough about all,

and perhaps any, of them. In this ‘ignorance’ reading of the situation, underly-

ing our ignorance is presumably a tame or textbook problem, just one that re-

quires ideal knowledge to identify, but towards which we should try to move.

But focussing on that would be both a too-partial reading of Rittel andWebber

and, more importantly, a strategic mistake. For instance, one might try devel-

oping all options to see if the best one stands out, or try guessing which one is

the best one. But in the presence of substantial numbers of options and time

and resourcing constraints, the former is likely not possible and the latter likely

not successful. In short, such strategies are unlikely to pay off. Knowing of the

bare existence of a best bank design, even were there one, is of no practical use,

and may even be detrimental, to designers if they are ignorant of what it is. The

practical problem is how then to proceed.

The rational alternative is to cease directly pursuing the best solution and

begin a search for an accessible, at-least-satisfactory one, even if it is not the

best. And now we are already in the domain of a very different overall model

of design methodology. Here the way to proceed is to consider a few alterna-

tive proposals across a reasonable spectrum of options, each accompanied

with a proposed investigation, use of realistic resources, etc. and a rationale

as to the value and realism of the approach. There follows a cyclic process: af-

ter critical discussion, including of available knowledge and resources, there

will emerge a decision to initially pursue a very few (perhaps only one) option

to a next presentation stage and, after the results of that round are in, the

whole process can be repeated again and again, sometimes with the same op-

tions, sometimes with a suggestive new one added, until one or more at-least-

satisfactory designs emerge that also can be completed within the investigatory

knowledge and resources available. One of these can then be chosen to

execute.

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This is a process of sifting through alternative further specifications of the

problem, and of the solution, looking for ones that ‘hang together’ and offer

opportunities and rewards to investigation. In fact, it is also a concomitant

search for an appropriate methodology for the joint problemesolution speci-

fication, since methods will change somewhat with the problemesolution, so a

joint problememethodesolution specification search is involved. The cycle of

repetitions of this process is then the process of improving the performance of

the solutions in the problememethodesolution couplings, through improving

our understanding of the main triadic options and their interrelations.7 Cf.

Rittel andWebber: “[the resolution of wicked problems involves] an argumen-

tative process in the course of which an image of the problem and of the solu-

tion emerges gradually among the participants, as a product of incessant

judgement, subjected to critical argument.” (p. 162)

This is certainly a different internal cognitive structure to the design process

from that for tame problems, where the problem is already fully and optimally

specified and the relevant information is then applied ‘algorithmically’ to

deduce the optimal design. The untamed process above is in fact just what

every design discussion identifies as the distinctive overall feature of the inter-

nal design process.8 It should not surprise that pragmatics, especially finitude

constraints, can and should affect method in this way. For instance, costebe-

nefiterisk analysis is often a useful decision tool; but however useful it is else-

where, anyone who has a hungry lion charging them and continues to use that

method to decide what to do, is irrational e instead ‘quick and dirty’ avoid-

ance heuristics are rationally to be preferred as method. The negotiation to

pursue design options above is more sophisticated; nonetheless, of necessity

it also uses heuristics.9

We have attended at length to wickedness feature #i because we wanted to

make the point that it identifies a process that is (i) normal for design and

everywhere recognised as distinctive of it, and (ii) derives just from the conse-

quences of finitude (including ignorance as a manifestation of epistemic fini-

tude) for rational method. It does not require any normative disagreements

among clients or authorities, nor any specifically social context, etc., to arise.

Instead it is a purely cognitive structure that frames any problem solving under

finitude constraints.

To make the point, notice that feature #i has an exact analogue in scientific

method itself. It occurs, e.g., whenever a given data set has two or more

different potential theoretical explanations (cf. design options) but limited

investigatory resources make it impractical to fully research every possibility.

It was, e.g., initially unclear whether chronic fatigue syndrome was caused by a

bacterium or virus, a fungus or mould, in each case perhaps deeply embedded

in tissue, or was due to a psycho-somatic condition, with any of these options

difficult and resource demanding to pursue. Then, just as with design, the issue

Design Studies Vol 34 No. 6 November 2013

Design & Wickedness

becomes which few of these possibilities is currently most worth pursuing and

in which specific forms. Various options will be developed in more detail, their

resource demands and risks analysed and their merits spelled out for consider-

ation. During that process more specific versions of the initial general problem

will be developed, some of them (e.g. the psycho-somatic option) perhaps

requiring a significant reformulation of both what the problem is and what

criteria a solution would need to meet. A critical debate will develop about

these options, the upshot being that one or two of them will be selected to pur-

sue, perhaps by individual laboratories, perhaps as cooperative ventures. After

the results of that round are in, the whole process can be repeated again and

again until an at-least-satisfactory explanation emerges within the investiga-

tory resources available. Strikingly, Rittel himself provides a graph of search

pathway options that is of this same kind and clearly applies directly to science

as well as design.10

It was Popper who famously pointed out that the foundations of science are

not anchored on the rock of proven truths but instead driven down into a

swamp of possibilities just deep enough to achieve sufficient stability to

continue research (Popper, 1980). It is not surprising that the same methodo-

logical process turns up in science also since, as noted above, it derives solely

from rationality in the face of finitude. In short, the cognitive differences here

between the cognitive processes of design and science are minimal and matters

of degree, not kind.

