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SCIENTIFIC STUDIES OF DESIGNS AUGUST 4, 2005

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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 toSushil Bajracharya, Cristina Videira Lopes, John Rusnak, Alan MacCormack, JoachimHenkel, Michael Jacobides, Nitin Joglekar, Gregor Kiczales, Karim Lakhani, Sonali Shah,Mary Shaw, and Edwin Steinmuller for sharing key data and insights. Finally, we wouldlike to thank participants in the NSF Science of Design Workshop, the MIT University ofMunich Innovation Workshop, and the Conference on Advancing Knowledge and theKnowledge Economy for conversations that contributed to this paper in significantways. We alone are responsible for errors, oversights, and faulty reasoning.

A previous, shorter version of this paper was entitled “Designs and DesignArchitecture: The Missing Link between ‘Knowledge’ and the ‘Economy.’ ”

*Direct correspondence to Carliss Y. Baldwin, Harvard Business School,[email protected]

Copyright © Carliss Y. Baldwin and Kim B. Clark, 2005


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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 policy-

makers. 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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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

things have designs.1 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

to the problem.3 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

instructions ... like a piece of software or a recipe.” 4 Thus, it is only through the agency of

designs that knowledge can become the basis of real goods and services.

1 Simon (1981), p. 129.2 Baumol (2002).3 Simon op cit., pp. 6 8; Alexander (1964), pp. 55 70.4 Mokyr (2002), pp. 4–21.


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Figure 1Designs Link Knowledge to the 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

the performance of the components and the system.5 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 systemto be created without the agency of a design architect. In that case, the system itself will have anarchitecture in the sense of “an abstract description of the entities of a system and therelationships between those entities” (ESD Architecture Committee 2004). However, the systemarchitecture 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 designis a point within a design space. “Value” is a mapping of a mathematical function onto a designspace; the process of design can be thought of as a search through a design space for high pointsin a “value landscape” (Simon 1981, pp. 136–144; Baldwin and Clark 2000, pp. 24–28, 232–234). Incomputer 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

Knowledge Design Economy

Architecture Completion


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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

variety.7

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,

computer science, architecture, and management.8 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 (Shawand 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 andcomplexity theory.7 Improvement (adaptation) and variety (radiation into niches) are two aspects of designevolution.8 The community of scholar researching “design theory and methods” is estimated to be roughly


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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”

at quite a high level of abstraction.9 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).


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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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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.

The critical properties of designs are as follows:10

• Designing requires effort, hence designs are costly. (Here we are referring tothe cost of creating the design, not the cost of making the artifact from thedesign.)

• Designs cannot be consumed directly: Their value is derived from the functionsperformed by the artifacts they describe. In most cases, designs must bereified (realized or implemented) in order to be valuable.11 Reification meansthat the design instructions are carried out and become embodied in aphysical object, a service, or an experience. The description of the process bywhich the design is reified is part of the design.

• Designs are “non-rival”; that is, one person’s utilization of a design does notprevent 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 usefulfor different purposes.11 Unreified designs may have educational or artistic value, but these are the exception, not therule.


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(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

use of the corresponding artifact.12 Even so, the decision to devote time and effort to

completing a design is an allocation of scarce resources, hence an economic action.

Table 1Comparison of Designs to Other Types of Goods

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).

Types of GoodsInformation Goods Tangible Goods Physical Assets Financial Assets

Properties Designs (music, books) (food, clothing) (buildings, machinery) (stocks, bonds)

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

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 propertyO = does not have the propertyS = sometimes has the property


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databases, magazines, movies, music, stock quotes, and Web pages.”13 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

the use of a design by one person does not preclude another from using it too.14 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 InformationRules (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 intrinsicproperty of designs.


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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

but not the obligation” to take a particular action.16 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

purpose.17 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).16 Merton (1998).17 Note that goods are rankable if and only if their designs are rankable. Saying that “thiscomputer is better than that one (for gaming)” or “this coat is better that one (for warmth)” is the


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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

fundamental units of design—the smallest building blocks—are decisions.18 Design

decisions yield the instructions and parameters that determine the final form of the

artifact.19 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


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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

the basis of a common set of design rules.20

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 aminimum, the rules must prescribe the interfaces between interacting components, commonprotocols, and conformance standards (Baldwin and Clark 2000, p. 77). Ideally, modules arestrictly independent of each other except for their common dependence on design rules. Inpractice, however, some intermodular dependencies can be tolerated. For a somewhat differentdefinition of modularity, based on the encapsulation of functions, see Ulrich (1995).


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different places.21 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. 49–62.22 Other domain-independent mapping techniques include layered views of designs, designhierarchy diagrams, and 3-dimensional (molecular) views of designs dependencies.


