SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
Between “Knowledge” and “the Economy”:
Notes on the Scientific Study of Designs
Carliss Y. Baldwin*
Kim B. Clark
Harvard Business School
Our special thanks to Christoph Hienerth, Peter Murmann, David Sharman,
Marcin Strojwas, Kevin Sullivan, Eric von Hippel, Tony Wasserman, Daniel Whitney,
and Jason Woodard for commenting on earlier drafts of this paper. Thanks also go to
Sushil Bajracharya, Cristina Videira Lopes, John Rusnak, Alan MacCormack, Joachim
Henkel, Michael Jacobides, Nitin Joglekar, Gregor Kiczales, Karim Lakhani, Sonali Shah,
Mary Shaw, and Edwin Steinmuller for sharing key data and insights. Finally, we would
like to thank participants in the NSF Science of Design Workshop, the MITUniversity of
Munich Innovation Workshop, and the Conference on Advancing Knowledge and the
Knowledge Economy for conversations that contributed to this paper in significant
ways. We alone are responsible for errors, oversights, and faulty reasoning.
A previous, shorter version of this paper was entitled “Designs and Design
Architecture: The Missing Link between ‘Knowledge’ and the ‘Economy.’ ”
*Direct correspondence to Carliss Y. Baldwin, Harvard Business School,
cbaldwin@hbs.edu
Copyright © Carliss Y. Baldwin and Kim B. Clark, 2005
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SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
Abstract
Designs are the instructions based on knowledge that turn resources into things
that people use and value. All goods and services have designs, and a new design lies
behind every innovation. Clearly then designs are an important source of economic
value, consumer welfare and competitive advantage for individuals, companies and
countries. But despite their pervasive influence, designs as drivers of innovation and
wealth creation are not much discussed by social scientists, senior managers, or policymakers. More often than not, to nonspecialists designs appear to be esoteric objects,
which can only be understood by experts in the design’s particular domain. We believe it
is time to integrate the study of designs across disciplines and make them the focus of unified
scientific research in their own right. The structure and value of designs as well as what
designs “need” in the way of organizations and social policies are all topics that can be
investigated scientifically and in a unified way across disciplines. These topics belong
on the agenda of research that seeks to understand how knowledge creates wealth in
modern economies. Such unified research may allow engineers to construct more
valuable designs and design architectures. It can also help senior managers organize
their enterprises more productively; assist investors in allocating resources; and inform
public debate and serve as the basis of rational public policy.
Key words: design — design architecture — science of design — design
structure matrix —modularity — option value — institutions — innovation
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SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
Introduction
Designs are the instructions based on knowledge that turn resources into things
that people use and value. Behind every innovation lies a new design. Tangible products
and production processes, intangible services and experiences, corporate strategies,
organizations, methods of contracting, governance, and dispute resolution—all of these
1
things have designs. Thus, “knowledge economies,” which are based on continuous
innovation and competition between old and new things, must produce a never-ending
stream of new designs.
2
Designs are created through purposeful human effort. A design process is a set of
activities that starts with someone’s problem and then devises an artifact to solve the
problem. The outcome of this process is the design of a particular thing that is a solution
3
to the problem. The solution may be tangible (a good) or intangible (a process or a
service) or a combination of the two.
Conceptually, designs can be thought of as lying between “knowledge” and “the
economy,” as depicted in Figure 1. At any point in time, knowledge about the world
exists in the heads of various people, in libraries, and in social and organizational
networks. Of itself, though, as historian of technology Joel Mokyr has argued, such
“propositional knowledge” doesn’t do anything. To affect the world, propositional
knowledge must be converted into “prescriptive knowledge,” that is, “designs and
4
instructions ... like a piece of software or a recipe.” Thus, it is only through the agency of
designs that knowledge can become the basis of real goods and services.
1
Simon (1981), p. 129.
Baumol (2002).
3
Simon op cit., pp. 68; Alexander (1964), pp. 5570.
4
Mokyr (2002), pp. 4–21.
2
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SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
Figure 1
Designs Link Knowledge to the Economy
Knowledge
Design
Architecture Completion
Economy
Furthermore, to create complex goods and services, the process of converting
propositional knowledge to prescriptive knowledge must itself be organized. Thus,
designs fall into two categories: design architectures, which are used to organize design
processes, and complete designs, which are the end result of such processes.
Design architectures are the starting point, hence the “forward-looking” or
“future-oriented” aspect of design processes. A design architecture creates a sensible
subdivision of the tasks involved in designing a large system. The architect sets up the
design rules for the system: He or she divides a to-be-designed system into parts, sets up
interfaces between those parts, and specifies ways of verifying the properties and testing
5
the performance of the components and the system. Just as physical architectures both
create and constrain opportunities for movement in physical spaces, design architectures
both create and constrain opportunities in the so-called “design spaces” wherein the
search for new designs takes place.
6
5
Baldwin and Clark (2000), pp. 76–77. Note that it is possible for the design of a complex system
to be created without the agency of a design architect. In that case, the system itself will have an
architecture in the sense of “an abstract description of the entities of a system and the
relationships between those entities” (ESD Architecture Committee 2004). However, the system
architecture will be undesigned or (in the language of complexity theory) “emergent.”
6
A “design space” consists of all possible variants of the design of an artifact. A complete design
is a point within a design space. “Value” is a mapping of a mathematical function onto a design
space; the process of design can be thought of as a search through a design space for high points
in a “value landscape” (Simon 1981, pp. 136–144; Baldwin and Clark 2000, pp. 24–28, 232–234). In
computer science, the concept of a design space was pioneered by Gordon Bell and Allen Newell
(1971) and has been used extensively in the fields of automated design and artificial intelligence.
The concept also appears in many fields of engineering. For example, in software engineering,
one early example of the explicit use of design spaces was Garlan and Notkin (1991). The concept
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SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
Complete designs are the end result of design processes. A complete design is the
“information shadow” of an artifact. It can be made into something real and valuable: a
product or a service. The economy in turn is based on the production and consumption
of products and services. Long ago, most goods were produced without first creating a
separate design. Today, much of the economy is devoted to the creation of designs and
the subsequent production of artifacts based on those designs.
Once they are created, design architectures and complete designs can be added
to the stock of knowledge, as the backward arrows in Figure 1 show. They can be used
again and again. Preexisting designs also serve as the starting point for new design
processes. Each generation of designs builds on the previous one, so that a series of
design processes can result in cumulative design improvement or burgeoning design
7
variety.