This commonality becomes still clearer whenever scientists venture into new

unexplored territories, e.g. from Newtonian into relativistic or quantum do-

mains, or where scientists are fundamentally re-evaluating previously explored

territory, e.g. re-exploring embryology in terms of cellular bio-synthetic path-

ways rather than earlier macro-physiological characterisations, in short when-

ever scientists are engaged in deep or revolutionary research. Faced with the

initial anomalous discrete spectral data that ultimately led to quantum theory,

scientists first tried various Newtonian and quasi-Newtonian approaches to

understanding the data, even to the point of giving up energy conservation

to preserve a general Newtonian conception, before standard quantum theory

was tentatively accepted. Not only is there the exploration of accessible op-

tions, there is also the question of appropriate methods. For instance, in the

transition from Newtonian to quantum mechanics, the Newtonian measuring

methods are revealed to carry small, ineliminable residual errors and comple-

mentary Newtonian measurements become mutually excluding in quantum

theory, governed by the uncertainty relations. Thus, as with design, it is neces-

sary to search for problemesolutionemethod options. To borrow from

Simon’s (1977) treatment of this situation, scientists will order the ill-

defined, ill-structured, situation by assuming a structure and then see whether

it promises to bring about a solution to the original problem. It is clear from all

this that the problems that scientists encounter are not tame problems which

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only require assiduous and rigorous mathematical manipulation to solve;

rather, the problems they encounter are exactly as Rittel and Webber concep-

tualise the poverty problem. There is in this respect no inherent difference in

process between the realms of science and design.11

The only difference between science and design here is that in science these

problems and their resolution processes can be spread across many institutions

and individuals and slowly collectively evolve, whereas in design it is more

common for an individual or single agency to take on such investigations e

though there are also public tenders involving multiple agencies and there is

indirect common learning through adaptation of tertiary education curricula

and the like. These differences are not trivial to the overall, collective charac-

ters of design and science as cultural expressions, an issue we examine else-

where as part of considering the roles of norms in the two activities (Farrell

& Hooker, 2012b). The point here is that, whatever these differences, they

do not alter the cognitive design/research process involved.

This analysis places Rittel and Webber’s feature #i firmly under our first basic

wickedness feature, finitude. It is possible, however, that some readers e

perhaps taking off from the poverty example e would consider that the pri-

mary issue concerns normative conflict in design versus none in science. In

response we make three brief remarks: (i) even if normative diversity is

involved in the example of poverty, this reading is inappropriate because it

misses the main cognitive significance of feature #i, (ii) the interpretation still

falls under our three basic conditions, but now under normativity, and (iii) as

noted above, we treat normative issues elsewhere, where we argue that they do

not divide design and science in terms of their core cognitive process.

ii. Wicked problems have no stopping rule.

One reason for this follows immediately from feature #i: since any given solu-

tion to a design problem has emerged as an at-least-satisfactory construct

given our finite resources, it follows that if given an increase in resources,

then we can always search problememethodesolution space more effectively

for more promising options and/or improve solution designs for the options

explored. Hence it is always possible to improve a given design solution.

This is an important cognitive feature of the design process in general, and

of wicked problem solutions in particular. But it is obviously equally true of

science as of design and for exactly the same reasons. It is always possible

to improve a given scientific solution. In both cases it lies in contrast to

tame problems where solutions are arrived at in a finite number of steps and

are complete.

But Rittel and Webber offer, not one, but three reasons for feature #ii, of

which that above is essentially the first. (Their version reads: “. the process

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Design & Wickedness

of solving the problem is identical with the process of understanding its na-

ture” (p. 162). In the absence of normative diversity, this comes down to the

fact of many possible problememethodesolution options to explore. This

conception dominates their discussion.) The second reason given is “. there

are no criteria for sufficient understanding”. If this is because we are ignorant

of how to formulate them until we better understand the field then it is another

aspect of the first reason, and one emphatically shared with science. Consider

Newton’s mechanics and the perihelion of Mercury; it was not until Maxwell

had formulated electro-magnetic theory and mathematicians and experimen-

talists had sharpened the conflict between it and mechanics that it was possible

for Einstein to formulate relativity theory as primarily a kinematical, not

dynamical, shift and provide a revolutionary explanation for the perihelion

rotation. This also shows that timing may not be only due to ‘local’ pragmatic

factors but can derive from our condition of historical development e just as

in design. Alternatively, Rittel and Webber could be taken as arguing that the

absence of a stopping rule derives from irresolvably competitive norm-based

selection of problememethodesolution options and the option-dependence

of solution criteria. Thus the first two reasons for feature #ii are reducible

to either finitude or normativity.

