design thinking and economics education

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,


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


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


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.


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).


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


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.


(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


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


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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