Clearly, designs are an important source of economic value, consumer welfare,
and competitive advantage for individuals, companies, and countries. They have also
been the focus of scientific research in a number of fields, including engineering,
8
computer science, architecture, and management. But despite their pervasive influence
and the large amount of academic research that has been done, designs as drivers of
innovation and wealth creation are not much discussed by social scientists, senior
managers, or policy-makers. More often than not, to nonspecialists designs appear to be
esoteric objects, which can only be understood and evaluated by experts in the design’s
particular domain.
We believe it is time to integrate the study of designs across disciplines and make
was generalized by Thomas Lane under the supervision of Mary Shaw and David Garlan (Shaw
and Garlan 1996, pp. 97–113) and recently has been formalized by Cai and Sullivan (2005).
Analogous concepts of search spaces and (fitness) landscapes arise in evolutionary biology and
complexity theory.
7
Improvement (adaptation) and variety (radiation into niches) are two aspects of design
evolution.
8
The community of scholar researching “design theory and methods” is estimated to be roughly
5
SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
them the focus of unified scientific research in their own right. The structure and value
of designs, as well as what designs “need” in the way of organizations and social
policies, are all topics that can be investigated scientifically and in a unified way across
disciplines. These topics belong on the agenda of research that seeks to understand how
knowledge creates wealth in modern economies. Such research in turn may allow
engineers to construct more valuable designs and design architectures. It can also help
senior managers organize their enterprises more productively, assist investors in
allocating resources, inform public debate, and serve as the basis of rational public
policy.
How does the scientific study of designs differ from other ways of studying
innovation and technology? Many scholars are already seeking to explain the dynamics
of technological change and innovation, drawing on economics, organizational behavior,
sociology, strategy, and other academic disciplines. What does the scientific study of
designs offer that is new? How can it improve on the excellent work already being
done?
In essence, the scientific study of designs as a general phenomenon offers a new
level at which to observe technologies and how they change. Social scientists especially
have struggled for some time with the problem of how to characterize “technologies”
and measure them in meaningful ways. But they have tended to approach “technology”
9
at quite a high level of abstraction. We and others who study designs scientifically think
that there is critical, observable structure below the level of an abstract “technology” and
indeed often within a single design. As we explain below, we believe it is important, and
sometimes crucial, to analyze designs at the level of decisions and dependencies. Only
by understanding designs at this more microscopic level can one ascertain their potential
on the order of 500 to 1000 people (Daniel Whitney, private communication).
9
See, for example, recent work on so-called “general purpose technologies” in economics, e.g.,
Bresnahan and Trajtenberg (1995), Helpman and Trajtenberg (1998), David and Wright (2003).
6
SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
to evolve, their economic value, and their probable future trajectories.
In the rest of this paper, we describe recent work that contributes to a scientific
understanding of designs across a range of fields. First, drawing on economics, we list
the properties of designs and compare them to other types of goods. Next, drawing on
engineering and computer science, we discuss design structure. We argue that there are
general and useful ways to map design structure: To support this argument, we describe
one set of methods, the so-called design structure matrix (DSM) mapping technique.
Methods such as these now make it possible to study designs as a general phenomenon, as
opposed to within particular domains of engineering, architecture, and management. Returning
to economics, we then describe the “net option value” (NOV) method of valuing designs
and their architectures and discuss the challenges of applying this method in practice.
Finally, we explain how designs both require and give rise to incentives, rewards, and
resource allocation mechanisms that, taken as a whole, amount to a system of
institutions. We recount two cases from the 1980s and 1990s in which the observable
institutions changed substantially, apparently in response to changes in underlying
design structure and value.
We are neither the first nor the only ones to contend that designs are worthy of
scientific study. Herbert Simon did so most eloquently in a series of essays and lectures
in the 1960s, and many others have followed in his footsteps. Simon even laid out the
subfields of inquiry for a general “science of design.” We have purposely not called the
studies described below the beginnings of a general science. They are simply a set of
scientific studies, widely scattered and yet related in their focus on designs as a general
phenomenon. At the end of this essay, we will speculate as to why there is as yet no
general science of design and consider whether such a field might emerge in the future.
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SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
Critical Properties of Designs
We begin by describing what we believe are the critical properties of designs.
Listing properties allows us to treat designs as general conceptual objects as opposed to
objects within a particular field or discipline. A list of properties can also serve as an
axiomatic base on which to build formal theories and models. Given a set of axioms, one
can derive testable hypotheses by considering how the properties of designs interact
with various external factors, such as constraints on resources, the presence of property
rights, and assumptions about incentives and human behavior.
10
The critical properties of designs are as follows:
•
Designing requires effort, hence designs are costly. (Here we are referring to
the cost of creating the design, not the cost of making the artifact from the
design.)
•
Designs cannot be consumed directly: Their value is derived from the functions
performed by the artifacts they describe. In most cases, designs must be
11
reified (realized or implemented) in order to be valuable. Reification means
that the design instructions are carried out and become embodied in a
physical object, a service, or an experience. The description of the process by
which the design is reified is part of the design.
•
Designs are “non-rival”; that is, one person’s utilization of a design does not
prevent another’s use of the same design.
•
Ex ante, the outcomes of design processes are uncertain.
•
In a formal sense, new designs are options.
•
Ex post, some designs are rankable within a category.
•
Designs have a structure made up of decisions and their dependencies.
Based on this list, we can compare designs to other types of things that people
need or value, as shown in Table 1. The fact that designs are costly means that they are
economic goods in scarce supply. A not-yet-complete design must therefore offer
10
This list is open to debate and discussion. Also, different subsets of these axioms may be useful
for different purposes.
11
Unreified designs may have educational or artistic value, but these are the exception, not the
rule.
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SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
(someone) enough economic value to cover the cost of completing it and of making the
artifact. However, the value offered by the design does not have to be denominated in money,
nor does it have to be exchanged. Eric von Hippel and his colleagues have demonstrated
that user-innovators may complete designs because they anticipate direct benefit from
12
use of the corresponding artifact. Even so, the decision to devote time and effort to
completing a design is an allocation of scarce resources, hence an economic action.