The third reason is “because there are no ends to the causal chains that link

interacting open systems” (p. 162). This is a separate issue where a planning

decision is envisaged as being applied in one system that is in mutual interac-

tion with several other systems. For instance, a modification of the transport

system of a city, such as installing lights or a new bridge, interacts with eco-

nomic decisions about costs of public versus private transport and with social

decisions about the safety and privacy of each. Rittel and Webber have in

mind that decisions within each of the latter, including responses to transport

design decisions, equally react back on the traffic flows within the transport

system, and so on in a never-ending flow of mutual responses. In these circum-

stances, they conclude, decision consequences in any such system will lack

well-defined boundaries and so there will also be no well-defined solution

criteria for transport problems.12 For the same reason, various problems

have scopes that overlap, so that no one of them can be tackled without

affecting all the others. A changed transport design will alter business and lei-

sure activities, socio-economic stratification and segregation, and thus the na-

ture and distribution of medical demand and criminal activity, and so on.

This third reason comes down to the fact that planners, and designers, deal

with complex systems. Our ignorance of the complex causal chains in such sys-

tems makes it exceedingly difficult to arrive at optimal solutions whereby we

can judge that the problem has been solved and we can stop further investiga-

tion. But this feature is clearly shared with science. Science has been addressing

the issue of complex systems for many decades now; for example, climate sci-

entists have developed sophisticated theories and methodologies that have

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enabled an ever deepening understanding of the interacting open system which

is earth’s climate. However, we are still far from having a settled climate

science e and major discoveries are still being made, for example, the identi-

fication of the Indian Ocean Dipole little more than a decade ago e and it is

uncertain, at the moment, whether we would recognise a climate science

that was settled even if we had it. This reason leaves feature #ii firmly within

our condition two, complexity.

In conclusion, whichever of Rittel and Webber’s reasons we take for their

wickedness-making feature #ii, it reduces to one or another of our three con-

ditions, and fails to divide design from science.

iii. Solutions to wicked problems are not true-or-false, but good-or-bad.

The wording chosen here is ambiguous. One reading is that solutions to

wicked problems are not exact and complete (e.g. optimal), but sufficiently

good-or-bad approximations to exact, complete solutions. In this case the

feature would have followed from feature #i and been explicable in terms of

finitude or complexity, or both. Moreover, it applies as equally to science as

design. Indeed, an immature science, including a mature science undergoing

revolutionary change, will have exactly the character that Rittel and Webber

ascribe to social planning: there is as yet no settled basis for paradigm exper-

iments, interventions will have unexpected consequences and practically

achievable tests will often have their validity or strength re-assessed as knowl-

edge develops.13 In these circumstances scientists commonly speak of theories

and tests being more or less good-or-bad, rather than true-or-false. That is

because the tracking of errors is at the fore. It is not until a mature, settled sci-

ence emerges with an established methodology and supporting technologies

that it is usual to speak of truth and falsity, and objectivity. The differences be-

tween immature and mature disciplines and design domains are important, but

they do not distinguish between science and design. Rather, they remind us

that in both mature sciences and mature design domains there have at least

been some once-wicked problems that have been resolved and transformed

into tame problems.

However, the focus of the text strongly suggests that the intended reading

should instead be along these lines: because of normative diversity in evalua-

tion, there are no agreed evaluation criteria, so solutions to wicked problems

are not correct or incorrect as judged by agreed evaluation criteria, but vari-

ously good-or-bad according to the norms being used. Various parties will

judge any proffered solution and their judgements “are likely to differ widely

to accord with their group or personal interests, their special value-sets, and

their ideological predilections.” (p. 163) If this is right, then this feature of

wickedness clearly falls under condition three, normativity. Either way feature

#iii is explained by one or another of our conditions.

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iv. There is no immediate and no ultimate test of a solution to a wicked problem.

This may be understood as a special case of feature #ii, third reason, where the

temporally extended consequences of decisions are confined to one system.

The response there applies here as well. Or it may be read as a version of

feature #ix (below) and our response there will apply here.

v. 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.

Despite some temptation to assume so, this is not simply another version of

feature #iii, or #iv. Instead, like feature #ii, third reason, it introduces yet

another additional, and significant, methodological consideration, path-

dependence, over and above those discussed under feature #i.

In Rittel and Webber’s words: “Whenever actions are effectively irreversible

and whenever the half-lives of the consequences are long, every trial counts.”

“. 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.” (p. 163) For the kind of social

planning examples Rittel and Webber had in mind the trial-perturbed system

state will overall generally diverge increasingly over time from the unperturbed

system state.14 In the expressway case above, the changes in driving patterns

and attitudes (e.g. expectations of travel times), business planning and school

busing, etc. induced by the building of the expressway will continue to alter the

overall societal behaviour in ways that will typically drive it in a different di-

rection from that that would have been taken had the expressway not been

built.

In short, the developmental trajectory of the society, the particular sequence of

states it passes through over time, will have been altered. Developmental tra-

jectories of this sort are common, from societies and persons through to ecol-

ogies and organisms of all sorts to the earth geologically, and ultimately the

cosmos itself. For instance, as we know, a single traumatic event in childhood

can have permanent consequences for a person, setting them on a coping

course distinctive to their personality and resources that marks the whole of

their lives. Similarly for positive experiences, these often reinforce a tentative

behaviour or create an attraction to their circumstances that marks the rest of

life. In this way person’s lives are highly unique, no two people are alike. All

the kinds of systems mentioned above show strong context-sensitive or path-

dependent developmental trajectories of this sort.

