design thinking and economics education
TRANSCRIPT
Australasian Journal of Economics Education
Volume 16, Number 1, 2019, pp.1-24
DESIGN THINKING AND ECONOMICS
EDUCATION*
Peter Docherty
Business School Economics Group,
University of Technology Sydney
ABSTRACT
Design Thinking is a relatively recent decision-making framework in management
studies that combines traditional analytical thinking with what Martin (2009) calls
intuitive thinking. Analytical thinking corresponds to the approach typically taken
by economists where causal patterns between variables are identified based on
empirical regularities. Once established, these patterns can become the basis for
decision-making in matters affected by the variables in question. Intuitive thinking,
on the other hand, grounds decision-making on connections that are apprehended
instinctively with a greater role for imagination and creativity. Design thinking
combines both approaches and reflects the epistemological pragmatism of Charles
Sanders Pierce and John Dewey. This paper argues that the rise of design thinking
presents economics with some interesting possibilities and conceptual challenges.
On the one hand, it holds out the possibility of an improved theory of
entrepreneurial behaviour. It may also have implications for economic education
more generally since at its heart is a theory of knowledge and learning, and this
may well affect economic knowledge and learning at a broader level. The paper
provides a preliminary examination of design thinking and its implications for
economic education. Two broad implications are identified firstly for what we
teach and secondly for how we teach in economics programs.
Keywords: Design thinking, intuition, pragmatism, economic pedagogy.
JEL classifications: A2, B1, B2.
* Correspondence: Economics Group, UTS Business School, University of Technology
Sydney. PO Box 123 Broadway, NSW, 2007, Australia. Ph. 61 2 9514-7780; Fax 61 2
9514-7777; E-mail: [email protected]. This paper was presented at the 23rd
Australasian Teaching Economics Conference, Curtin University, 12-13 July, 2018.
Thanks to participants at the conference, Ruth French, Rod O’Donnell, Megha Sachdeva
and two anonymous referees for feedback and suggestions.
ISSN 1448-4498 © 2019 Australasian Journal of Economics Education
2 P. Docherty
1. INTRODUCTION
A recent development in business school education has been the rise of
what is now called Design Thinking. This is a multi-disciplinary
approach to decision-making and the solution of business problems that
allows greater room for the application of skills such as lateral thinking
and creativity than traditional approaches to decision-making and
problem solving have typically allowed. It draws upon methodologies
used in disciplines such as architecture that have strong creative or
design components, and it tends to stress looking at problems from
more than one perspective. A corollary of this approach has thus been
a questioning of traditional disciplinary boundaries, or at least a
questioning of the institutional expression frequently given to such
boundaries (including the existence of separate departments within the
overall business school structure). Not surprisingly, this has led to a
certain degree of resistance from academics occupying the spaces
historically defined by such boundaries.
Design thinking appears, at least on the surface, to be so different
from the way most economists conduct their research and organise their
teaching, that their natural response might well be to ignore it.
Economics appears to be located well within the confines of what
Martin (2009) calls analytical thinking, and this might be regarded by
economists as such a fundamental characteristic of the discipline that
design thinking should be treated as a separate field located within the
discipline of management studies. But an alternative approach might
recognise the possibility that design thinking could shed additional light
on aspects of business behaviour that is relevant to economics or that it
might suggest ways of integrating economics education with education
in other business disciplines that will enhance the intellectual skills of
graduates. It is, therefore, worth reflecting on the nature of design
thinking more carefully before we dismiss it as irrelevant to economics
research or the way economics graduates are prepared.
The objective of this paper is to outline the structure of design
thinking and to ask whether it could have any implications for the
content and pedagogy of economics programs. It is organised as
follows. Section 2 provides a detailed account of the central principles
of design thinking as outlined by one of its key proponents. Section 3
then unpacks the relationship between design thinking and disciplinary
methodology and epistemology which will be important for thinking
about the relationship between design thinking and economics. Sections
Design Thinking and Economics Education 3
4 and 5 then reflect on the implications of design thinking for both the
content of what we teach in economics programs, as well as the way we
teach. Section 6 draws some broad conclusions and suggests some
issues for further consideration.
2. WHAT IS DESIGN THINKING?
Two perspectives are useful in outlining the elements of design
thinking. The first involves specification of the uses to which its
advocates typically suggest it is most effectively put. The second is a
contrast between two approaches to decision-making: what business
academics in general, and economists in particular, traditionally regard
as analytical thinking; and a more intuitive practical approach to
decision-making. Design thinking combines both of these approaches.
On the first of these perspectives, advocates typically suggest that
design thinking can provide solutions to a range of business problems
and the development of strategies to enhance business performance
(Dunne & Martin 2006, p.514; Martin 2009, pp.9-10). The kind of
problem in view, for example, might be how sagging demand for a
firm’s previously successful product could be addressed. Should the
firm redesign the product? Should it revise its approach to marketing
the product or change the way it produces or distributes the product to
reduce costs, and hence offer the product at a lower price? Or should
the firm develop a new product with improved features to replace the
struggling product altogether? Coming up with solutions to problems
like this may well involve significant innovation in the form either of
developing new products or developing new production processes and
associated technology. It is not surprising, therefore, to find that this
problem-solving perspective on design thinking is often linked to
innovation-enabling performance, and that one of the motivations for
convincing business leaders to embrace it, is that successful
breakthroughs have been achieved by firms using this approach
(Euchner 2012, p.10). It also follows that teaching MBAs or business
undergraduates design thinking is good for firms because graduates
then come with the ability to use this approach when solving problems.
