BLS WORKING PAPERS 

 

U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Productivity and Technology 

Network of Tinkerers: A Model of Open-Source Technology Innovation 

Peter B. Meyer, U.S. Bureau of Labor Statistics 

Working Paper 413

November 2007

All views expressed in this paper are those of the author and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
Network of Tinkerers:

A Model of Open-Source Technology Innovation

Peter Benjamin Meyer ∗

U.S. Bureau of Labor Statistics †

meyer.peter@bls.gov

November 5, 2007

Abstract

Airplanes were invented by hobbyists and experimenters, and some personal computers were

as well. Similarly, many open-source software developers are interested in the software they

make, and not focused on profit. Based on these cases, this paper has a model of agents called

tinkerers who want to improve a technology for their own reasons, by their own criteria, and

who see no way to profit from it. Under these conditions, they would rather share their

technology than work alone. The members of the agreement form an information network.

The network’s members optimally specialize based on their opportunities in particular aspects

of the technology or in expanding or managing the network. Endogenously there are

incentives to standardize on designs and descriptions of the technology. A tinkerer in the

network who sees an opportunity to produce a profitable product may exit the network to

create a startup firm and conduct focused research and development. Thus a new industry can

arise.

∗With thanks to Harley Frazis, Tomonori Ishikawa, Anastasiya Osborne, Larry Rosenblum, Leo Sveikauskas,

Cindy Zoghi, and participants at seminars at BLS, the Midwest Economics Association, BEA, the Naval Postgraduate

School, the 2006 International Economic History Congress, and OSSEMP 2007. JEL code O31. Keywords: Open

source; invention; innovation; tinkerers; technological change

†All views expressed in this paper are those of the author and do not necessarily reflect the views or policies of the

U.S. Bureau of Labor Statistics.

1
Some important technologies have been advanced by open sharing among innovators who

were not motivated mainly by prospective profits. For example, many hobbyists around the world

tried to make aircraft in the late 1800s, before there were what we now call airplanes. Personal

computers were advanced greatly by hobbyists who met in groups, notably at the Homebrew

Computer Club (Freiberger and Swaine (1984) and Levy (2001)). Many open-source software

projects make source code publicly available. The airplane, personal computer, and open-source

software cases are examples of “open source” technology development processes.

Allen (1983) introduced the related term collective invention to describe firms sharing

technical information. Schrader (1991), Nuvolari (2002), and von Hippel (2005) offer other

examples. Harhoff, Henkel, and von Hippel (2003) model this phenomenon. In this literature, the

technology is known to deliver useful outputs, and the profit-minded firms exchange information.

By contrast, this paper describes situations in which a novel technology appears first because of

the combined efforts of people who do not expect to sell anything.

This paper places open source technology development in an abstract, deductive model.

Three key assumptions are necessary. First, agents called tinkerers are interested in advancing the

technology for some reason, such as their own inherent interest. Second, each tinkerer sees how

to improve the technology using his own criteria for improvement. Third, the tinkerers believe the

technology is so immature and uncertain that current actions do not significantly affect future

opportunities for commercialization. Under these conditions, tinkerers share their technologies

with one another, forming an an open-source network.

Within the model, a new industry appears when a tinkerer envisions a way to profit from the

technology, and leaves the network to try that. In the cases described, amateur tinkerering

eventually led to increases in commercial output and productivity.

2
I Examples of open-source technology development

I.A Before the airplane

For decades before there were functional airplanes, there was an international discussion

about wings and flying machines. By the 1890s several journals and societies in France, Britain,

Germany, and the United States were devoted to this topic. Important experiments by Otto

Lilienthal, Samuel Langley, and Lawrence Hargrave advanced the field.

A Chicago railroad engineer named Octave Chanute was inspired by the possibility that by

cooperating, experimenters around the world could make winged flying machines a reality. He

visited many of them, and corresponded with many more. Chanute’s speeches and writings were

“noteworthy for fostering a spirit of cooperation and encouraging a free exchange of ideas among

the world’s leading aeronautical experimenters” (Stoff, 1997, p. iv). In his optimistically titled

1894 book Progress in Flying Machines, Chanute summarized and commented on hundreds of

kites, gliders, experimenters, authors, and theorists of aerial navigation. Newly interested people

learned about the subject from this important book. Wilbur and Orville Wright read it and

contacted Chanute.

Like many others, the Wrights discussed their experiments openly. Chanute visited them and

invited colleagues to participate in their effort. At Chanute’s invitation, Wilbur Wright made a

speech about their experiments at the Western Society of Engineers. Wilbur Wright published in

British and German aircraft journals. In other words, the Wrights took an open source perspective

on their technology as they advanced it.