Table 1
Comparison of Designs to Other Types of Goods
Types of Goods
Tangible Goods
Physical Assets
(food, clothing)
(buildings, machinery)
Designs
Information Goods
(music, books)
Costly to Complete
X
X
X
X
X
Cannot be Consumed Directly
X
O
O
X
X
Non Rival
X
X
O
O
O
Uncertain Behavior and Value
X
S
S
S
S
Properties
Financial Assets
(stocks, bonds)
Optional
X
S
S
S
S
Rankable
S
S
S
S
S
Structure of Decisions/Dependencies
X
O
O
O
O
Key:
X = has the property
O = does not have the property
S = sometimes has the property
Although designs are economic goods, Table 1 also shows that they are not
exactly like any other major types of good. The main differences are highlighted by the
two boxes in the table. We discuss them below.
First of all, because designs are only a description or “shadow” of a thing, they
cannot be consumed directly. In this sense, they are not like tangible goods such as food
or clothing, nor like other types of information such as “baseball scores, books,
12
Von Hippel (1988, 2005); Franke and Shah (2003); Hienerth (2004).
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SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
13
databases, magazines, movies, music, stock quotes, and Web pages.” All of these
things can be consumed directly or used in a production process. In contrast, a design
must be turned into something—the thing specified by the design—in order to be useful.
Indeed all the other goods mentioned in the table, including the information goods, have
designs—that is, each has a set of instructions that specifies how the good will be
produced.
Designs are a kind of asset and thus may be compared to physical assets and
financial assets. Physical assets such as buildings and equipment supply a flow of
services, which can be consumed or used in a production process. Financial assets such
as stocks and bonds produce a stream of cash in the future: The cash cannot be
consumed directly but can be converted into other things. A (complete) design provides
the ability to make something in the future: In this sense it supplies a flow of “design
services.” However, the analogy between physical or financial assets and designs is not
perfect. A physical asset provides specific services; a financial asset provides general
purchasing powers; a design provides instructions for making one specific thing.
Designs can be represented as a stream of symbols, communicated in symbolic
form and translated from one language or medium to another. Thus, designs are
“information.” Like other forms of information, designs are “nonrival.” This means that
14
the use of a design by one person does not preclude another from using it too. In
general, information cannot be “consumed” in the sense of “used up.” Therefore, a
design survives its own use, although it may be lost or forgotten.
The outcome of a design process is always uncertain. If the content of a design
13
This list of information goods is taken from the introduction of the influential book Information
Rules (Shapiro and Varian 1999, p. 3).
14
Property rights, e.g. patents or copyrights, can prevent others from using the design. However,
property rights are a feature of the institutional environment (see below), not an intrinsic
property of designs.
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SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
were known, the design would already exist and the design process would be finished.
15
Because design processes are uncertain, the behavior of a newly designed artifact is not
perfectly predictable, and the ways users will react to it are not predictable either.
Therefore, the ultimate value of a design—the value users will ascribe to the artifact less
the cost of making it—is uncertain while the design process is under way. This means
that design processes, unlike production processes, cannot be algorithmic progressions
with well-controlled, guaranteed-to-be-correct outcomes (Whitney 1990).
Uncertainty in turn makes options valuable. Technically, an option is “the right
16
but not the obligation” to take a particular action. When a new design is created, users
can accept it or reject it. They have “the right but not the obligation” to solve some
problem in a new way. Formally, therefore, all new designs are options. Other types of
goods also provide options: For example, an investor may purchase an option to buy a
financial security at a fixed price. A flexible production line incorporates options to
change inputs or outputs in response to price fluctuations. Hypertext gives readers
options as to what information to seek next. But while other goods sometimes provide
options, new designs are always options.
In addition to being uncertain and optional, designs are sometimes rankable
within a category. If so, most people will agree that a particular design is best for some
17
purpose. When designs are rankable, their “optional” and “nonrival” properties
interact in a powerful way. The best design can be used by everyone (the nonrival
property), and the inferior designs can be discarded (the optional property). As a result,
competition among rankable designs will be characterized by “winner-take-all” payoffs
and serial obsolescence. Only the best designs in any cohort will be rewarded, and new
15
Clark (1985).
Merton (1998).
17
Note that goods are rankable if and only if their designs are rankable. Saying that “this
computer is better than that one (for gaming)” or “this coat is better that one (for warmth)” is the
16
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SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
and better designs (and artifacts) will replace older ones over time. In contrast, when
designs are not rankable, many designs will be rewarded, and many will survive,
serving different needs in different niches.
The above-named properties, which apply to whole designs, are sufficient for
some types of analysis. But in other cases it is necessary to look below the level of the
whole and investigate design structure in more detail. Looking at structure is especially
important when one is trying to establish the boundaries of designs for purposes of
valuation and in order to understand their evolutionary behavior. And as we discuss in
the next section, the structure of designs is determined by a pattern of decisions and
dependencies.
Design Structure
Much of science involves the study of how observable structure affects behavior.
Thus, without a structure to observe, scientific inquiry cannot begin. All of the goods
listed in Table 1 have underlying structures. Tangible goods are made up of atoms and
molecules. Financial assets are made up of contractual promises and contingencies.
“Ordinary” information goods are made up of content (e.g., baseball stories and scores)
and templates for arranging content (e.g., the sports pages of a newspaper). The
structural elements of a design are different from any of these.
In investigations of design structure, there is an emerging consensus that the
18
fundamental units of design—the smallest building blocks—are decisions. Design
decisions yield the instructions and parameters that determine the final form of the
19
artifact. Design structure in turn is determined by dependencies that exist between (or
same thing as saying “this design is better than that one (for some purpose).”
18
See, for example, two recent papers from different fields: Yu et al. (2003) and Cai and Sullivan
(2005).
19
Note that a “decision” is both a “task” (viewed ex ante) and an “outcome” (view ex post). Both
12
SCIENTIFIC STUDIES OF DESIGNS
AUGUST 4, 2005
among) decisions. Speaking informally, decision B depends on decision A if a change in
A might require a change in B. In this case, B’s decision-maker needs to know what has
been decided about A in order to choose B appropriately.
Even small designs may have thousands of associated decisions. To avoid getting
bogged down in details, design decisions that are highly interdependent may be
grouped into clusters corresponding to the components of the design. The pattern of
dependencies between any two components in turn may be independent, modular, or
integral. Two components are independent if anything in the first can change without any
impact on the second and vice versa. The designs of a laptop computer and an
automobile are essentially independent in this sense. Two components are integrally
related, or simply integral, if almost every decision about either one depends—directly or
indirectly—on decisions about the other. Finally, two components are modularly related,
or simply modular, if they are (almost) independent of each other but work together on
20
the basis of a common set of design rules.