On the second of the perspectives outlined above, design thinking
may be understood in relation to two seemingly opposed approaches to
making decisions. The first is traditional analytical thinking (Martin
2007, pp.6-7; 2009, pp.5-6). Analytical thinking “harnesses two
familiar forms of logic – deductive reasoning and inductive reasoning
– to declare truths and certainties about the world” (Martin 2009, p.5).
4 P. Docherty
These “truths and certainties” or the “knowledge” generated by
deductive and inductive reasoning processes, become the foundation
for making rational and informed business decisions. Martin (2007, p.7)
characterises the central criterion for identifying good plans or
proposals for action according to this approach as reliability. If a plan
for action is based upon forecasts or relationships observed in historical
data, and that plan has turned out more often than not to have been
successful, that plan passes the reliability test, and this justifies acting
upon it. One way of describing decisions made in this way is in terms
of optimisation where the objective is well defined and attained with
respect to some choice variable in the presence of a set of relevant
constraints (Glen, Sucio & Baughn 2014, p.654).
Martin argues that analytical decision-making is inherently
conservative because it is based upon what is already known from the
past and in large measure, therefore, ensures that the future will look
like the past (Euchner 2012, p.11). But an alternative approach to
decision-making is to allow a greater role for intuition and creativity
(Martin 2009, p.5; Euchner 2012, p.11). Here the decision-maker looks
at the problem from multiple perspectives and asks questions about the
nature of the problem that may generate new ways of thinking about it.
Observation and data collection are important parts of this process, but
the observations made and data collected may be of a wider variety than
is usually dictated by the analytical approach. A frequently cited
example is that of statistical outliers. Outliers are frequently removed
from data sets in economics because they bias parameter estimates in
the key relationships under consideration and can be thought of as
unique events that, by definition, do not fit into such relationships very
well. Design thinking makes greater use of phenomena such as outliers
to ask questions and challenge the assumptions implicitly made about
the relationships in question. It also makes greater use of ongoing
experimentation where this is possible. Thus action may not be reserved
until the final analytical strategy has been formulated. Instead, potential
strategies may be trialled, possibly on a small scale, and progressively
modified in the light of feedback about their effectiveness.
For Martin (2007, p.7), validity rather than reliability becomes the
central criterion for identifying good plans or proposals for action when
intuition and creativity drive the decision-making process. This does
not imply that a good intuitive strategy is arbitrary. The decision-maker
will have reasons for the strategy design and proposed course of action,
Design Thinking and Economics Education 5
but the factors taken into account in establishing its validity are likely
to be wider than analytical decision-making allows, and a considerable
amount of judgment is also likely to play a role in the process (Martin
2007, p.11). Validity is in some senses then a softer decision-making
criterion than reliability (at least on the surface), and ultimately a
decision is validated ex-post within the intuitive approach if it leads to
a desirable outcome (Martin 2007, p.7).
While these two approaches may appear to be antithetical, design
thinking, as suggested above, combines both. As Roger Martin argues:
If you use analytical thinking alone, you will just extrapolate from the past,
which will work for you if you are willing to accept a future that is no different
from the past. If you use intuitive thinking alone, you won’t take advantage in
a rigorous way of the data that’s available. Both of them are needed. Analytical
thinking tends to miss new, different things that can change the environment.
And intuitive thinking tends to be just plain wrong too many times. What you
want and need is a combination of the two.
(Euchner 2012, p.10)
But it remains the case that because design thinking allows a greater
role for intuition, it is a broader decision-making process than analytical
thinking alone. Martin argues that it is an approach actually used by
leading business innovators and that it accounts for the success of
profitable and landscape-changing breakthroughs in business models
and product design.
Lego’s decision to produce the Lego Friends line provides an
example (Martin & Goldsby-Smith 2017, p.131). Lego had had very
little success selling their construction toys to girls, according to Martin
& Goldsby-Smith, with sales data indicating that boys made up the vast
proportion of the market for this product-line. Analytical decision-
making might have suggested that investment in capacity to produce a
product-line targeted at girls would have been a mistake, with no
previous attempts to do this having been successful. But Lego’s CEO
made an intuitive judgment that girls could be persuaded to use the
product if it was reimagined in the right way. A new series of Lego toys
was thus designed that more directly appealed to girls but which were
very different to the traditional product line. Lego then invested in the
capacity to produce this new line, which, according to Martin &
Goldsby-Smith, turned out to be highly profitable for the company. But
this would not have happened without the initial imagining that a new
group of customers could be attracted to the product.
6 P. Docherty
Business schools have traditionally focused on developing analytical
thinking, and under the influence of MBA and other graduates trained
in these schools, the analytical approach has become a widespread
decision-making tool in the post-World War II western businesses. But,
according to Martin, firms who rely solely on this approach are not the
most innovative or successful. Innovation rewards those who take a
more complex and adventurous approach.
3. DESIGN THINKING, EPISTEMOLOGY AND HUMAN
COGNITION
The characterisations of analytical and intuitive thinking outlined in the
previous section correspond closely to well-known epistemologies, or
theories of knowledge. This isn’t surprising given that the business
manager or entrepreneur is making decisions about courses of action
for the firm that depend upon the extent and veracity of information.