In 1902 and 1903, the Wrights developed better wings and propellers than their predecessors,

partly because of the uniquely accurate and precise measurements they got from their wind tunnel

and its instrumentation. They began to withdraw from processes of open sharing as they believed

they were near to a successful powered glider flight. (Crouch, 2002). They planned to protect

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their rights to patent and license their technology. This led to permanent conflicts with Chanute,

who was devoted to open-source processes of invention.

I.B The beginning of personal computers

In the 1970s many clubs of hobbyists were working on microcomputers. The Homebrew

Computer Club which met in Menlo Park and Palo Alto, California, starting in March, 1975 was

particularly central. Most of the people who attended were interested in making computers for

their own home use. At the first meeting, “it turned out that six of the thirty-two had built their

own computer system of some sort, while several others had ordered Altairs” (Levy, 2001, p.

202). The Altair was a new kit for making a hobbyist computer.

Meetings were informal. “The group had no official membership, no dues, and was open to

everyone. The newsletter, offered free . . . became a pointer to information sources and a link

between hobbyists.” (Freiberger and Swaine, 1984, p. 106) “They discussed what they wanted in

a club, and the words people used most were ‘cooperation’ and ‘sharing’.” (Levy, p. 202).

Homebrew meetings included a presentation, often of a demonstration of a club member’s latest

creation. Then there was “the Random Access session, in which everyone scrambled around the

auditorium to meet those they felt had interest in common with them. . . . . [M]uch information

had to be exchanged; they were all in unfamiliar territory” (Freiberger and Swaine, 1984, p. 106).

The information flow was a cause and also an effect of the fact that they often used similar

parts, attempted similar projects, and read the same newsletters and magazines. Members were

drawn to the hands-on experience of making computers and understanding the component parts,

not theories of computing, or the social effects of computing. (Levy, 2001)

The Homebrew club of hobbyists had an important effect in moving personal computer

technology forward. There were many other places for hobbyists to get involved in this exciting

4
area. There were a series of (U.S.) West Coast Computer Faires which gathered tremendous

interest and attendance. Hobbyists ran bulletin board personal computer systems to which people

could dial in and send email, and engaged in Usenet discussions on the Internet. Hobbyists did

this activity, mostly not for profit.

At one Homebrew meeting, Steve Wozniak demonstrated a new board which could do many

things a computer would do. He did not intend to start a company or sell anything, but his

entrepreneurial friend Steve Jobs convinced him to cofound a company and to sell this product as

a computer, which they called the Apple I. Only computer hobbyists could use it, but among them

it was quickly in demand. The personal computer industry took off with this device.

Apple Computer, and perhaps twenty other companies, were started by Homebrew attendees.

But the club started because of an interest in computers, not business.

I.C Open source software projects

In open-source software, human-readable source code files are made widely available on a

computer network. Source code, in computer languages, is fed as input to specialized

development tool programs, such as interpreters, compilers, assemblers, and linkers, which

generate the instructions which a computer eventually executes.

Sharing source code makes it possible for many programmers to experiment and improve the

code in parallel. A user may also alter the program for a particular purpose. Sponsors of open

source projects usually copyright the software in a way that allows a wide spectrum of uses.

Revisions are published under the same license. This is a powerful mechanism to support

collective invention because it is common knowledge that some later improvements will become

part of the shared code.

The owners of a chunk of source code moderate the final choices in released versions of the

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software. Users may make a version different from a released one. The owners try to avoid the

project’s source code “forking” into permanently divergent, partly-incompatible versions. If that

were to happen, the project’s members would lose some of the benefits of having one code base

which improved along many dimensions over time.

Several roles and institutions support sharing in open source projects:

• Web servers store the source code.

• Intellectual property claims are explicitly preempted by special open copyrights.

• The relevant programmers have similar development tools and skills.

• Source control programs keep records of who changed the software and how.

• Moderators or “owners” control which of those changes are published.

• Culturally, experimentation is welcome and unrestricted.

Such projects have been started by individuals with many different interests. The operating

system Linux, for example, was started, sponsored, and organized by a student, Linus Torvalds.

Now it is a core product of firms with hundreds of millions of dollars in revenue annually. Many

other projects such as Apache and Firefox also have this form. Open source software projects

often have an explicit copyright condition to keep the core technology in the public domain.