The significance of these categories is that with independent or modular designs,
design decisions can be divided among several autonomous or semiautonomous groups.
In contrast, integral designs require close coordination, so their decisions cannot be
easily divided up. In this fashion, design structure directly affects organizational
structure—that is, how work gets done in the economy.
Independent, modular, and integral are three basic patterns of design structure.
Other patterns are possible too, and a large design may display different patterns in
views are relevant, though one or the other may dominate in different types of analysis.
20
The design rules specify what the modules must do in order to work together as a system. At a
minimum, the rules must prescribe the interfaces between interacting components, common
protocols, and conformance standards (Baldwin and Clark 2000, p. 77). Ideally, modules are
strictly independent of each other except for their common dependence on design rules. In
practice, however, some intermodular dependencies can be tolerated. For a somewhat different
definition of modularity, based on the encapsulation of functions, see Ulrich (1995).
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SCIENTIFIC STUDIES OF DESIGNS
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21
different places. This is why we think it is essential to map design decisions and
dependencies. One useful mapping technique is the so-called design structure matrix
(DSM) mapping method. We will discuss this method in some detail to give readers a
sense of what design mapping involves and what it can reveal.
22
To apply the DSM mapping method, a design is first characterized by listing the
design decisions or components of the system. (As indicated, a component is a group or
cluster of decisions.) The components are then arrayed along the rows and columns of a
square matrix. The matrix is filled in by checking—for each component—which decisions
about other components affect it and which in turn are affected by it. For example, if a
decision about component A affects some decision about B, then we put a mark “x” in
the cell where the column of A and the row of B intersect. We repeat this process until we
have recorded all the dependencies. The result is a map showing the locations of the
dependencies.
Figure 2 presents a DSM map of the dependencies in the design for a laptop
computer system circa 1993. The map shows that the laptop computer design has four
blocks of very tightly interrelated design parameters corresponding to the drive system,
the main board, the LCD screen, and the packaging of the machine. There is also a
scattering of dependencies (“x’s”) outside the blocks. The dependencies arise both above
and below the main diagonal blocks; thus, the blocks are interdependent. Because each
component depends on every other one, directly or indirectly, the overall design
structure is integral.
21
Baldwin and Clark (2000), pp. 49–62.
Other domain-independent mapping techniques include layered views of designs, design
hierarchy diagrams, and 3-dimensional (molecular) views of designs dependencies.
22
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SCIENTIFIC STUDIES OF DESIGNS
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Figure 2
Design Structure Matrix Map of a Laptop Computer
Drive
System
Main
Board
.
x
x
x
x
x
.
x
x
x
x
.
x
x
x x
x
x
x
x
x
x x
x
x
x x
x
. x x
. x
x x .
x
x
x
x x
x
Packaging
x
x
x
x x
x
x x x
x
.
x
x
x
x x x
x
x x
LCD
Screen
x
x
x
x
x
x
x
x
x
x
. x
x . x
x .
x x x
x x
x
x
x x
x
x
x
x
x
x
x x
x
x x
. x
x
x . x
x x . x
x .
x
x
x
x
x
x
x
x
x
x
. x x
x
x . x x x
x
. x
x x x . x x
x x x x
x x x . x
x
x x .
x x
x
.
x
x
x
x
x
x
x x x
x
x
x x
x
x
x
x
x x
x
x
x
x
x
.
x
x
x
x
x
x
x
x x
x x
x
.
x
x . x
x
. x
x
x .
x
x
x
x x
x
x
x x
x
x
x x
.
x .
Source: McCord and Eppinger (1993). Reprinted by permission.
Herbert Simon (1962) and Christopher Alexander (1964) appear to have been the
first to represent the dependencies of a complex system using a square matrix. Donald
Steward (1981) came independently to the same representation and identified the rows
and columns with design decisions (or components). Daniel Whitney (1990) argued that
Steward’s matrices could be used in “designing the design process” of a complex
23
artifact. Whitney, together with Steven Eppinger and his colleagues, have extended
Steward’s framework and used it to construct maps of numerous engineering design
processes and complex artifacts. We built on this prior work in developing our concepts
of modularity and design rules (Baldwin and Clark 2000). More recently, Yuanfang Cai
and Kevin Sullivan (2005) formalized the notion of pair-wise dependency among design
variables in terms of a constraint-based representation of design spaces.
23
Eppinger (1991); McCord and Eppinger (1993); Eppinger et al. (1994). See also the entries at
15
SCIENTIFIC STUDIES OF DESIGNS
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Because the fundamental elements of a DSM are decisions and dependencies,
DSMs can be constructed for any design or design architecture. Some examples are
presented below. Figure 3 shows the components and dependencies of a 10-megawatt
industrial gas turbine, a large physical artifact. Like the laptop computer, the structure
of this design is (essentially) integral. Figure 4 presents DSMs for two software
codebases. In contrast to the laptop computer and the gas turbine, these design
structures are (essentially) modular. (One should not generalize from these examples.
Tangible artifacts do not always have integral designs and codebases do not always
have modular designs.)
The modularity of the software DSMs in Figure 4 can be seen from the fact that
each has a set of almost independent block components in its lower right quadrant plus
one or more vertical columns, representing external variables and design rules, running
down the left-hand side. The DSMs also reveal an important property known as
24
“information hiding.” In these designs, external conditions, which are outside the
designer’s control yet might change, do not interact with the design rules. This is
evidenced by the fact that in the DSMs, the blocks labeled “external parameters,” “basic
concerns,” and “crosscutting concerns” have no cross-dependencies with the “design
rules” blocks.
Information hiding was proposed as a desirable property of software designs by
David Parnas in 1972. Sullivan et al. (2001) were the first to include enviromental
variables in a DSM and to characterize an information-hiding modularization as one in
25
which the design rules are invariant to the environmental variables. In effect, an
information-hiding modularization, whose presence can be verified by DSM mapping
techniques, protects the “skeleton” of the design structure from outside disruption but
http://www.dsmweb.org/publications_year.htm.
24
Parnas (1972, 2001).
25
In subsequent work, Cai and Sullivan (2005) formalized the concept of “information-hiding
16
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allows change to take place in the so-called “hidden modules.” In this fashion,
information hiding tends to localize the impact of external change on the design and
thus enhance the evolvability of the system as a whole.