Assessing this veracity is an inherent dimension of the decision-making
process, and this is essentially an epistemological task. How does the
manager know that information about such things as the evolution of
demand for the firm’s product, the relationship between that demand
and price, or the nature of the firm’s competition is reliable or true? We
could frame this question in terms of whether the manager is justified
in believing the information upon which the particular course of action
under consideration is based, and this is precisely the language of
epistemology (see Pritchard 2014, p.23).
Analytical thinking assesses the truth of knowledge-statements using
deductive, inductive or more commonly, falsification processes (Martin
2009, p.5; Glen et al. 2014, p.654). Deduction, of course, begins with a
set of assumptions and derives conclusions that are logically entailed in
those assumptions (Chalmers 1999, pp.41-43; Pritchard 2014, p.94).
While such processes are frequently used in forming hypotheses about
the nature of the world, very little knowledge is ultimately derived using
only such processes (Chalmers 1999, pp.49-53). Another approach to
the assessment of truth-statements is induction. This approach identifies
regularities in observed cases and generalises from these particular
instances to universal principles posited to apply in all cases (Chalmers
1999, pp.43-49; Pritchard 2014, pp.95-96). This process is a supposedly
empirical approach that derives truth from observation. Unfortunately
it suffers from Hume’s famous problem of induction whereby the
principle of induction, the idea that a regularity observed with sufficient
frequency can be used to infer a universal principle, is not itself
Design Thinking and Economics Education 7
supported by any independent epistemic justification (Chalmers 1999,
pp.50-53).
A third approach is Karl Popper’s principle of falsification. This
radically revises the definition of knowledge associated with empirical
investigation so that in one sense we may end up knowing very little at
all. This is because it is always possible that counterfactual cases may
be discovered that contradict and thus disprove propositions we
currently believe. In practice, however, we regard as knowledge
propositions we have some reason to believe. But we hold this
knowledge only provisionally because of the possibility that
counterfactual cases may be observed. If such cases are observed, we
discard or reformulate this knowledge. But if repeated attempts to
falsify particular propositions have failed, we retain these propositions
within the body of provisional knowledge, and more than that, we
believe such propositions with greater confidence than those that have
survived fewer tests (Chalmers 1999, pp.59-65).
These analytical approaches to assessing whether propositions are
true, attempt to derive some external benchmark for this assessment,
the benchmark essentially being a tight correspondence between the
proposition and the observed world. This is what Martin refers to as the
reliability of knowledge in analytical approaches (Martin 2007, p.7). It
constitutes the most rigorous form of a workable empiricism that
scientists have been able to develop.
The problem is that this kind of empiricism doesn’t provide a
satisfactory account of important developments in scientific knowledge
(Chalmers 1999, pp.91-92; Euchner 2012 p.11). It is frequently the case
that falsified propositions are not rejected, and that non-falsified
propositions are rejected for reasons unrelated to the falsification
process. A second problem is that this kind of empiricism may not be
as reliable as one might think. Some important propositions which were
at one time falsified and thus rejected, have later been accepted because
some aspect of the original falsification process was shown to be
problematic (the Copernican theory of planetary motion is frequently
cited in this respect, see Chalmers 1999, pp.92-101). Together, these
problems suggest that criteria apart from those Martin classifies under
the rubric of reliability, may be useful in both generating and justifying
propositions.
One possible alternative Martin (2009) identifies is the principle of
abduction or inference to the best explanation employed in American
8 P. Docherty
pragmatism (cf. Pritchard 2014, pp.96-98). This approach does not look
for regularity and general laws in empirical data to determine the
veracity of propositions, but to whether the proposition accounts for the
observed phenomena in a satisfactory manner (Rescher 1995, p.710). It
is an approach that may be used on infrequently observed phenomena
as well as on regularly observed relationships between key variables. It
also allows for learning, in that propositions are initially held much
more tentatively than even the provisional knowledge of Popper’s
falsificationism, and these propositions may be adapted as further
experience provides new insight about associated phenomena (Rescher
1995, p.712). In this respect, abduction is well suited to circumstances
of strategic importance that arise periodically in the life of a company
when new directions are being set, new strategies developed, or
particular opportunities evaluated. It accounts, according to Martin (cf.
Euchner 2012, p.10), for breakthrough or so called “disruptive”
innovations. Knowledge developed from previous occurrences of such
situations is clearly not available, and decisions based on relevant
knowledge subsets which are derived in this way may actually be
misleading since they inherently omit the unique and determinative
aspects of the situation. In these circumstances, Martin suggests that
abduction may take the form of managerial intuition that puts the pieces
of observed information together instinctively. Propositions, and their
associated courses of action, derived from such intuition, may then be
analysed, scrutinised and modified until a final workable strategy is
developed and this strategy can be implemented. Even after
implementation, however, subsequent experience may lead to further
modification and adjustment.