The problems posed to methodology by context-sensitive developmental tra-

jectories derive primarily from their irreversibility and longevity, but also

from their uniqueness. Irreversibility combined with longevity means indeed

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that no sequence of interventions are ever made under repeatable conditions, a

problem acknowledged under feature #ii, third reason. This need not in itself

be a problem if the impacts of past interventions can be measured and dis-

counted when evaluating later interventions. However, where interactive

complexity makes it impossible to separate effects this correction technique

cannot be exact but only produce approximations and sensitivity estimates

of the importance of residual uncertainties. All these latter methods were

developed in science where the same problems recur in the study of complex

systems from geology to ecology. These considerations clearly place this wick-

edness feature under complexity.15

The uniqueness of developmental trajectories to which context-sensitive tra-

jectories typically leads then poses additional methodological challenges

because it is evidently impossible to use past empirical data to inductively

predict future developmental states since future interrelationships will, ex hy-

pothesi, be unique and it is also impossible to generalise across groups of

such trajectories. Thus it is apparently impossible to design or plan in a

way that will reliably meet an individual’s, or a group’s, needs or wants.

But again we note that science faces these same problems whenever such

complex systems are studied. In response it employs three basic kinds of stra-

tegies. The first is to exploit repeated kinds of transitions. For instance,

although there is only one earth to use to study terrestrial geology (thereby

exacerbating the problem of generalising, one might judge) it undergoes

closely related transitions many times over, such as vulcanism, erosion and

continental drift. This allows these processes to be studied in the usual scien-

tific manner. The second is to exploit the underlying models of dynamic in-

teractions that physics and chemistry provide. Thus although rusting and

fire manifest rather differently they are both oxidation processes at molecular

level. This provides a fine fabric of established dynamical processes interrelat-

ing the developmental processes. Finally, third, it is possible nowadays to

construct complex-systems models of classes of unique path-dependent devel-

opmental trajectories that reveal the structure of possibilities for these sys-

tems and, through that, empirically identify small classes of trajectories

into which a particular trajectory falls and hence approximate that trajectory.

These methods do not entirely remove the uniqueness barrier but they do

substantially moderate it, to varying degrees in various fields e substantially

enough to establish the science of terrestrial geology (ironically) but to a

lesser degree in domains where individual variation swamps averages and sys-

tems have long-term, relatively inaccessible processes running, e.g. in human

psychology.

However interesting these problems may be in themselves, the relevant point

here is that science clearly faces these problems arising out of dealing with

complex systems in the same ways as design does. In conclusion, feature #v

is explicable in terms of condition two.

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Design & Wickedness

vi. Wicked problems do not have an enumerable (or an exhaustively describ-

able) set of potential solutions, nor is there a well-described set of permissible

operations that may be incorporated into the plan.

This feature of wickedness is just the issue of finitude discussed under feature

#i: “. normally, in the pursuit of a wicked planning problem, a host of poten-

tial solutions arises; and another host is never thought up. It is then a matter of

judgement whether one should try to enlarge the available set or not. And it is,

of course, a matter of judgment which of these solutions should be pursued

and implemented.” (p. 164)16 Just so. And exactly the same in science. Only

the puzzle-solving activities (Kuhn’s description) of a mature science will

look at all like Rittel and Webber’s portrayal of scientific research, and then

only insofar as it is not disturbed by hidden errors or incompatibilities arising

externally. An immature science e that is, either a science in a new domain or

one newly emerging from a revolution in an old domain e will not have an

enumerable set of potential solutions to problems, for this is what makes a

discipline ‘immature’. Nor will there be a well-described set of permissible

methods, these too will be hotly debated in an immature science. Thus imma-

ture science will have the character that Rittel and Webber ascribe to social

planning. This feature of wickedness is wholly explainable in terms of condi-

tion one.

vii. Every wicked problem is essentially unique.

“But by ‘essentially unique’ we mean that, despite long lists of similarities be-

tween a current problem and a previous one, there always might be an addi-

tional distinguishing property that is of overriding importance.” (p. 164)

Noting that in science, including engineering, the ideal suggested by classes

of differential equations is that of obtaining parametric characterisations of

classes of dynamics, Rittel and Webber point out that this becomes less

possible as complexity mounts. And we would add, especially in systems

whose behaviour is governed by many weak interactions rather than a few

strong ones and where there are many long-period, path-dependent processes

running. “In the more complex world of social policy planning, every situation

is likely to be one-of-a-kind. If we are right about that, the direct transference

of the physical-science and engineering thought ways into social policy might

be dysfunctional, i.e. positively harmful. “Solutions” might be applied to

seemingly familiar problems which are quite incompatible with them.” (p.

165) But while all this is true, the barriers should not be over-estimated; tech-

niques of the sort used in geo-physics (see feature #v above) mean that many

component interactions can be successfully generalised, at least to some extent,

and general models of complex systems still offer general insight into charac-

teristic kinds of dynamics with their concomitant shaping possibilities, e.g.

parametric shaping of a strange attractor despite the chaotic behaviour it sup-

ports, and so on.