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Figure 2Design Structure Matrix Map of a Laptop Computer

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

artifact. Whitney, together with Steven Eppinger and his colleagues,23 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

. x x x x x

x . x x x x x x x x x

Drive x x . x x x

System 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

Main x x x . x x x

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 . x x x

x x x x . x x x x

LCD x x x . x x

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

Packaging 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 .


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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

“information hiding.”24 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

which the design rules are invariant to the environmental variables.25 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


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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

proxy for dependencies.27

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 Kirschnerand Gerhart (1998). Information hiding localizes the impact of particular environmental changesand thus prevents them from ramifying throughout the system. Kirschner and Gerhart argue thatencapsulation is a general property of evolvable systems.27 Rusnak (2005).


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Figure 3DSM of an Industrial Gas Turbine circa 2002

Source: Sharman et al. (2002a). Reprinted by permission.


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Figure 4Two Software DSMs: Winery Locator and Hypercast

Winery Locator Hypercast

Sources: Winery Locator DSM: Lopes and Bajracharya (2005); Hypercast DSM: Sullivan et al. (2005).


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Figure 5Call Graph DSMs for Two Versions of the Mozilla Browser

Source: MacCormack et al. (2004).


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Figure 5 also illustrates an important point about design structure: The functions

of a design do not totally determine its structure.28 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

rapid-cycle methods.29 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

unwieldy for their methods.30 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 itsimplications are often overlooked by managers, policymakers, and social scientists.29 MacCormack (2001).30 Raymond (1999).


22

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, two-

dimensional 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


23

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


24

to organize design search processes.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

another.33 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 theprinciples of dynamic decision-making under uncertainty (Merton 1973). Design options differfrom financial options in two important ways: First, they are “real options,” meaning that theirexercise affects the world, and second, there is usually no underlying asset to be replicated, thusBlack-Scholes replication does not apply. Despite these differences, design options fall withinMerton’s general framework (Merton 1998).32 Baldwin and Clark (2003); Gomes and Joglekar (2004).


25

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

or for the system.34 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

is also no theory to tell us what those distributions “should” look like.35

In essence, design valuation today is in a state similar to that of insurance

33 In contrast, the design rules, including the interfaces, must remain relatively fixed.34 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. Eachmodule’s value in turn can be written as

NOVi = max

kii (ni )

1/2 Q(ki ) C(ni )ki Zi[ ] ;

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 measuresthe 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 thencome down to differences in the parameter of “technical potential,” denoted .


26

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

future claims.36

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 well-

documented design histories. Currently, two separate research efforts are under way

that aim to correlate codebase changes with achieved functionality and value in order to

assess option value.37 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 ofmodern probability theory and statistics.37 Bajracharya and Ngo (2005); Karim Lakhani and Neil Conway (private communication).


27

capture games” within design architectures.38 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).


28

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 innovationfrom other perspectives. First and foremost is Richard Nelson and Sidney Winter’s path-breakingbook An Evolutionary Theory of Economic Change, which has stimulated an enormous amount ofscholarly research since first published in 1982. Much of the work in this line deals implicitly withthe impact of new designs on corporate and institutional structures and vice versa. See, inparticular, 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 inBritain 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 tofulfill those “needs” (Murmann 2003). Other related work evaluates search strategies on abstractvalue 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


29

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

of the underlying designs changed.40 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

industry.41 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 innovationchanged in response to changes in design structure and value.41 Grove (1996).


30

Figure 6The Vertical-to-Horizontal Transition in the Computer Industry

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 consumermanufacturer from the computer bar, choose an operating system … grab one ofthe several ready-to-use applications off the shelf … and take the collection ofthese 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 andvital. ... But by the end of the eighties, many large vertical computer companieswere in the midst of layoffs and restructuring … .

[A]t the same time, the new order provided an opportunity for a number of newentries to shoot into preeminence.43

Grove’s horizontal industry structure is distinguished by the fact that most firms

42 Ibid., pp. 41–42.

"Vertical Silos"

"Modular Cluster"


31

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. 45.44 Jason Woodard (in progress) is conducting computational experiments designed to shed lighton how and why modular cluster form and how such clusters evolve.


32

centralized, a priori approach was required. I believed that the most importantsoftware (operating systems and really large tools like the Emacs programmingeditor) needed to be built like cathedrals, carefully created by individual wizardsor small bands of mages working in splendid isolation, with no beta releasedbefore 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

to partition the design of the System/360’s system software46 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 asystem 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, delegateeverything you can, be open to the point of promiscuity—came as a surprise. Noquiet, reverent cathedral building here—rather the Linux community seemed toressemble a great, babbling bazaar of differing agendas and approaches … out ofwhich a coherent and stable system could seemingly arise only by a succession ofmiracles. …

… [But] the Linux world not only didn’t fly apart in confusion, [it] seemed to go

45 Raymond (1999), p. 29.46 “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.


33

from strength to strength... .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 ofdesign.52

50 Raymond, op. cit. p. 30.51 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.


34

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.


35

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


36

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.


37

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