Glen et al. (2014, pp.659-660) add a further behavioural dimension
to this account of abductive decision-making. They explicitly link the
epistemological development and testing of propositions encompassed
in the abductive approach to one aspect of the cognitive distinction
between so-called System 1 and System 2 modes of thinking
(Kahneman 2011, pp.20-24). In this distinction, System 1 thinking is
rapid, automatic and apparently effortless. It depends on general pattern
matching and the use of heuristics or rules of thumb to make
assessments and decisions. System 2 thinking is slow, deliberate and
focused, allocating substantial mental attention to the detail involved in
carefully assessing existing information to make a decision. Glen et al.
argue that System 1 thinking essentially corresponds to Martin’s
Design Thinking and Economics Education 9
abductive entrepreneur and that decision-making of this kind is
necessitated by the particular epistemological problems that
entrepreneurs face on a regular basis.
Martin is slightly more circumspect than this. He does indeed argue
that intuitive thinking is an important part of entrepreneurial behaviour
and that an account of decision-making that underpins successful
innovation cannot ignore this kind of thinking. Design thinking,
however, explicitly incorporates both analytical and intuitive thinking.
It does not argue that all business strategies that generate significant
innovations should be decided solely on an intuitive basis, but he does
argue that such decisions cannot be made without such intuition, and
this marks a significant departure from traditional approaches taught
within business schools and adopted by some business leaders over the
last five or six decades which over-emphasise analytical thinking to the
exclusion of intuition. For Martin, the epistemological problem of the
entrepreneur necessitates an important role for intuition, and the theory
of cognition indicates how widespread and important intuitive decision-
making processes are in day to day life.
Design thinking has not been without criticism. Vasdev (2013,
pp.101-103) considers the objection that the approach described by
Martin (2009) has been packaged by management consultants into a set
of digestible steps for sale to firm managers as the instant solution to
their lack of innovation and competitive advantage. This seems
incongruous given, he argues, the “complexity, serendipity and
uncertainty” that surrounds decision-making in the design thinking
approach (Vasdev 2013, p.101). Whether effective intuition is
something that can be taught or learned is the implicit question raised
by this criticism. Iskander (2018) raises a number of related challenges.
She suggests that design thinking is poorly defined, based on anecdote
rather than data, and amounts to little more than common sense. But her
most penetrating criticism is that design thinking is inherently
conservative since it centralises decision-making in the hands of the
“designer” who is given an extraordinary licence to determine the
appropriate course of action, and is likely to do so in a way that
disempowers others affected by the decision on view.
Most of these criticisms are, however, tangential to the central issue
of how managers actually make business decisions. It may indeed be
the case that design thinking “packages” offered by management
consultants are oversimplified recipes for a complex intuitive process.
10 P. Docherty
But that does not mean that effective business decisions do not involve
what Martin calls intuitive thinking. Nor does it imply that there are not
effective ways of helping unpractised managers to improve their ability
to step away from analytical decision processes and allow greater space
for intuition.
Iskander’s identification of the poor definition of design thinking, its
foundation on anecdote and its common sense nature are also
unconvincing. The description of design thinking offered by Martin
(2009) is quite well defined, and is coherently cast in terms of analytical
and intuitive elements with links to both behavioural psychology and
epistemology. That it has been identified via cases (uncharitably
relabelled as “anecdotes”) is irrelevant although this may open the way
for more extensive research to explore how widespread it is as a
decision-making phenomenon (something discussed further below). It
is also debateable whether design thinking can be called “common
sense”. Martin (2009) makes a good case that management “common
sense” for several decades has involved a strict adherence to the
analytical approach which expressly eschews any role for intuition. The
sheer number of MBAs trained in this approach suggests that this is
what passes for common sense among the management profession.
Identification of a complement to this approach would appear to be less
common although this issue could be settled by the research agenda
suggested above.
Iskander’s central criticism that design thinking is inherently
conservative appears to be levelled at its application to problems of
public policy. Martin’s chief identification of design thinking, however,
is as an approach to business decisions where decision-making
authority is already vested in an identified manager. The question is
how this agent makes decisions. This is a different question to who
ought to making decisions about the use of public resources or solutions
to public problems. But even here, one might be able to distinguish
between cases where decisions are made with a broad democratic base
but where only Martin’s analytical processes are employed, and those
in which other influences are permitted. This is, however, unrelated to
the issue of how business managers actually make the best decisions.
As discussed above, Martin makes a convincing case that the analytical
approach, based only on historical data, is the more conservative
approach compared to one which admits newly imagined possibilities.
Design Thinking and Economics Education 11
None of the criticisms considered above, therefore, appear to
seriously undermine the basic idea of design thinking as a decision-
making approach worthy of further consideration. The question we now
ask is: what implications does this approach have for economics and
economics education?
4. DESIGN THINKING AND WHAT WE TEACH
Design thinking has at least four implications for the content of
economic theory and thus for what we teach in economics programs. It
firstly challenges the theory of entrepreneurial behaviour and decision-
making, and suggests some shortcomings in how economists have
traditionally thought about this behaviour. This theory might thus need
significant revisions in the light of insights from design thinking and
this will affect what we teach our students in this field. The second
implication is that it would be useful to introduce explicit treatment of
methodology into economics programs since how we derive
knowledge, including economic knowledge, is central to the
contribution of design thinking. Students should have an awareness of
how the discipline decides what counts as economic knowledge to fully
appreciate the distinction between intuitive and analytical thinking.