II Motivation and psychology

The model which follows is meant to describe the airplane experimenters and also hobbyists,

hackers, and innovators of the computer age such as Steve Wozniak (developer of the Apple I

personal computer), Richard Stallman (a defining programmer of the free-software movement),

Tim Berners-Lee (inventor of the World Wide Web’s browsers and servers), and Linus Torvalds

(the founding programmer of the Linux operating system). These innovators created important

technologies without intending to sell them.

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Such innovators have various motivations. They may find a project inherently absorbing and

enjoyable. They may benefit from some service it provides. These are sometimes called intrinsic

motivations. They may anticipate receiving honors, prestige, wealth, or career benefits from the

project, which are extrinsic motivations. They may anticipate that the project could improve the

human condition apart from themselves, which is an altruistic motivation. The model to follow

directly incorporates intrinsic or altruistic motivations, and demonstrates how certain network

behaviors emerge.

Important aircraft experimenters referred to their intrinsic or altruistic motivations:

• “A desire takes possession of man. He longs to soar upward and to glide, free as the bird

. . . ” (Otto Lilienthal, 1889).

• “The glory of a great discovery or an invention which is destined to benefit humanity

[seemed] . . . dazzling. . . . Otto and I were amongst those [whom] enthusiasm seized at an

early age.” (Gustav Lilienthal, 1912 introduction).

• ”The writer’s object in preparing these articles was . . . [to ascertain] whether men might

reasonably hope eventually to fly through the air . . . [and] To save . . . effort on the part of

experimenters . . . ” (Chanute, 1894).

• “I am an enthusiast . . . as to the construction of a flying machine. I wish to avail myself of

all that is already known and then if possible add my mite to help on the future worker who

will attain final success” (from Wilbur Wright’s 1899 letter to the Smithsonian Insitution

requesting information).

• “Our experiments have been conducted entirely at our own expense. At the beginning we

had no thought of recovering what we were expending, which was not great . . . ” (Orville

Wright, 1953, p. 87).

7
The motivation of hardware hackers was often instrinsic or altruistic too. After the first

meeting of the Homebrew Club, Steve Wozniak reports (Wozniak, 2006, pp 156-7):

I started to sketch out on paper what would later come to be known as the Apple

I. . . . I did this project for a lot of reasons. For one thing, it was a project to show the

people at Homebrew that it was possible to build a very affordable computer . . . with

just a few chips. In that sense, it was a great way to show off my real talent, my talent

of coming up with clever designs, designs that were efficient and affordable. By that I

mean designs that would use the fewest components possible.

I also designed the Apple I because I wanted to give it away for free to other

people. I gave out schematics for building my computer at the next meeting I

attended.

This was my way of socializing and getting recognized. I had to build something

to show other people. And I wanted the engineers at Homebrew to build computers

for themselves . . .

Open source developers have a similar mix of motives. Lakhani and Wolf (2003) show based

on surveys that many programmers participate in open source projects because of the creative

enjoyment and the value of using the output, not explicit rewards. Pavlicek (p. 146) reports that

”Open Source people are used to doing work on a project because they perceive its value to the

community.”

It is difficult to define in output or engineering terms what the tinkerers, hobbyists, or hackers

are accomplishing in the short run. The devices or software do not work well, and they are not

clearly commensurable, because they are qualitatively different attempts to make a desirable

design. In the model to follow, progress is therefore not measured by attributes of the artifacts, but

by the individual’s own satisfaction with it, that is, in terms of utility.

III A tinkerer

Let us define an individual called a tinkerer who enjoys a technological activity A. The

notation A stands for aircraft or some other hobbyist activity such as building a computer or

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writing a computer program at home. A has no market value, and no honors or profits are

associated with it. The tinkerer may imagine that there may someday be honors or profits, but

thinks this is unlikely and assigns a low expected value to such possibilities.

The tinkerer receives a periodic flow of positive utility at directly from the existence of A in

period t. Let a0 ≥ 1 be a parameter defining the utility received in period zero, the present period,

and treat the choice about tinkering separately from all other utility decisions. The tinkerer values

alternative choices in a risk-neutral way according to the net present sum of expected future utility

payoffs. Utility to be received in future periods is discounted by a β ∈ (0, 1) for each intervening

period. Using a standard time series summation ((1 − β)(P∞

t=0

β

t

) = 1), expected utility at time

zero can be put into a closed form:

(1) EUt=0 = a0 + βa0 + β

2

a0 + · · · = a0

X∞

t=0

β

t = a0(

X∞

t=0

β

t

)(1 − β

1 − β

) = a0

1 − β

The tinkerer can choose to invest in (“tinker with”) A in order to raise future benefits at. An

investment costs one utility unit in the present period representing the effort, expenses, and the

opportunity costs of time involved. The agent anticipates that tinkering will raise his future utility

by p units in each time period in the future. The notation p stands for progress which the agent

experiences subjectively. We assume p is fixed, positive, and that the tinkerer’s forecast is correct.