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These issues are another aspect associated with complex systems. The impor-

tant point for our purposes here is that, as under feature #ii, none of these con-

siderations divides science from design: these issues are equally true of

modelling (science) as of shaping (design) any complex system. For instance,

modelling science faces the problem that for systems sensitive to conditions

it is always possible that hitherto weak interactions making little difference

to overall system dynamics (and that might therefore be neglected to a good

approximation) can nonetheless, on entering a new sensitivity domain, become

a determining factor there. Making reasonable decisions as to what compro-

mises adequate modelling and design for a partially unknown system is a

part of the critical discussion process underlying pursuit of promising prob-

lememethodesolution possibilities discussed under feature #i. Thus feature

#vii is reducible to conditions one and two.

viii. Every wicked problem can be considered to be a symptom of another

problem.

Rittel and Webber contend that “Removal of that cause [of the original prob-

lem] poses another problem of which the original problem is a ‘symptom’.” (p.

165) This feature can be interpreted in two broad ways. Firstly, it could be in-

terpreted as an unending progression, a progression that can be read either

‘horizontally’ or ‘vertically’. Reading it horizontally yields a regress of means

within method: a proposed solution to the initial problem (one that removes its

cause) involves bringing about condition X, as eliminating some form of crime

may in turn require providing police; but procuring X in turn requires bringing

about Y, as policing in turn has budgeting and planning consequences, and

procuring Y in turn ., and so on. This is true, but true in some degree of

any kind of problem, not just wicked ones. And certainly equally true of sci-

entific problems. Understanding hot plasmas requires the capacities to create

and contain gases at a million degrees, and instruments to probe their inte-

riors, but these in turn.Nor, if there were any qualms over claims the regress

is endless (which a reader may well have), would these qualms be any less

appropriate to science than to design. So this feature of wicked problems

will not divide science from design (or even wicked problems from most

others). It simply expresses another aspect of the finitude of any human situ-

ation (condition one).

Reading the progression vertically yields a regress of problems that become

ever more general. Returning to the crime example, eliminating some form

of crime may in turn be seen as part of a larger problem of alleviating poverty,

and this in turn seen as part of removing adverse living conditions, and so on.

This too is certainly a feature of practical design decisions e of any design de-

cision in degree, wicked or not e and equally a feature of scientific investiga-

tions. Understanding hot plasmas requires understanding in turn how ionised

gases interact internally, but this in turn requires understanding how electrons

Design Studies Vol 34 No. 6 November 2013

Design & Wickedness

and ions interact generally, and this in turn requires understanding how forces

behave at atomic scales and high temperatures, and in turn . In both the

crime and the plasma cases there are serious qualms over the claim that the

regress is endless, since in both cases we ascend in a few steps to very general

issues, beyond which further issues look more horizontally associated than

genuinely more generalised. Setting this aside, this vertical-regress feature of

wicked problems will also not divide science from design (or even wicked prob-

lems from most others). It simply expresses another aspect of the finitude of

any human situation (condition one).

However, Rittel and Webber do raise an interesting practical consequence of

the availability of several vertically ordered degrees of generality: which degree

of generality is best for formulating a given problem? “. the higher the level of

a problem’s formulation, the broader and more general it becomes: and the

more difficult it becomes to do something about it. On the other hand, one

should not try to cure symptoms: and therefore one should try to settle the

problem on as high a level as possible.” (p. 165) This seems right and, as Rittel

and Webber note, the issue is reinforced by the general unsatisfactoriness of

either extreme methodological policy: pursuing grandiose generalities (‘ho-

lism’) is likely to be self-defeating unless and until large amounts of smaller-

scale knowledge have been accumulated, while pursuing only the smallest

detail (‘incrementalism’) is equally likely to be self-defeating because ignored

higher-order processes may render the data insufficient for problem solving.

However their claim that “There is nothing like a natural level of a wicked

problem.” (p. 165) does not follow. Only the wrongly implied arbitrariness

of these decisions could support that inference. As more becomes known

about the relevant interacting processes at various generalities and super-

systems, decisions about what to include, and why, become sharper. Of course,

these issues are exactly the same whether it is explanation (science) or shaping

(design) that is involved and the preceding wording has been chosen to illus-

trate the point. Thus they too will not divide science from design (or even

wicked problems from most others), but again express another aspect of the

finitude of any human situation and the problems posed to problem-solving

by the interaction between our finitude and the complexity of the world.

The second broad interpretation follows from the following quote: “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. The level at which a problem is

settled depends upon the self-confidence of the analyst and cannot be decided

on logical grounds.” Here ‘happen’ and ‘like’ in “explanation you happen to

like best” implies that selection of explanations is arbitrary, whereas it is

not, though it may be characterised by ignorance, for either science or design.

However, looking past this loose language, Rittel and Webber could be

arguing that how we interpret a problem, and how it relates to other problems,

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will depend upon our normative convictions. If this second interpretation is

the intended interpretation, then feature #viii reduces to the question of nor-

mativity. Thus under the first interpretation this feature is concerned with con-

ditions one and two, problem features clearly shared with science, and under

the second interpretation it reduces to condition three, which we contend also

does not separate design from science cognitively.

ix. The existence of a discrepancy representing a wicked problem can be ex-

plained in numerous ways. The choice of explanation determines the nature of

the problem’s resolution.