Even without the influence of design thinking, however, economics
students ought to possess an understanding of the processes used within
the discipline to generate knowledge. The third implication of design
thinking for the content of what we teach is that our programs need to
be more pluralist because design thinking highlights the value of
looking at phenomena from more than one perspective. The fourth
implication we identify is indirect and arises from the possibility of
applying design thinking to the conduct of economic research itself.
Since design thinking is an approach to epistemology, it may be that
generating and assessing economic propositions using abductive as well
as analytical techniques will generate new insights into the functioning
of economic systems. Hence what we teach our students will change
because we discover new things when design thinking is used. Each of
these implications is considered in turn.
(a) The Theory of Entrepreneurial Behaviour
On the first of these implications, Martin’s observation that design
thinking characterises the approach taken by successful innovators
suggests that there may be something missing from the traditional
theory of investment spending. This is an interesting perspective given
12 P. Docherty
the state of our knowledge about this dimension of economic system
behaviour.
The traditional theory of investment may be understood in terms of
the following familiar equation:
𝑃𝐾 = ∑𝑃𝑌,𝑡
𝑒 ∙𝑌𝑡𝑒−𝑊𝑡
𝑒∙𝐿𝑡𝑒−𝑃𝑖,𝑡
𝑒 ∙𝑄𝑖,𝑡𝑒
(1+𝑟𝐾)𝑡𝑛𝑡=1 (1)
This equation determines the marginal efficiency of capital, rK, on a
particular type of capital good as the discount rate which equates the
supply price or replacement cost of that type of capital good, PK, with
the net present value of what Keynes (1936, p.135) called the
“prospective yield” of the capital good. This yield is made up of a series
of net revenue flows in each period of the capital good’s life determined
by subtracting running costs from the expected sale of output produced
by the capital good. Running costs are made up of the wage bill (the
money wage, 𝑊𝑡𝑒, multiplied by the quantity of labour employed, 𝐿𝑡
𝑒)
plus the total cost of other productive inputs such as raw materials
(where 𝑃𝑖,𝑡𝑒 represents the price of input i in period t and 𝑄𝑖,𝑡
𝑒 represents
the quantity of input i used in period t). Gross revenue is simply the
product of output price in period t, 𝑃𝑌,𝑡𝑒 , and the volume of production
in that period, 𝑌𝑡𝑒. These net revenue flows are shown in the numerator
of the term after the summation sign on the right hand side of equation
(1). Because all of the variables that make up the prospective yield lie
in the future, their values are uncertain and must be forecast ahead of
time. The “e” superscript for each variable thus indicates that these are
expected or forecasted values. Discounting the prospective yield in
order to equate its present value with the replacement cost of the capital
good thus provides the rate of return on this good or the marginal
efficiency of capital, rK.
If the marginal efficiency of capital is greater than the cost of funds
that can be borrowed from the financial system, this benchmark model
indicates that the capital good in question should be purchased (cf.
Chirinko 1993, p.1878). The flow of investment spending is thus a
negative function of the cost of funds or rate of interest, and a positive
function of expected net income flows. Adjustments to this benchmark
model can be made for lags in the delivery of capital goods, the cost of
installing these goods and the payment of such things as tax credits (see
Chirinko 1993, pp.1879-81).
Design Thinking and Economics Education 13
A negative relation between the rate of interest and investment flows
requires the specification of investment flows as changes in the stock
of aggregate capital, and this in turn requires the specification of that
aggregate stock at different points in time. Given that the actual stock
of capital at any point in time is made up of a heterogeneous range of
capital good types, aggregation processes typically value these
heterogeneous types at market or replacement prices. The infamous
capital debates of the 1960s, however, demonstrated that the prices
used to aggregate heterogeneous capital goods themselves depend upon
distributive variables including the marginal efficiency of capital (see
Harcourt 1969). No single quantity of capital can thus be specified in
advance of the determination of the marginal efficiency, raising doubts
about the existence of a monotonically downward sloping relation
between the rate of interest and the flow of investment spending
proposed by the neoclassical theory (see Garegnani 1983, pp.39-41).
One possible alternative to the neoclassical theory that might be
considered in the light of these theoretical problems with cost of capital
effects is the well-known accelerator theory which relates investment
spending to lagged changes in output (Pasinetti 1974, pp.96-100). This
approach reflects the idea that capital is required for production, and the
greater the expected level of this production in the future, the more
capital will be needed to undertake it in a timely manner. Future
production levels must, therefore, be forecast, and this can be done by
looking at changes in production levels in the recent past.
An additional factor that may affect investment spending is the
availability of finance, especially if capital markets are characterised by
asymmetric information (see Hubbard 1998). In the presence of
informational asymmetries, the costs of lending may be substantially
higher than indicated by typical risk-adjusted interest rates. Lenders
may respond to the existence of these costs by rationing credit to
borrowers who cannot post collateral that aligns their incentives with
those of the lender. Credit-rationed borrowers are, however, able to
undertake more investment when internal cash flows increase or when
the value of assets that can be used as collateral for external loans,
increases, and rationing is, therefore, relaxed. Cash flows and asset
values both tend to increase during economic upswings and to fall in
downswings, so that these so-called “financial acceleration”
mechanisms are inherently pro-cyclical (Hubbard 1998, p,198).