A tinkerer chooses whether to tinker based on the estimated costs and benefits. The utility

benefits from one effort to tinker have the value p in each subsequent period. The discounted

payoffs to tinkering in the present period are

pβ + pβ2 + pβ3 + pβ4 + . . . =

pβ

1 − β

The investment required to receive this payoff is one utility unit at time zero so the net payoff

to tinkering in period zero is pβ

1−β − 1. Benefits exceed cost when p > 1−ββ

. For example, if

9
β = 0.95 and p = 0.07, tinkering in the current period brings a positive surplus of

.07∗.95

.05 − 1 = .33.

Unless parameters or conditions change, any tinkerer who finds it worthwhile to tinker once

will find it worthwhile to tinker again and again. As long as p > 1−β

β

, the agent will tinker in

every period, receiving payoff of a0 − 1 in the current period, a0 + p − 1 in period one, and in

general a0 + pt − 1 in period t. The associated payoff stream is

EUt=0 =

X∞

t=0

β

t

(a0 + pt − 1)

= (a0 − 1)X∞

t=0

β

t + p

X∞

t=0

β

t

t

=

a0

1 − β

−

1

1 − β

+ p

X∞

t=0

β

t

t

The last term can be expressed in closed form using this derivation:

X∞

t=0

β

t

t = β + 2β

2 + 3β3 + · · ·

=



β + β

2 + β3 + · · · 

+



β

2 + β3 + β4 + · · · 

+



β

3 + β4 + β5 + · · · 

+ · · ·

=

β

1 − β

+ β

β

1 − β

+ β

2 β

1 − β

+ β

3 β

1 − β

+ · · ·

=

β

1 − β



1 + β + β

2 + β3 + · · · 

=

β

1 − β



1

1 − β



=

β

(1 − β)

2

With that substituted in, the tinkerer’s overall expected utility at time zero is:

EUt=0 =

a0

1 − β

−

1

1 − β

+

pβ

(1 − β)

2

(2)

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The first term of equation 2 expresses the present value of expected utility from A in its

original state. The second term is the present value of the costs of endless tinkering. The third

term is the present value of the benefits expected from endless tinkering.

For a tinkerer characterized by β = 0.95 and p = 0.07, the gain in expected utility from

tinkering forever is the sum of the second and third terms:

pβ

(1 − β)

2

−

1

1 − β

=

.07 ∗ .95

(0.05)2

− 20 =

1.33

.05

− 20 = 6.6

So, for these parameters (which will be used throughout the paper to facilitate comparison),

endless tinkering increases the tinkerer’s utility by 6.6 times the cost of a one-time investment.

This self-motivated tinkerer is a perpetual innovation machine.

IV A network of tinkerers

To get to the main proposition quickly, we make simple and extreme assumptions. Later

sections relax the underlying assumptions.

Let there be two tinkerers with identical utility functions working on similar projects A1 and

A2 whose innovative tinkerings could be useful to one another. Each tinkerer believes that the

other cannot profit from the project using any foreseeable version of the existing technology. Let

the subjective rate of progress of the first player be p1, and the subjective rate of progress of

player two be p2.

Suppose the two tinkerers can make a verifiable and enforceable agreement to share a

well-defined set of the functional design changes in A1 and A2 and their experimentally

discovered effects. This agreement forms a network for future information. At any time, either

partner can depart from the network, and then ceases to share his subsequent innovations and

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ceases to learn from the other tinkerer.

Let fraction f ∈ (0, 1) of each tinkerer’s innovation be perceived as useful to the other one’s

project, so that knowing tinkerer two’s most recent innovation would benefit tinkerer one by fp2

each turn. The remaining fraction (1 − f) does not carry over because the projects are not

identical and perhaps there are costs to interacting.

If the tinkerers expect each other to produce a positive flow of innovations, they are always

better off by joining in a network. If they tinker and share with these parameters forever, tinkerer

one’s expected utility is:

(3) EU0 =

a0

1 − β

−

1

1 − β

+

p1β

(1 − β)

2

+

fp2β

(1 − β)

2

The new fourth term expresses the benefits player one receives from the flow of information

coming from player two. Because of this free good, utility is greater in equation (3) than in

equation (2). The tinkerer prefers joining a network rather than working alone. Thus under these

assumptions, rational agents generate open-source technology networks. This is the central

analytical result of this paper.