26
As a final example, Figure 5 presents DSMs for two states of a codebase known
as Mozilla. These differ from the previous DSMs in several ways. First of all, these maps
are considerably larger and more detailed than the previous ones: each has more than
1500 rows and columns, while those in the previous figures had less than 50 each. It was
feasible to construct these larger and finer-scale maps because the decisions and
dependencies were automatically extracted from the artifact itself (the codebase). Source
files were used as a proxy for (clusters of) decisions and function calls were used as a
27
proxy for dependencies.
With automated mapping, DSM techniques can be applied to much larger
systems than was previously possible. However, automatic extraction can be
problematic. Some dependencies, which may have influenced a design process, do not
leave “tracks” in the finished artifact. Nevertheless, many dependencies do show up in
automated maps, and the ones that do may be the ones most likely to affect the future
evolvability of the design. More work clearly is needed to develop and to assess the
strengths and weaknesses of automated design mapping techniques.
modularity” within a particular class of mathematically represented design spaces.
26
Basically, information hiding is a strategy of encapsulation in the sense described by Kirschner
and Gerhart (1998). Information hiding localizes the impact of particular environmental changes
and thus prevents them from ramifying throughout the system. Kirschner and Gerhart argue that
encapsulation is a general property of evolvable systems.
27
Rusnak (2005).
17
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Figure 3
DSM of an Industrial Gas Turbine circa 2002
Source: Sharman et al. (2002a). Reprinted by permission.
18
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Figure 4
Two Software DSMs: Winery Locator and Hypercast
Winery Locator
Hypercast
Sources: Winery Locator DSM: Lopes and Bajracharya (2005); Hypercast DSM: Sullivan et al. (2005).
19
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Figure 5
Call Graph DSMs for Two Versions of the Mozilla Browser
Source: MacCormack et al. (2004).
20
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Figure 5 also illustrates an important point about design structure: The functions
28
of a design do not totally determine its structure. The two codebases shown here were
two versions of the same browser and were (for practical purposes) functionally
equivalent. Yet, as the figure shows, their design structures are dramatically different.
The codebase depicted on the left was developed within a company (Netscape) using
29
rapid-cycle methods. When Netscape ran into financial difficulties, the codebase was
released under an open source licence. But in the open source environment, this design
structure was found to be unsatisfactory: Open source developers did not want to
maintain or contribute to the codebase because (among other things) it was too
30
unwieldy for their methods. A small team of designers then spent half a year
“refactoring” the browser code to make its structure more modular. The resulting
change in design structure is evident in Figure 5.
These and other studies have shown quite conclusively that for complex designs,
function does not wholly determine structure. The architects of complex designs have
degrees of freedom and can satisfy functional requirements in different ways. That is
good news for consumers and entrepreneurs because it means that there is room for
improvement even of very successful designs. But it is bad news for those who would
like to predict the future of a technology without delving into the details of design
structure. Just observing “what the technology does” is not enough. It is also necessary
to look at how the underlying designs are put together—their structure of decisions and
dependencies and their so-called “technical potential”—to figure out what the future
may hold. This fact again points up the need for comparative studies of design structure
spanning a range of disciplines.
28
This statement is embarrassingly obvious to designers, engineers, and architects, but its
implications are often overlooked by managers, policymakers, and social scientists.
29
MacCormack (2001).
30
Raymond (1999).
21
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The development of DSMs and other general design mapping methods during
the past several decades now makes it possible to study designs as a general
phenomenon. Until now, most designs had to be studied within the “silos” of specific
engineering disciplines. Great theorists of design such as Herbert Simon or Allen Newell
could see unity in the phenomena and begin to sketch the outlines of a science. But
without a lingua franca that could span disciplinary boundaries, there was no way to
capitalize on their insights. DSMs and other mapping methods offer a common set of
building blocks and a general way to represent designs. These maps can be a lingua
franca that cuts across disciplines and unifies the scientific study of designs.
At the same time, all maps have limitations. Indeed, the problems inherent in
DSM mapping (for example) illustrate the difficulties that are endemic in all mapping
efforts. First of all, some patterns of dependency do not lend themselves to a flat, twodimensional representation. For example, David Sharman, Ali Yassine, and Paul Carlile
(2002a) have shown that some designs (including the gas turbine of Figure 3) are better
represented in three dimensions than two. However, all mapping involves the projection
of higher dimensional phenomena onto lower dimensional representations. Thus, while
it is important to know what may be hidden behind the projections, the fact that things
are lost (or obscured) in a mapping does not mean that the map itself is worthless.
Second, observing designs at the level of decisions and dependencies is truly
daunting: Even a small design may involve hundreds of decisions and tens of thousands
of dependencies. Thus, the clustering of design decisions into “components” or
“protomodules” is essential. In fact, we have never seen a DSM that was constructed or
observed at the level of single decisions. All DSMs aggregate decisions in some fashion,
but methods of aggregation are still largely intuitive and ad hoc. Nevertheless, one of the
strengths of this methodology is that, in practice, one can identify a dependency without
isolating the exact decision(s) that created it. Thus, a dependency can be attributed to a
cluster of decisions (a component) without understanding the structure of the cluster in
22
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detail. This in turn means that maps of dependencies can be “bootstrapped.” The
mapmaker can start with a coarse representation of decisions and dependencies and
work toward finer representations, stopping when the cost of greater detail outweighs
the benefit.
The bottom line is that we need to know more about what different maps of
design structure do and do not show. The only way we can learn more is to work
systematically with the maps we have, criticize them, improve them, and experiment
with new mapping methods. Such work is a quintessentially scientific undertaking.
Design Value
A design process is a costly venture into the unknown. Each step in the process is
expensive: formulating an architecture, completing the design, and reifying the design
once it is complete. Because the path is uncertain, there are also often costly cycles,
loops, and even blind alleys. How then can one come to an informed judgment that the
search is worthwhile? How can one know if the expected benefits are likely to exceed
the inevitable costs? And when confronted with different ways of organizing the design
process—different design architectures—how can one decide which is likely to result in
a better design at the end of the day?
The questions just posed are all about the comparative value of different
alternatives. The valuation of alternatives in turn is the focus of the branch of applied
mathematics that deals with decision-making under uncertainty. During the past fifty
years, this field has grown in many directions. One of its main subfields, which has
developed within the discipline of economics, deals with the valuation of options. As we
said earlier, every new design embodies at least one option and some involve many.
Thus, option theory is highly relevant to decisions about whether to undertake and how
23
SCIENTIFIC STUDIES OF DESIGNS
to organize design search processes.