The wording of this feature encourages the belief that it is another version of

feature #i. However, and despite further confusing text,17 it seems clear that

Rittel andWebber’s intention is to argue that there is a looseness of connection

between evidence and explanation that can be exploited to defend any given

explanation from refutation while claiming supporting evidence for it. In sci-

ence, they assert, this does not happen, for if, under conditions C, hypothesis

H implies that evidence E occurs, and E doe not occur, then H is refuted. But

in design it is possible to argue that E did actually occur, or that intervening

processes independent of H prevented E from occurring, or that E is delayed

but will still occur, and so on. “In dealing with wicked problems, the modes of

reasoning used in the argument are much richer than those permissible in the

scientific discourse. Because of the essential uniqueness of the problem (see

Proposition 7 [feature #vii]) and lacking opportunity for rigorous experimen-

tation (see Proposition 5 [feature #v]), it is not possible to put H to a crucial

test.” (p. 166).

Is this then a dividing point between design and science? No, because the real

situation in science is exactly the opposite to that of the idealised logic machine

that Rittel and Webber here assume applies. Notoriously, it is also logically

possible in science to attempt to protect a hypothesis from refutation, and

through exactly the same kinds of responses as quoted above. Just this is the

point of the famous DuhemeQuine thesis, which holds e and for science in

general, not just wicked problems e that it is always possible to avoid refuta-

tion of a hypothesis in precisely these ways. It is for this reason that Karl

Popper, the great exponent of falsification in science, enjoined the methodo-

logical policy of testing hypotheses as severely as possible in an attempt to

falsify them e precisely because doing that was not logically guaranteed e

and considered it of the essence of taking a rational stance to enquiry. And

it was an attempt to show how a modicum of hypothesis-protection in the

face of apparently adverse evidence might nonetheless rationally sit with ulti-

mate falsification that Popper’s follower Imre Lakatos introduced the dual

model of a protected core or key hypothesis surrounded by a falsifiable belt

of auxiliary hypotheses. And this is still while trying to take a formal-logical

approach to science, rather than the more strategic conception of scientific

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Design & Wickedness

method we would advocate, where strategies of all these kinds are of the

essence, and choices of which to pursue and when at the heart of pursuing

knowledge rationally.18

This phenomenon of hypothesis-testing and possible refutation avoidance is

therefore common to both design and science. Moreover, the ultimate source

of this phenomenon derives from both the cognitive finitude of human beings

and from resource finitude. We cannot, in science, perform all possible exper-

iments, all at the same time, in order to clearly assign fault to one of the many

sub-systems potentially causing an unsuccessful experiment. Similarly, we

cannot, in design, implement all possible designs, all at the same time, in order

to see which one is optimal and which ones not.

However, as with many of their wickedness-making features, Rittel and Web-

ber’s claim can be interpreted in a different manner. Consider this quote: “.

People choose those explanations which are most plausible to them. Somewhat

but not much exaggerated, you might say that everybody picks that explana-

tion 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.” (p. 166) It is possible to read some of this passage

conservatively, e.g. it is reasonable to choose plausible and practically acces-

sible alternatives to investigate. But fitting ‘intentions’ and ‘world views’ are

clearly intended to suggest that explanations, and attempted avoidance of dis-

crepancies to these explanations, are governed by normative principles that

cannot be brought into coherent structures, and differ widely in the commu-

nity. Once again we have two interpretations of Rittel and Webber’s text:

one, based upon finitude considerations (condition one) and the other focused

on normativity (condition three), and neither ultimately discriminating be-

tween design and science.

x. The planner has no right to be wrong.

“Planners are liable for the consequences of the actions they generate; the ef-

fects can matter a great deal to those people that are touched by those ac-

tions.” (p. 167) This wickedness-making feature is the only one of Rittel and

Webber’s ten features that doesn’t neatly fit into our three conditions for wick-

edness. However, we do not think that this undermines our analysis and this

for three reasons. Firstly, the idea that a planner/designer has no right to be

wrong only reasonably stretches to mastery of current knowledge and skills:

why should we hold someone responsible for consequences of which they,

and everyone else, were ignorant? Moreover, it is exactly the same for scien-

tists. A poorly constructed research programme wastes scarce resources,

may mislead scientists who rely on its data and conclusions, and its poor per-

formance contributes to having its approach, or even the whole domain,

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evaluated as a poor research prospect, unfairly biassing future scientists. But,

again, that liability only reasonably stretches to mastery of current knowledge

and skills.

Secondly, the language employed undermines Rittel and Webber’s position,

since in feature #iii they claimed that solutions to wicked problems are not

true-or-false but good-or-bad. Now if a solution to a wicked problem is wrong,

in the sense of incorrect, then this is a blatant contradiction. However, if Rittel

and Webber intended to say that the planner has no right to ’bad’ solutions,

then the grounds for this evaluation would need to be spelled out and we

are most likely led back to our first response above.

Thirdly, it is unclear whether this feature really is wickedness-making. Un-

doubtedly there are consequences that follow from our actions, some horren-

dous e for example, Thalidomide e but this relates to the complex-systems

nature of the world, interacting with our finitely constrained actions and

knowledge, for which we should not be held responsible, as above for our first

reason. Additionally, even if planners and designers are held liable for the con-

sequences that follow from their plans/designs, it is hard to see why this makes

the problem wicked, except in that it amplifies, in the mind of the designer, the

wickedness encapsulated in the other features.