V Standardizing, specializing, and consensus redesign

Only a fraction f ∈ (0, 1) of the experimental discoveries made by player two are usable to

player one. Suppose that for cost cs, a tinkerer can adjust some elements of his project to look

more like the other one’s project, and that doing so would raise the fraction of innovations of the

other tinkerer which apply to his own project from f to f2. If tinkerer one pays this cost to

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standardize on an element of tinkerer two’s design, expected utility changes to

EU0 =

a0

1 − β

−

1

1 − β

+

p1β

(1 − β)

2

4 +

fp2β

(1 − β)

2

− cs +

(f2 − f)p2β

(1 − β)

2

Comparing this to equation 3, a tinkerer would pay the standardization cost if:

βp2(f2 − f)

(1 − β)

2

> cs

In words, a player benefits more from standardization if, holding other things constant: (a) other

tinkerers produce a large flow of innovations p2; (b) the cost of standardization, cs, is small; (c)

the increase in usable innovations (f2 − f) is large; and (d) the tinkerer is patient for results (β is

close to 1). Intuitively, these are the conditions under which it makes sense for a software

developer to replace a working piece of code by a standard library of code written by others.

The same argument can explain why experimenters tend to develop a common technical

language to describe their technologies. This can reduce communication costs and also clarify

thinking. For example, Wilbur Wright published a journal article (Wright, 1902) asking other

experimenters to cease using “angle of incidence” to mean the angle between a wing (or other

airfoil) and the ground. The better definition, he argued, was the angle between the airfoil and the

flow of air coming at it; the angle with respect to the ground was not relevant. This request was an

effort both to improve the thinking processes of other experimenters and to lower frictional losses

in communication. In a more important example, Lawrence Hargrave’s experiments showed that

a box-shaped kite was more stable than a flat kite in a gust of wind. This specialist contribution

helped glider flyers standardize on a biplane (two wing) design for gliders.

Both kinds of standardization partly explain why tinkerers would agree to publish their

findings. The fewer differences between experiments there are, the lower future communication

and adoption costs will be. It benefits player one in communication if his preferred language and

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concepts are available to both players. If a tinkerer anticipates adopting part of another’s

tinkerer’s technology at some time in the future, he lowers the future cost of that adoption by

giving the other tinkerer a chance to use his own technology now. It also means they would be

able to compare options for standardization and choose the best one, in the sense of moving the

project forward or raising f more. This incentive could be formalized by making f() a decreasing

function of a player’s own history of making new findings public. An experimenter who publishes

more makes it easier for other players to communicate with him or to learn from his design

choices. If f is a declining function of the number of findings a player has shared, it partly

substitutes for the enforcement of the rule that players should share all their findings. Each one

has an incentive to share in order to get the others to learn from his own findings, and to

standardize on his own choices (rather than having to pay the costs of standardizing on the

choices of others).

A tinkerer may invest in redesign to make the device easier to learn or easier to use, because

it represents progress p or makes it easier to exchange information, raising f. This is important in

the software context where a project can “fork” (split over time into incompatible versions used

by different people) if the contributors do not agree to standardize enough. In the history of Unix

there was a painful fork, and programmers refer to this history to convince others to keep projects

unified even as they work independently. In this model, they are willing to pay some price to

maintain the large network on the project. A redesign to achieve a consensus and avoid forking is

therefore rationalized by the flow of future exchanges that are possible if the players can avoid

forking.

For f = 0.5, f2 = 0.54, p1 = 0.07, and β = 0.95, the payoff to standardization is

p2β(f2−f)

(1−β)

2 =

.07∗.95∗.04

.05∗.05 = 1.064. In this illustration, if the cost of the standardization investment

were one utility unit, like the cost of a normal investment, it would be just worth undertaking.

Thus standardization and specialization are natural outcomes of exchanging information to

develop a technology. They can occur without any necessary reference to competition or market

14
exchange. The network is a search technology which provides the tinkerer with information he

values and does not obtain by experiment.

VI Joining, searching, or matching costs

Perhaps there are costs to finding a match partner or joining together once a match is found.

Let cj be the immediate cost in utility terms to a tinkerer for joining a sharing institution or

starting one with a known partner. The gross benefits of joining the group are again f p2β

(1−β)

2

, and if

cjis less than this, the tinkerer would prefer to join than to work alone.