14 P. Docherty
Empirical investigation of investment spending in advanced
capitalist economies, however, casts doubt upon more than one of these
traditional investment models (Chirinko 1993, p.1875, 1906-7). Cost of
capital effects on investment spending appear to be particularly weak,
adding empirical support to the theoretical questions about them raised
by the capital debates, while lagged output effects appear to exert the
strongest observed influence but nevertheless fail to account for a very
large proportion of observed variations in investment spending
(Chirinko 1993, p.1881). The size of financial acceleration effects is
still relatively uncertain (Hubbard 1998, p.220). The unexplained
component of investment in most of these models is thus quantitatively
important, indicating that there is still a great deal that needs to be
understood about how entrepreneurs make investment decisions.
It is at this point that Martin’s observations about entrepreneurial
decision-making have the potential to be very useful. The key variables
in equation (1) and those implied by the accelerator theory of
investment, relate, as outlined above, to the future. It is future values of
income, wages, and the prices of inputs and outputs that determine the
profitability of investment projects and hence their attractiveness to
entrepreneurs. But the future values of such variables are highly
uncertain, and so a great deal depends on how entrepreneurs deal with
this uncertainty when we try to explain investment spending. Analytical
approaches to modelling investment decisions assume that
entrepreneurs forecast the future value of these variables from data on
past values, and that they plug these forecasts into equation (1) or some
variant of it, or into an acceleration equation, and base their investment
decisions on the resulting estimate of the marginal efficiency of capital
or expected rate of income growth. Uncertainty might be explicitly
incorporated into this analysis by attaching probabilities to the possible
values of key variables and using the expected value of these variables
in the calculation of prospective yield or changes in income. A more
recent treatment of investment attempts to handle uncertainty by
placing a value on the possibility of waiting rather than investing
immediately. Dixit (1992) shows that under conditions of increased
uncertainty, defined in terms of the variance of the prospective yield in
equation (1), there is value to the entrepreneur in holding back from
investment and seeing whether expected future cash flows increase or
decrease as the economic business cycle evolves. The result is that
investment spending becomes a less smooth function of the variables in
Design Thinking and Economics Education 15
equation (1), and this may explain a larger proportion of the variation
in observed investment behaviour.
An alternative approach was suggested by Keynes. According to
Keynes, the ‘outstanding fact’ about forecasts of the prospective yield
generated by capital assets is the ‘extreme precariousness’ of the
knowledge on which they are based (Keynes, 1936, p. 149), and this
underscores the fundamental nature of the uncertainty that defines the
environment within which entrepreneurial decisions must be made.1
Keynes (1936, p. 152) admits that entrepreneurs frequently respond to
the uncertain nature of this knowledge by adopting a convention, for
example, that the current state of affairs may be expected to continue
until such time as new information indicates otherwise. This suggests a
clear role for analytical calculation along the lines outlined above in the
investment decision process. But Keynes observes that entrepreneurs
may also be of ‘sanguine temperament and constructive impulse’
(Keynes, 1936, p. 150) and that they supplement rational estimates of
the prospective yield with ‘a spontaneous urge to action rather than
inaction’ (Keynes, 1936, p. 161). He characterises such ‘urges to action’
as ‘animal spirits’, and animal spirits enable entrepreneurs to respond
to uncertainty in part by forming judgments, based on a long-term
perspective, about the likely profitability, or more generally the overall
desirability, of projects with which investment spending is associated.
That is, for Keynes, entrepreneurs respond to uncertainty partly by
undertaking rational calculation, but partly by forming judgments that
underpin their decisions. The Lego case described earlier is a good
example of such decision-making, and it bears a remarkable
1 O’Donnell (2013, pp.125-127) divides Keynes’ treatment of this uncertainty in the
Treatise on Probability into three categories: probabilistic uncertainty where
probabilities that consequence a will be generated from conditions h, are known and can
be assigned numeric values; probabilistic uncertainty where probabilities that
consequence a will be generated from conditions h, are known but cannot be assigned
numeric values; non-probabilistic uncertainty where probabilities that consequence a
will be generated from conditions h, are not and cannot be known. This latter type of
uncertainty is also called irreducible uncertainty. O’Donnell argues that Keynes carried
this treatment of uncertainty over from the Treatise on Probability into The General
Theory so that the “extreme precariousness” of the information on which forecasts of the
prospective yield of capital assets are based, corresponds to irreducible uncertainty.
Davidson (1978, p.142) divides Keynes’ treatment of uncertainty into only two
categories: risk where probabilities can be assigned; and fundamental or irreducible
uncertainty where probabilities cannot be assigned. He also allocates the prospective
yield of capital assets in Keynes’ General Theory analysis to the category of irreducible
uncertainty.
16 P. Docherty
resemblance to Martin’s version of entrepreneurial design thinking that
encompasses analytical and intuitive processes.
It must again be stressed, however, that this kind of intuitive decision-
making need not be arbitrary. According to Kahneman (2011, pp.11-
12; 236-237), intuitive decision-making processes involve a degree of
pattern recognition, but the recognition process may be more complex
than the investment modelling strategies outlined above are able to
accommodate. Studying such decision-making processes may,
therefore, require new skills and perspectives including training in non-
falsificationist epistemologies, deeper reflections on the nature of
uncertainty, and a knowledge of System 1 and System 2 cognitive
processes.2
Economists may also have to explicitly acknowledge a new, inter-
disciplinary dimension to understanding entrepreneurial decision-
making. The theories of investment spending outlined above take an
essentially one-dimensional approach to thinking about investment.