This concludes our discussion of Rittel and Webber’s ten wickedness-making

features. We have seen that nine out of the ten features are clearly reducible

to our proposed three conditions for wickedness-making; this alone contrib-

utes to a better understanding of wicked problems. Additionally, conditions

one and two are undoubtedly shared alike by both design and science and,

therefore, cannot form the basis of any argument discriminating design

from science. In this paper we have not specifically dealt with the third con-

dition, normativity. We do this in Farrell and Hooker (2012b) where we

argue that, like the other two conditions for wickedness, normativity plays

the same core cognitive roles in science as it does in design. In brief: The

original argument to the contrary hinged on claiming that design was

norm-driven while science was norm-free and thus the two were distinct

kinds of problem-solving activity. But of course this conception of science

assumes the empiricist logic-machine conception of scientific method; once

substitute the strategic conception of method and method too becomes

norm-driven (by epistemic norms). A subsequent position accepts that sci-

ence is norm-driven but claims that the norms in science are distinctively

cognitive in character whilst the values that operate in design are distinc-

tively conative or pragmatic in character, and therefore their core cognitive

processes are correspondingly different. Specifically, it is the intentional ac-

commodation of distinctively human desires and preferences in design, in

contrast to their intentional exclusion from science, that is held to distinguish

the two activities. But the differences between norms in design and science

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Design & Wickedness

are irrelevant to the issue of whether design and science share a core

cognitive process in common. It matters only that both processes take the

form of a strategic pursuit of value constrained by satisfying a collection

of norms.

2 ConclusionThere is a widely accepted argument distinguishing design from science

based upon the idea that science and design address different types of prob-

lems e wicked problems in the design case, and tame problems in the science

case. We have argued that this argument does not succeed. The wicked/tame

distinction is not an exclusive dichotomy; rather, it is a continuum upon

which all problems can be based, scientific and design alike. Along the

way we have shown what underlies Rittel and Webber’s 10 features of wick-

edness are just three conditions that generate some aspect of wickedness, to

some degree: finitude, complexity and normativity. The first two of these

concern such general conditions that they are easily seen to apply to both

design and science (and much else) leaving design and science to share the

common cognitive process to which they give rise. Upon examination the

third condition, normativity, also does not distinguish design from science,

but even if it did it would follow from our analysis here that the wickedness

or not of problems is no longer an independent argument for distinguishing

design from science.

This paper has focussed on the analysis of wickedness. What has been

learned is that design method, like scientific research method, is a product

of a common core cognitive process and management of pragmatic

complicating conditions, and that methodological procedures and skills

break into ways of progressing each of core and pragmatics and managing

their interactions. This structure can be exploited e for instance, by trans-

fer of problem-solving experience and strategies, whether across sub-

domains within design or between science and design, despite pragmatic

differences. This will, for example, facilitate (within limits) the abstraction

and transfer of engineering design theory and procedures, where well

structured characterisations of problems, procedures and pragmatics

have made possible sophisticated resolution structures, to other, more

pragmatically complicated fields.19 More generally it provides designers

a critical tool to widen their outlook and reflect on their practices and pro-

vides a common framework within which to pose and test design research

issues.

AcknowledgementsThe insightful and encouraging comments of two anonymous referees and a

journal associate editor are gratefully acknowledged as contributing to a clear,

balanced and well-focussed paper.

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Endnotes

1. In what follows all unattributed page references are to this paper.2. Cross here employs one part of the Kuhnian terminology, puzzles, without

including the all-important complementary terminology, revolutions. This isan oversight deriving from empiricist assumptions about scientific method(see below) that persists despite a revolutionary tradition in design process ex-tending from the early work of Schon (1963) (e.g. synthetic paintbrush) to

Crilly (2010).3. The logic-machine conception of scientific problem solving has long been

known by scholars of science to have multiple defects. There have been

many attempts to patch it up but without success and it is reasonably assessedas irretrievably flawed. For a summary of arguments see Hooker (2010).

4. See, e.g., Buchanan (1992), Chapman (2010), Hussain and Ritchey (2011),

Rooksby et al. (2006), Whelton and Ballard (2002) and references.5. For instance, Conklin (2005) proposes to encapsulate the original 10

wicked features in just 6 and Chapman (2010) in 5, while Love (2013)characterises wickedness in terms of functional time constraints and the

like, but all use, rather than assess, the nature of their characterisation.However, reflection will show that their content can be subsumed underour analysis here.

6. Kroes (2009), for example, additionally argues that (engineering) design i) has agreater array of constraints, specifically social constraints, than does science,and ii) is primarily characterised as employing means-end reasoning rather

than the ‘theoretical’ reasoning dominant in science. More detailed expositionof our responses to these other arguments is pursued elsewhere as part of ourtreatment of the roles of norms in the two domains see Farrell and Hooker

(2012b).7. What Farrell and Hooker (2009) called exploring the methodological ‘possibil-

ity space’ for the problem, speaking of science.8. See, e.g., discussions and references in Cross (2006), Goel (1995), Lawson

(2005), Rittel (2010, 3.3) and Zeisel (2005). Within these cyclic processes there

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is scope for the use of themes, prototypes, sketches and correlative ‘middle-stage’ design tools that have been widely noted, for exploring within and acrossoptions and for helping generate new options.