So the model predicts the tinkerer joins, ceteris paribus, if (1) costs of joining, cj, are small

enough, (2) the flow of innovations from the others in the group, p2, is large enough, (3) the

innovations are relevant enough to his own project, as measured by f, and (4) the tinkerer’s

valuation of future events, β, is high enough.

The same comparison applies if cjis the cost for a tinkerer to search for a network or

candidates to join an existing network. This parameter can incorporate the real-world problem

that usually few people know a network exists and how to communicate with it. The problem is

addressed in the real world by members who write books, edit journals, make speeches, talk about

their hobby to outside people, or broadcast emails.

There might be many tinkerers, working in isolation, making almost no progress because

they do not share. Here we have a situation in which an information failure alone prevents a

Pareto-improving institution from appearing. Probably there are many situations in which

tinkerers would join a network, but the search costs are such that they do not find one another. If

one thinks of tinkerers as a natural resource, institutional attributes of the environment (like the

presence of the Internet) affect whether they can find one another and work together and their

speed of progress.

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An individual tinkerer might specialize in expanding the network, e.g. through

speech-making, book-writing or other publicity. Tinkerers who make A easier to learn or easier to

use can also lower search costs by making it easier for others to see the virtues of A.

We do see such editor/moderators in the cases of interest:

• Aircraft experimenter and author Octave Chanute had a strong interest in open sharing of

information. He expressed affection for the point of view of Lawrence Hargrave, who on

principle published all his results and patented nothing, with the idea that this open-source

approach would maximize the speed of collective progress.

• In the Homebrew computer club, Lee Felsenstein, who usually moderated the meetings,

established a ”Random Access” interaction time for people to talk to whoever could help

them.

• In the open source software cases, charismatic founders or charismatic projects draw in

interest, and the programmers are explicitly and routinely encouraged to share innovations,

sometimes by the licensing agreement.

VII Intellectual property

Some of the innovators discussed preferred to avoid formal intellectual property claims and

institutions, such as patents, which might get in the way of using a technology. Pioneering aircraft

experimenter Lawrence Hargrave and programmer Richard Stallman are examples. This behavior

can be rationalized in this model. Effort devoted to establishing intellectual property rights in an

unprofitable technology may not pay off as well as sharing which pushes the technology forward.

One can formally illustrate this. For simplicity, consider a two-tinkerer case. Assume all the

utility functions are linear in money and have been normalized to the money metric, and that none

16
of them expect to be make a commercial product. Suppose each tinkerer has property rights to his

designs and can charge a price to use the design information he transmits to the network. He may

impose a cost c1 for each information transmission on each network member who makes use of it.

With one network partner, a tinkerer receives c1 times fp1 in copyright payments, and pays out c2

times fp2. This pattern of zero-sum exchanges is profitable to the tinkerers who produce the

greatest flow of innovations, but some of the others may find it too expensive enough to stay in

the network, which slows overall progress.

More realistically, if there are many partners and frictional costs to defining, managing, or

enforcing intellectual property rights, private ownership may bring the tinkerers greater social

costs than social benefits. For both reasons, tinkerers in the model are better off and more willing

to participate in networking if the rules of the game do not include the definition and protection of

intellectual property.

That changes when commercialization to a broader market, beyond the tiny population of

tinkerers, is likely. So far the model has not considered the mixed incentives faced by a tinkerer

who anticipates selling a product some day. That tinkerer faces a perceived opportunity cost if he

does not create a barrier to competition. In one useful example, the Wrights changed their

behavior once they believed they were about to invent the airplane.

VIII Entrepreneurial exits from the network

Starting in late 1902, after they had run tests on wings in a wind tunnel, the Wrights were

decreasingly willing to share information. From Crouch (2002), p. 296:

The brothers had been among the most open members of the community prior to

this time. The essentials of their system had been freely shared with Chanute and

others. Their camp at Kitty Hawk had been thrown open to those men who they had

17
every reason to believe were their closest rivals in the search for a flying machine.

This pattern changed after fall 1902.

The major factor leading to this change was the realization that they had invented

the airplane. Before 1902 the Wrights had viewed themselves as contributors to a

body of knowledge upon which eventual success would be based. The breakthroughs

accomplished during the winter of 1901 and the demonstration of . . . success on the

dunes in 1902 had changed their attitude.

They applied for a patent in March 1903, received it in 1906, and started an airplane

business. Chanute had criticized others who kepts secrets before, and he began to have conflicts

with the Wrights. These conflicts grew severe and in the end, Chanute and the brothers were no

longer on speaking terms.

This kind of split also occurred in the Homebrew club, whose attendees had tended to follow

what Levy (2001) called the Hacker Ethic – that information should be freely available. After

Apple and other companies were founded by its members, the experience at the club changed.