AUGUST 4, 2005
31
Just as DSMs offer a general way to represent designs across a range of fields,
option theory offers a general way to value designs at any point in their existence, from
prearchitecture to postreification. In fact, design representation and design valuation are
inseparable. The application of option theory requires that the boundary and scope of
the individual options in a design be crisply delineated. If, as is often the case, the design
embeds multiple options, they must be enumerated. The boundary and scope of options
in turn are determined by the underlying pattern of dependencies. Basically, designs
that have many independent (or quasi-independent) components contain more options
and are likely to have higher option value. And the boundaries of options correspond to
the “thin crossing points” in a map of dependencies.
32
Modular designs require components to be (almost) independent of one another,
linked only by design rules. Because they are (almost) independent, the module designs
can be “mixed and matched” and can evolve along separate paths independently of one
33
another. In this fashion, modular designs create options (hence option value) in the
later stages of a design process. Coordination across modules is accomplished via design
rules, which all groups must obey and in turn can expect others to obey. Although
subject to design rules, each module embodies a separate and distinct set of options. In
practice, this means that the design of a module can change and improve over time
without regard to what is happening in other modules and without harming the rest of
the system.
A general formula for the value of a complex design can be written as a sum of
31
Robert Merton was the first to put forward a general theory of option valuation based on the
principles of dynamic decision-making under uncertainty (Merton 1973). Design options differ
from financial options in two important ways: First, they are “real options,” meaning that their
exercise affects the world, and second, there is usually no underlying asset to be replicated, thus
Black-Scholes replication does not apply. Despite these differences, design options fall within
Merton’s general framework (Merton 1998).
32
Baldwin and Clark (2003); Gomes and Joglekar (2004).
24
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the value of a “minimal system” plus the values of individual modules. The value of
individual modules in turn is determined by the functions they perform for the end user
34
or for the system. By convention, this method is known as the NOV (net option value)
approach to design valuation. The NOV approach has been applied to several actual
designs, including those depicted in Figures 3 and 4. But this approach to design
valuation is still in its infancy. As is common with early-stage work, each application of
the methodology has raised as many questions as it has settled.
The most salient questions are: (1) How does one translate achieved functionality
into value, and (2) from what probability distribution(s) are the uncertain design
outcomes being drawn? We know from both logic and observation that designs are
valued because of the functions that “their” artifacts perform, and that design outcomes
are uncertain. Furthermore, designers and architects regularly make qualitative
judgments about the potential of different designs and design architectures to achieve
functionality and deliver value in return for effort. Nevertheless, as of today, we have no
data to support statistical estimation of the relevant probability distributions, and there
35
is also no theory to tell us what those distributions “should” look like.
In essence, design valuation today is in a state similar to that of insurance
33
34
In contrast, the design rules, including the interfaces, must remain relatively fixed.
The formula is as follows:
System Value = S0 + NOV1 + NOV2 + ... + NOVj
;
where S0 is the value of a minimal system and the NOVi are the values of each module. Each
module’s value in turn can be written as
[
NOVi = max i (ni )1/2 Q(ki ) C(ni )ki Zi
ki
]
;
where ki is the number of experimental trials conducted on the ith module; i (ni )1/2 Q(ki ) is the
expected value of the best of ki designs, C(ni )ki is the cost of the experiments, and Zi measures
the degree to which the module is “hidden” from others.
35
As a working assumption, the NOV method assumes that design outcomes have normal,
mean-zero, i.i.d. distributions. The differences between distributions for different modules then
come down to differences in the parameter of “technical potential,” denoted .
25
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contracting 350 years ago. At that point in time, life and property insurance contracts
were being bought and sold, but statistics on mortality and property losses were not
available to the buyers and sellers. As a result, the pricing of insurance contracts was a
helter-skelter, catch-as-catch-can affair. Many mistakes were made, and many frauds
were perpetrated because of the lack of objective data on which to base projections of
36
future claims.
With respect to design valuation today, we have a promising framework based
on robust mathematical and economic logic. But we have insufficient data on hand to
support formal hypothesis testing or statistical inference of the key parameters
describing functions or value. Making matters worse, design valuation is more complex
than insurance valuation, because the functions of artifacts are far more diverse than the
functions of insurance contracts. However, research is even now being done to address
these gaps. The work mainly focuses on open source codebases: These are promising
sites for scientific work because they are accessible and because they often have welldocumented design histories. Currently, two separate research efforts are under way
that aim to correlate codebase changes with achieved functionality and value in order to
37
assess option value. These studies represent important first steps toward building up
useful data on design functions and outcomes, which can support more objective and
quantitative methods of design valuation in the future.
Design Games and the Institutions of Innovation
The intrinsic difficulty of design valuation is compounded by the fact that in
modern economies, companies and entrepreneurs play complex, competitive “value-
36
Hacking (1975). The problems of insurance valuation were a key driver in the development of
modern probability theory and statistics.
37
Bajracharya and Ngo (2005); Karim Lakhani and Neil Conway (private communication).
26
SCIENTIFIC STUDIES OF DESIGNS
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38
capture games” within design architectures. As a result of these “games,” the value
created by an evolving set of designs does not always stay in the same hands. This is
good news for society but bad news for science, which must track design value as it
moves around and seek explanations for its movement.
Sometimes the first to introduce a new architecture captures the lion’s share of its
value. At other times, value is captured by those who focus on a small set of modules. In
the marketplace of personal computers, for example, Intel Corporation represents the
first type of success; Dell Computer Company represents the second. IBM is an example
of an architect-firm that failed to capture long-term value from its PC architecture;
Compaq Computer first succeeded and then failed at competition focused on modules
of the PC architecture.
The complexity of value-capture games means that the scientific study of designs
must distinguish between achieved functionality (a property of a complete design) and
the financial success of the design’s creators, owners, or sponsors. Achieved
functionality is necessary, but not sufficient, for financial success. The Internet, for
example, is a triumph of achieved functionality, but it is not “owned” by anyone. It has
not made its creators rich in proportion to the value it has created for others.
The distinction between value created and value captured points to another topic
in the general scientific study of designs: the study of “what designs need” from the
economy and from society. As we said above, designing is a costly activity. For a
complex design, several stages of cost must be incurred before a (hopefully) valuable
artifact or system can come into existence. The economy and society must therefore
structure incentives and rewards and provide resource allocation mechanisms to
support these stages from beginning to end. Taken as a whole, the incentives, rewards,
and mechanisms that support the creation and reification of designs constitute a system
38
Brandenburger and Nalebuff (1996).