This focuses on the pace and timing of augmentation to the capital stock
within a given competitive environment and production technology.
But some aspects of investment spending may be the by-products of
other business decisions that include: the development of a broader
competitive strategy for the firm; whether to expand or contract the
product range; the choice of production technique, whether to change
this technique or whether to adopt a new technology; or even whether
to invest in the development of new production or service delivery
technologies. While some work has been done on aspects of these
problems (see, for example, Aghion & Howitt 1992 on endogenously
determined research and development spending) this has not
fundamentally altered the nature of thinking about investment, and
these are questions about which the disciplines of management and
finance are likely to have something useful to say. This implies that the
nature of investment spending might be inherently inter-disciplinary.
The validity of design thinking would thus imply that the nature of
investment spending might need to be rethought, and as suggested
above, this would be the first, direct implication of design thinking for
the content of what we teach. Research based on interviews with
managers about investment decisions, cross-checked with the
predictions of quantitative models would thus be extremely valuable.
2 See Considine & Duffy (2016, p.316) who explicitly link ‘animal spirits’ in Keynes’
treatment of entrepreneurial decision-making to Kahneman’s System 1 cognition.
Design Thinking and Economics Education 17
(b) Introducing Methodology into Economics Courses
Any recognition that abductive processes of proposition assessment
could be admitted into methods of economic knowledge formulation
may necessitate courses in economic methodology that enable students
to understand such processes. How abduction differs from deduction,
induction and falsification, the nature of statistical regularity, and
examples of these things could all be explored in such a course. Such
courses would have the added benefit of enhancing the ability of
students at a more general level to understand and critique economic
argument, to structure better economic arguments and to design
empirical research.
(c) Fostering Pluralism in Course Offerings
Since design thinking is about looking at familiar problems from more
than one perspective, students’ skills in this area would be enhanced by
the opportunity to look at common economic phenomena from multiple
perspectives. Offering courses from a range of economic perspectives
or schools of thought would thus provide students with the opportunity
to develop this skill. Courses in heterodox economics, Post Keynesian,
feminist, ecological, behavioural and Austrian perspectives, among
others, could all contribute in this respect (See O’Donnell 2010, pp.265-
267).
(d) New Economic Knowledge from New Epistemological Standards
A fourth, indirect implication would arise from the adoption of design
thinking methodology in the conduct of economic research itself.
Economics has traditionally modelled the processes it examines in
rigorously mathematical and statistical ways that fit Martin’s
description of analytical thinking. If, however, abduction constitutes a
reasonable principle for generating and assessing propositions or truth-
statements under certain circumstances, there is no reason why such an
epistemological method might not be used to undertake economic
research on a range of issues. This might be especially true for low
frequency but high impact economic phenomena such as financial
crises or severe downturns where the application of quantitative
methods is problematic. Careful consideration of outliers, qualitative
research methods such as case studies, surveys and interviews, and
more general evaluations of propositional validity might usefully be
added to the traditional suite of quantitative methods used in economic
research.
18 P. Docherty
But the application of such methods is likely to alter the structure of
economic knowledge by admitting or expelling propositions that
analytical methods have treated differently. There is, of course, no way
of telling in advance which propositions would fall into either of these
categories, but it is difficult to believe that a modification of economic
methodology in the direction of design thinking would not have any
impact on the content of economy theory and thus on what we teach our
students.
Design thinking thus has important implications for what we teach in
economics programs.
5. DESIGN THINKING AND HOW WE TEACH
Acceptance that design thinking has any epistemological legitimacy
also has implications for the kinds of pedagogy we employ in the
teaching of economics students. Glen et al. (2014, pp.660-661) observe
that teaching design thinking within the business school context is best
done not simply by explaining it to students within the traditional
lecture format but by providing opportunities for students to experience
and trial it as part of the learning process. Given that epistemological
pragmatism, which underpins design thinking, generates and assesses
truth-statements by examining their practical workability and has a role
for experimentation, adaptive learning and truth-statement
modification, design thinking pedagogies should incorporate these
activities. This implies opportunities for students to engage with
alternative explanations, simulations, team-based projects, real problem
cases and the use of feedback from previous work as key learning
strategies.
Examining real cases presents students with the challenge of
considering a policy problem or investment decision, having to identify
available information, and having to compare this with the information
requirements for a good decision about the policy response or the
investment strategy. Any discrepancy between available information
and information requirements then presents students with the need to
choose between System 1 and System 2 approaches to closing this
discrepancy, and deciding how the formulation of relevant expectations
might be different under these two approaches. Once this is done, they
can formulate an initial decision or strategy, review that decision or
strategy, and decide whether further modifications are needed.
Choosing the structure of a final decision or strategy then allows further
comparisons between the students’ own approaches, decisions taken in
Design Thinking and Economics Education 19
the real case, and outcomes in the real case, so that students can explore
how effective their decision-making approaches were, in the light of
these comparisons. Simulations provide students with similar
opportunities for feedback as well as the opportunity to revise plans and
alter strategies to see whether improved outcomes can be achieved.