Members who had started companies stopped coming, partly because keeping company secrets

would be uncomfortable at Homebrew. From Levy (2001), p. 269:

No longer was it a struggle, a learning process, to make computers. So the

pioneers of Homebrew, many of whom had switched from building computers to

manufacturing computers, had not a common bond, but competition to maintain

market share. It retarded Homebrew’s time-honored practice of sharing all

techniques, of refusing to recognize secrets, and of keeping information going in an

unencumbered flow. . . . Now, as major shareholders of companies supporting

hundreds of employees, they had secrets to keep.

“It was amazing to watch the anarchists put on a different shirt,” [former

Homebrewer] Dan Sokol later recalled. “People stopped coming. Homebrew . . . was

still anarchistic: people would ask you about the company, and you’d have to say, ‘I

can’t tell you that.’ I solved that the way other people did—I didn’t go. I didn’t want

to go and not tell people things. There would be no easy way out where you would

feel good about that. . . . ”

It no longer was essential to go to meetings. Many of the people in companies

like Apple, Processor Tech, and Cromemco were too damned busy. And the

companies themselves provided the communities around which to share information.

Apple was a good example. Steve Wozniak and his [friends and employees] Espinosa

and Wigginton, were too busy with the young firm to keep going to Homebrew.

18
In the open source software world, analogous tensions arise between programmers who think

a particular program should be freely modifiable and reusable, and those who would allow a

business or person to have intellectual property rights over it. The subject of licensing is

complicated and philosophical, but the Free Software Foundation classically defines and defends

the free software concept, and private businesses take an interest in ownership of software code,

and there are a spectrum of views regarding various programs.

VIII.A Modeling entrepreneurial exits

In each of the historical episodes, firms burst out from networks of tinkerers to create an

industry. The transition is complicated. One altered assumption can make it happen mechanically

in the model. Earlier the assumption was made that the tinkerer could not see how to implement a

marketable form of the technology. One might say that a veil blocks the tinkerer’s view of better

forms of the technology. If that veil were to lift, the tinkerer might see how to produce a product.

Substantively, the new perception or belief about making an implementable product might be

caused by advances in the technology, or changes externally, or by internal reflection. For

simplicity, in this section the probability that the veil lifts each turn is fixed, exogenous, and

known to the agent.

A generic derivation will help incorporate this into the model. Consider a one-time utility

payoff which arrives with probability π at the beginning of each future period. Denote the

unknown random period in which it arrives by s. We can compute the mean discount factor to

apply to this payoff, E[β

s

]. It is the probability-weighted average of the appropriate discount rates

19
for each possible s. The time series summation trick is used again:

E[β

s

] = X∞

t=0

P r(s == t)β

t

= 0 + πβ + π(1 − π)β

2 + · · · + π(1 − π)t−1βt + . . .

= πβ + (1 − π)πβ2 + (1 − π)

2πβ3 + . . .

= πβ[1 + (1 − π)β + (1 − π)

2β2 + . . . ]

= πβX∞

t=0

[β(1 − π)]t

=

πβ

1 − β(1 − π)

=

πβ

1 − β + βπ

Suppose at some point a tinkerer (or an entrepreneur advising the tinkerer) envisions a

directed research and development process which would result in a profitable product or service

based on project A. Suppose further that if the tinkerer were to continue to share experimental

findings universally, this would reduce the utility of the resulting monopolistic profits by more

than the utility of staying in the network, so the tinkerer wishes to drop out of the tinkerer’s

network. Dropping out means entering a new game, in exchange for losing the payoffs a0, ceasing

to tinker with A, and ceasing to receive inflows of information from the other tinkerers, but

continuing to use information from past investments and inflows.

Let M be the present utility payoff of a large monopoly profit minus the utility cost of

directed research and development, capital costs, risks, and the value of the future inflows of

information that would have come from the network of tinkerers, all computed at the instant the

tinkerer exits the network. Let π1 be the probability each turn that this tinkerer sees a opportunity

to take M, and π2 be the probability that the partner tinkerer does. Assume that these events

cannot occur in the same period. For intuition, assume these are small probabilities.

The time-zero present value of this prospective exit in unknown period s, is M discounted by

20
E[β

s

], which is M times πβ

1−β+βπ as calculated above. The utility value of tinkerering up until s is

a0

1−β − E[β

s

]

a0

1−β = (1 −

πβ

1−β+βπ ) ∗ (

a0

1−β

) = ( 1−β

1−β+βπ ) ∗ (

a0

1−β

) = a0

1−β+βπ1

The mean utility cost of tinkering each period until s, falls analogously to 1

1−β+βπ . The mean

benefits expected from tinkering each period until s fall to p1

(1−β)

2 − E[β

s

]

p1

(1−β)

2 =

p1β

(1−β)(1−β+βπ1)

.