27
SCIENTIFIC STUDIES OF DESIGNS
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of institutions in the formal sense defined by Masahiko Aoki (2001).
According to Aoki, institutions can be viewed as equilibria of linked games with
self-confirming beliefs. As a result, the properties of institutions can be derived from the
formal specification of a game. The properties of designs, design structure, and design
value in turn can be part of the formal specification. Thus, from Aoki’s theory of
institutions, it is possible for the first time to develop a formal and comprehensive
theory of the institutional systems needed to support the creation of new designs. These
systems perforce are institutions of innovation.
Using Aoki’s methods, studies of the institutions of innovation can be based on
the twin foundations of design structure and design value. Design structure constrains
the form and organization of the institutions; design value supplies the fuel (in the form
of incentives and rewards) and channels it (via resource allocation mechanisms) to
different points in the design structure. However, as we have seen, scientific studies of
design structure and design value are just getting started: thus, it may be too early for
formal studies of institutions to get off the ground. Still, the need for this type of work is
clear. If we want to understand how designs affect the world from a scientific
perspective, we must look at how they affect and are in turn affected by institutions.
Because the formal study of institutions through the lenses of design structure
and design value is brand new, there is hardly any “recent work” to report yet.
39
39
There is, of course, a large and valuable literature that looks at the institutions of innovation
from other perspectives. First and foremost is Richard Nelson and Sidney Winter’s path-breaking
book An Evolutionary Theory of Economic Change, which has stimulated an enormous amount of
scholarly research since first published in 1982. Much of the work in this line deals implicitly with
the impact of new designs on corporate and institutional structures and vice versa. See, in
particular, seminal papers by Langlois and Robertson (1992), Garud and Kumaraswamy (1995),
Sanchez and Mahoney (1996), and Schilling (2000) on modular designs and organizational forms,
as well as recent contributions by Brusoni and Prencipe (2001), Sturgeon (2002), and Jacobides
(2005). Murmann’s study of the coevolution of chemical engineering science and institutions in
Britain and Germany in the late 19th century is especially revealing of the interaction between the
“needs” of a set of new product designs and the institutional structures that were developed to
fulfill those “needs” (Murmann 2003). Other related work evaluates search strategies on abstract
value landscapes: see, for example, Levinthal (1997), Rivkin (2000), and Rivkin and Siggelkow
(2003). What is new today is the opportunity to integrate explicit characterizations of design structure and
28
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Instead, in the remainder of this section, we will describe two recent cases wherein the
institutions changed—visibly and radically—apparently because the structure and value
40
of the underlying designs changed. Because we understand so little, it is appropriate to
frame these two cases as “puzzles.” Solving these “puzzles,” we believe, requires
research on how design structure and value together create a nexus in which new
institutional forms can arise and flourish.
Puzzle #1—Vertical-to-Horizontal Industry Transitions and Modular Clusters
In 1995, Andy Grove described a vertical-to-horizontal transition in the computer
41
industry. In a now-famous picture (Figure 6), he described the transformation of that
industry from a set of vertically integrated “silos,, e.g., IBM, DEC, Sperry Univac, and
Wang, to a large number of firms spread out among a set of horizontal layers:
specifically, the chip layer, the computer layer, plus the operating system, application
software, and sales and distribution layers.
design value with Aoki’s game-theoretic approach to institutions.
40
Strojwas (in progress) considers another case in which the institutions of innovation
changed in response to changes in design structure and value.
41
Grove (1996).
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Figure 6
The Vertical-to-Horizontal Transition in the Computer Industry
"Vertical Silos"
"Modular Cluster"
Source: Adapted from Grove (1996, p. 44).
Grove did not know exactly what had caused this transition. Intuitively, he felt it
was spurred by changes in the cost of components and the recombinant possibilities of
the underlying designs, that is, by changes in design structure and value:
A consumer could pick a chip from the horizontal chip bar, pick a consumer
manufacturer from the computer bar, choose an operating system … grab one of
the several ready-to-use applications off the shelf … and take the collection of
these things home. … He might have trouble making them work, … but for
$2000 he had just bought a computer system … .42
But though the causes were unclear, Grove believed the consequences of the transition
were profound:
Going into the eighties, the old computer companies were strong, growing and
vital. ... But by the end of the eighties, many large vertical computer companies
were in the midst of layoffs and restructuring … .
[A]t the same time, the new order provided an opportunity for a number of new
entries to shoot into preeminence.43
Grove’s horizontal industry structure is distinguished by the fact that most firms
42
Ibid., pp. 41–42.
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in it make modules that are in turn parts of larger systems. For this reason, we call this
industry structure a “modular cluster.” Modular clusters can be made up of hundreds or
even thousands of firms operating in many “submarkets,”, i.e., different but
complementary product categories.
The “modular cluster” form of industry structure is—probably—an institution of
innovation, meaning that its form responds to the structure and value of an underlying
set of designs. This form emerged in the computer industry between 1975 and 1990
during a time when computer design architectures were becoming increasingly modular
and “open,” giving rise to the “mix-and-match” property Grove described. The
consequences of the transition were indeed vast—in terms of value created for
consumers, value created (and destroyed) for investors, and turbulence in participation
and market shares. At least one other industry—mortgage banking— has gone through
a vertical-to-horizontal transition, as documented by Michael Jacobides (2005). Other
industries such as telecom and pharmaceuticals are allegedly moving in the same
direction and may become modular clusters in the process. But the causes of these
transitions—in particular their roots in the underlying design structures and values—
remain quite mysterious.
44
Puzzle # 2: Open Source Development of Linux
Our second institutional puzzle is the emergence of the open source development
process in the 1990s. Before 1990, it was widely believed that software code above a
certain level of complexity had to be designed and built by a tightly knit team of
dedicated experts. Eric Raymond described his own views at that time as follows:
[I] believed there was a certain critical complexity above which a more
43
Ibid., p. 45.
Jason Woodard (in progress) is conducting computational experiments designed to shed light
on how and why modular cluster form and how such clusters evolve.