Working in teams allows students access to a wider set of intuitive
inputs and to test the validity of their own intuitions which are both
features of the way good decisions should be made in real corporations
according to Martin. Welsh & Dehler (2012) outline the structure of a
design thinking course in management that takes what they call a
“studio approach” to team-based learning. Within this approach,
student teams are set business problems that need to be solved drawing
upon course readings, TED talks and an on-going process of shared
critical reflection and in-class presentations that indicate how the
problem can be addressed or reframed to more effectively encapsulate
the client’s fundamental objectives. Wang & Wang (2011) outline an
alternative process for structuring team-based examination of cases that
emphasises early specification of a detailed management plan that then
passes through a series of experimental iterations. Instructor facilitated
“knowledge-sharing” meetings built around the generation of
consensus, progressively refine and reshape the plan until a final
version is developed. Seidel & Fixson (2013) outline a team-based
approach with more brainstorming, debating and experimentation early
in the strategy development process and less of these activities in the
later implementation and finalisation stages of the case study.
This range of pedagogical tools are precisely those that educators,
both within and outside economics, have been advocating for some time
as more effective approaches to teaching than traditional lecturing (see,
for example, Ramsden 1992; pp.165-180; Becker 2000; Salemi &
Walstad 2010; O’Donnell 2010, 2014). This is, however, no accident.
The argument above is that pedagogical practices such as
experimentation, case study use and team-based learning, which
actively engage the student, are given epistemological support from
philosophical pragmatism upon which design thinking is founded. It is
because pragmatism assesses truth-propositions by their practical
workability in a process built around experimentation, adaptive
learning and proposition–modification that it makes sense for these
features to characterise the learning practices that pragmatism suggests
should be employed in the classroom. But early justification for these
20 P. Docherty
educational practices was provided by John Dewey who explicitly
linked them to his own pragmatist philosophy which emphasised active
experimentation in the assessment of truth-claims (Hanson 1995, p.198;
cf. Rescher 1995, pp.709-710). Beckman & Barry (2007, p.28-29)
interpret entrepreneurial innovation as a learning process precisely
within the context of Dewey’s educational perspective. A coherence
thus exists between these pedagogical approaches and the fundamental
logic of design thinking.
It should also be noted that increased attention to the development
of design thinking does not imply the abandonment of traditional
analytical skills. Such skills have an important role to play in design
thinking which draws upon both intuitive and analytical approaches.
But analytical skills are firmly entrenched in the economist’s mindset
and the development of intuitive skills to complement them is likely to
require significant mental effort as habitual modes of thinking are
challenged and modified. Design thinking thus has implications for the
approaches we take to teaching as well as for the content of what we
teach our students.
6. CONCLUSION
This paper has reflected on the rise of design thinking in business
schools and its implications for economic education. It has suggested
that the combination of what Martin (2009) calls intuitive thinking
along with analytical thinking that make up the design thinking
approach has the potential to provide insight into some key issues in
economics. Martin’s identification of the more complex nature of
decisions than is traditionally portrayed in models of investment
spending suggests that these decisions are made in the context of an
interplay between strategic entrepreneurial decisions for the firm,
decisions about the choice of production and delivery techniques, the
adoption of externally generated technological innovations, and
decisions about investment in the development of new technologies
within the firm. In addition, Martin observes that many investment
decisions made within this context are not based simply on traditional
analytical techniques that forecast the values of variables such as
demand for the firm’s products, associated costs and cash flows, and
interest rates based on past quantitative relationships. These decisions,
he argues, are based on informed intuition and judgement as well as on
such quantitative information, and this approach is reasonable in the
context of uncertainty. Such an approach also corresponds to
Design Thinking and Economics Education 21
epistemological pragmatism advocated by Charles Sanders Pierce and
John Dewey in the early 1900s.
It has been argued, therefore, that economic programs need to
reintroduce the study of methodology because the formation of
expectations about the future value of economic variables that
uncertainty necessitates, is the kind of epistemological problem
examined in methodological studies. This is the first implication of
design thinking for economics education. A second implication is that
expanded epistemic standards that supplement traditional empirical
tests for what counts as economic knowledge with abductive reasoning,
could generate new insights into the workings of economic systems,
and this is likely to change the content of what we teach in unpredictable
ways. A further implication is that because design thinking involves
looking at problems from multiple perspectives, it justifies a pluralist
approach to economics education.
Design thinking also has implications for how we teach economics.
Because epistemological pragmatism assesses truth-propositions by
their practical workability in a process built around experimentation,
adaptive learning and proposition–modification, it makes sense for
these features to characterise the learning practices employed in the
classroom. These are precisely the pedagogical tools that educators,
both within and outside economics, have been advocating for some time
as more effective approaches than traditional lecturing.
It is one thing to identify the implications of design thinking for
economics education but quite another to advocate changes based on
this approach. Such an advocacy ultimately depends on the validity or
at least the acceptance of epistemological pragmatism particularly in
research activities. In assessing this validity, it is worth noting that there
is already substantial evidence that the traditional empirical
methodologies of inductivism and falsificationism used in economics
are not only conceptually problematic but they are not actually used in
practice to assess the truth-value of propositions. Pragmatism
constitutes one epistemological framework that might be considered as
an alternative. But there are others, of course, and part of the process of
evaluating design thinking might involve a wider consideration of such
philosophical positions. But this simply reinforces the case for renewed
attention to methodological studies in mainstream economics programs
so that economists are equipped to consider these issues.
22 P. Docherty
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