9. See Hooker (2010) for the radical consequences of finitude for method. See also

Caroline Whitbeck on the ethics of engineering design decisions, Whitbeck(1996) and www.onlineethics.org.

10. See Rittel (2010, Fig. 3.3.1, p. 191). Rittel claims that the process through

which designers traverse this net of possibilities is ‘beyond reason’ (p. 192).However, examination of his discussion reveals that he assumes that reasoningmust be expressible as rules or equivalents that will ultimately permit its course

to be specified by ‘algorithms’, that is, the same formal reason that empiricismassumes (among many). In the absence of such rules a-rational judgement for-mation simply replaces reasoning. But between the horns of his rational/a-

rational dichotomy there lies non-formal rational judgement formation (seee.g. Hooker, 2010).

11. This restores the role of Kuhnian revolutionary changes in science, to comple-ment that of puzzle solving (cf. note 2). However, rather than there being a

puzzle/revolution dichotomy, as Kuhn conceived it, we conceive of it as a con-tinuum in exactly the same manner as we do the tame/wicked distinction.

12. One suspects that Rittel and Webber assumed that in these circumstances no

solutions would ever have well- defined boundaries and solution criteria, butthis is not so. Provided that the effects and reactions across the interactingsystems damp out fast enough, or perhaps can be disentangled from others,

it will be possible to integrate their effects and then possible to provide so-lution criteria, e.g. in terms of permissible bounds on perturbation of systemparameters across all the interacting systems from integrated effects. But

Rittel and Webber would be right to claim that this is currently often notpossible.

13. For a detailed account of this in the case of ape language research see Farrelland Hooker (2009). A classic example of this is the state of chemistry before

the chemical revolution of the late 1700s. Because there was no formal notionof what might constitute an element, what might constitute a chemical reac-tion, what might constitute elemental purity, and so on, there was no

agreed-upon basis for fundamental advance in the field. Everything aboutthe nature of matter was controversial; for example, the idea that mass couldbe an indicator of elemental constitution, a lynchpin of modern chemistry, was

mired in controversy and many leading scientists argued that mass wascompletely irrelevant for understanding chemistry. Correspondingly, designersexploring design potential in an immature or radically altered setting, will nothave a mature design possibility space developed, so there wont be specific

design alternatives that can indicate clearly the strengths or limitations ofwhole classes of designs (paradigm experiments), design changes may have un-expected consequences and the specificity or power with which two design

ideas are accepted as demonstrating differences in design value for classes ofdesign will often have to be re-assessed.

14. Technically, their requirements as enunciated above are necessary but are not

sufficient for what they intend. For that, the system state perturbed by eachtrial has also to diverge sufficiently far and uniquely over time from thatwith no trial intervention, otherwise the trials will eventually ‘wash out’,

despite irreversibility and temporally extended consequences.15. They also apply to science itself in its historical development. For instance,

new observation and measurement technologies mostly grow out of new usesfor, and combinations of, old technologies that stretch their past capabilities,

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Design & Wickedness

thus generating technological developmental trajectories. For more along theselines on the dynamics of science itself see Hooker (2009, IIIc, 2010, 4.3).

16. We take issue here with the implication hiding behind this quote from Ritteland Webber: design has judgment, whereas science has . formal logic? This is

another instance of Rittel and Webber’s presumption that science is a logic ma-chine. It is true that science employs many forms of formal-logical systems.And it is true that formal systems of reasoning have as one of their aims to re-

move reliance on non-formal judgement, but in this they fail. It may take rela-tively little judgement to check that formal procedure has been followed,though not none, but to apply any such systems to the real world requires large

amounts of non-formal judgement: which aspects can be formalised?, usingwhich formalism?; which kinds of conclusions can be most valuably drawnfrom this?, and how? (a non-trivial issue if proof construction is not formally

decidable, as it mostly is not), and so on. Moreover, the aspects that can beformalised are always manifestly too poor for our cognitive needs. Finally,properly disciplined non-formal reasoning is a much richer tool and deservesto be considered the foundation of rationality. A machine can run a formal

system, but it takes real non-formal intelligence to invent that formal systemand show that it is appropriate to purpose. For further discussion seeHooker (2010).

17. Early on Rittel and Webber say: “There is no rule or procedure to determinethe “correct” explanation [of a problem phenomenon] or combination of them.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.” (p. 166) Butthen they go on to examine ways of avoiding refutation of a hypothesis. If inthe quote ‘refuting’ were replaced by ‘confirming’ the passage would fit with

belonging under feature #i.18. See, respectively, Harding (1976), Popper (1972, 1980), Lakatos (1970) and

Hooker (1995, 2009, 2010). When discussing feature #x (see below) Ritteland Webber come close to citing Popper as supporting falsification as a policy.

19. See Gul, Gu, and Williams (2008), among many; cf. the broader, less struc-tured categories of Blessing and Chakrabarti (2009) where cognitive/pragmaticstructures are not assumed. The advantage is akin to possessing fruitful species

concepts in biology that help structure and evaluate research.

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