44
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centralized, a priori approach was required. I believed that the most important
software (operating systems and really large tools like the Emacs programming
editor) needed to be built like cathedrals, carefully created by individual wizards
or small bands of mages working in splendid isolation, with no beta released
before its time.45
The rationale for these beliefs was convincingly set forth in Fred Brooks’ classic,
The Mythical Man-Month, published in 1975. Brooks was one of the chief architects of
IBM’s System/360, the first “truly modular” computer system. But when he attempted
46
to partition the design of the System/360’s system software into discrete modules (as
had been done with the hardware), the attempt failed. Reflecting later on this (and other)
software engineering projects he had led, he formulated Brooks' Law:
Adding manpower to a late software project makes it later..47
In a nutshell, Brooks argued that when new people are added to a project, the extra tasks
of training them and repartitioning the work would drag down the performance of the
group. Accordingly, Brooks advocated the “small sharp team” approach to software
design and development:
[O]ne wants the system to be built by as few minds as possible.48
[T]he entire system also must have conceptual integrity, and that requires a
system architect to design it all, from the top down.49
Against the backdrop of Brooks’ Law, Linux and its development process
emerged in the mid-1990s as an anomaly. According to Raymond:
Linus Torvalds’ style of development—release early and often, delegate
everything you can, be open to the point of promiscuity—came as a surprise. No
quiet, reverent cathedral building here—rather the Linux community seemed to
ressemble a great, babbling bazaar of differing agendas and approaches … out of
which a coherent and stable system could seemingly arise only by a succession of
miracles. …
… [But] the Linux world not only didn’t fly apart in confusion, [it] seemed to go
45
Raymond (1999), p. 29.
“System software” is now called the computer’s operating system.
47
Brooks (1995), p. 25. Italics in original.
48
Ibid. p. 30.
49
Ibid. p. 37.
46
32
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from strength to strength... .
AUGUST 4, 2005
50
In fact, Linux was only one, albeit the most visible, of a group of open source
codebases that came into public view during the 1990s. These codebases were developed,
debugged, and maintained by self-described communities of user-developers. In contrast
to Brooks’ notion of “small, sharp teams,” open source methods seemed to be anarchic.
Tens, hundreds, or even thousands of people would participate in the creation and
evolution of a codebase on a voluntary, as-needed basis.
Open source development communities are also—probably—institutions of
innovation. It appears that some design structures can support and benefit from this
form of organization, while others cannot. (Linux is an example of the former type; the
first-released Mozilla codebase, depicted on the left-hand side of Figure 5, is an example
of the latter type.) In related work, we have argued that design structure and value can
explain the scale of effort that will be drawn into an open source development process.
51
But as with modular clusters, there is still much work to be done to place this argument
on a firm scientific footing.
Scientific Studies of Designs vs. Simon’s Science of Design
In this paper we have argued that designs are worthy of investigation as a
general phenomenon and can be the object of scientific study across disciplines. Herbert
Simon said the same thing more than forty years ago. Never given to understatement, he
sought to rearrange Alexander Pope’s famous dictum, saying:
The proper study of mankind has been said to be man. ... If I have made my case,
then we can conclude that, in large part, the proper study of mankind is the science of
52
design.
50
Raymond, op. cit. p. 30.
Baldwin and Clark (in press).
52
Simon (1981), p. 159. Italics added. Pope’s lines, from An Essay on Man are
Know then thyself, presume not God to scan;
The proper study of Mankind is Man.
51
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Reality has not lived up to Simon’s vision, however. At present, most scientific
work on designs takes place in widely separated, often noncommunicating fields. The
study of designs has made no dent on the natural or social sciences. There is no
recognized field called the “science of design.”
Putting Simon’s bluster aside, why has so little happened? Why did his
compelling vision fail to materialize? Historian of science Peter Galison (1987) has
argued that scientists will go where their tools of observation and analysis take them,
but can go no further. We think that Simon, with characteristic optimism, greatly
underestimated the complexity of actual designs and overestimated the capacity of our
tools to measure, sort, categorize, and compare designs across different domains. He
assumed that designs would be easily accessible to “full inspection and analysis.” This is
simply not the case. A design can be made up of a million different instructions. Such an
object cannot be categorized, taxonomized, or compared to others very easily. Yet for
purposes of conducting science, the ability to observe an object in its raw state is not
enough. One also needs tools that can convert raw observations into useful summaries,
projections, and views—and do so efficiently.
Designs have proved to be much more complicated than Simon perceived in the
late 1960s. Moreover, the abstractions needed to support different design processes
across a range of fields are not very similar, even when they are all expressed in digital
formats. The CAD files describing a building are not easily compared to the source code
of an operating system. Both are expressed in computer-readable languages, but the
translation from one to the other is difficult and tedious. Simon did not foresee such
high barriers to integration across different fields of design. Thus today, while there are
many places where designs are studied scientifically, there is no unified “science of
design” of the type Simon envisioned.
34
SCIENTIFIC STUDIES OF DESIGNS
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Given that unification has not happened yet, it is hard to be optimistic about the
possibility of a truly general science of design emerging in the near future. Nevertheless,
as we have tried to show, in the forty-plus years since Simon delivered his manifesto,
there has been significant progress in building tools that can be applied to the scientific
study of designs as a general phenomenon. As a result, the “Galison gap” that existed in
the 1960s may have shrunk somewhat. The most important tools, we believe, address
the three areas of inquiry identified above: structure, value, and institutions. In support
of design structure analysis, there are DSMs and other mapping methodologies, which
can be a lingua franca of design structure. In support of design valuation, there is option
theory, functional valuation, and the net option value (NOV) method. And in support of
institutional studies, there are the methods of comparative institutional analysis
pioneered by Masahiko Aoki (2001).
Significantly, the new tools are compatible and complementary. They have a
common mathematical base: search and decision-making under uncertainty in complex
design spaces. Hence, with these new tools, three previously separate areas of
inquiry—design structure, design value, and institutions of innovation—can be
integrated in mutually supportive ways. Indeed, many of the works cited above have
already done so with preliminary but exciting results. In these works one can see how
design structure affects value, but value helps to predict the evolution of structure. One
can see how design structure constrains institutional forms, but institutions also
influence changes in design structure over time. One can see that design value matters
because it both predicts and rewards behavior, while institutions are important because
they filter value. These and other insights are being verified and amplified as the work
proceeds.
In conclusion, new tools of observation and analysis now make it possible for
widely scattered studies of design structure, value, and institutions to come together and
begin to build upon one another. If that were to happen, the separate scientific studies of
35
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design would coalesce into a general science of design and Simon’s vision would
become a reality. At this point in time, the barriers to integration are still very high, but
they are coming down. Thus, with new tools in hand, we are—cautiously—optimistic.
36
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References
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