The inflow of information from the partner is cut off if either partner exits, so s arrives with

probability (π1 + π2) each turn until the end. Putting that into the generic derivation, the capital

value of inflows from other tinkerers falls to f p2β

(1−β)(1−β+βπ1+βπ2)

. Combining these pieces, the

overall expected utility from joining the network is now

(4)

EU0 =

a0 − 1

1 − β + βπ1

+

p1β

(1 − β)(1 − β + βπ1)

+

fp2β

(1 − β)(1 − β + βπ1 + βπ2)

+

π1βM

1 − β + βπ1

The first three terms now incorporate the possibility that these streams of utility will end, and

the fourth term incorporates the new payoff of leaving the network to take payoff M.

The previous results extend forward analogously with this adjusted discounting. The net

benefit of redesigning, standardizing, or specializing to raise communication efficiency to f2

becomes p2β(f2−f)(1−π1−π2)

(1−β)(1−β+βπ1+βπ2) − cs. The net benefit of joining the network is f p2β(1−π2)(1−β)(1−β+βπ2) − cj

.

For the tinkerer to prefer to exit the network when offered M, M must be at least as great as

the right side of equation 4, since at that level the tinkerer is indifferent between taking it or

continuing in the network. For the story to hold together, the exit value parameter M must satisfy:

M ≥

a0 − 1

1 − β + βπ1

+

p1β

(1 − β)(1 − β + βπ1)

+

fp2β

(1 − β)(1 − β + βπ1 + βπ2)

+

π1βM

1 − β + βπ1

from which one can derive the minimum value of M:

M ≥

a0 − 1

1 − β

+

p1β

(1 − β)

2

+

fp2β(1 − β + βπ1)

(1 − β)

2

(1 − β + βπ1 + βπ2)

21
Using the previous parameters β = .95, a0 = 1, f = .5, f2 = .54, and p1 = p2 = .07, here is

how the payoffs change when the possibility of exits is included in a tinkerer’s forecasts:

Concept Expression Without exits With exits

(π1 = π2 = 0) (π1 = π2 = .01)

Utility cost of 1

1−β+βπ1

-20 -16.81 future investments

Present value of βp1

(1−β)(1−β+βπ1)

26.6 22.35 own future progress

Present value βf p2

(1−β)(1−β+βπ1+βπ2)

13.3 9.64 of future inflows

Present value of βp2(f2−f)

(1−β)(1−β+βπ1+βπ2)

1.064 .771 standardizing

Minimum payoff minimum M 39.9 38.07 worth exiting for

The payoffs of being in the network are thus somewhat lower if the tinkerers expect members

to exit. Still, they are positive, so tinkerers would be willing to network in the near run if the entry

price is low enough. Even if the tinkerers expect to be in competition with one another, the

network might still hold up, depending on the parameters. To include this aspect would

complicate the model and is not attempted here. It does not seem to be very important in the

historical cases under consideration. The Wright Flyer Company did not compete mainly with

others who had previously been connected to Chanute. The early Apple Computer did not

compete mainly with other Homebrew Computer Club alumni. Open source software companies

are in practice cooperating as well as competing with the same network their founders were in

before they started their company. In these empirical cases, progress is more important than

competition in the mind of the tinkerer.

There are also more differentiated outcomes in real open source software situations than the

binary choice of exiting or staying in the network which was modeled. For example, the source

code to the operating system Linux is freely available on the Internet, but companies such as Red

Hat and SuSE/Novell develop and distribute it, and offer complementary products and services.

There are a variety of licenses for open source software which keep some of the source code in

22
the public domain. These nuanced arrangements reduce the conflict inherent in the choice as it

was modeled.

The model makes explicit how tinkerers make progress before the industry starts, according

to utility maximization, not market criteria.

IX Relaxing the assumptions

IX.A Rates of progress

The assumption that each tinkerer achieves a high, steady, known rate of progress can be

relaxed in some cases, and still allow a tinkerer’s network to hold.

First, the assumption that progress occurs at a known fixed rate is stronger than necessary,

although it is a useful simplification. A more realistic description is that tinkerers see some stream

of opportunities to achieve progress as they define it. They have informed expectations about

experiments, based on their knowledge and experience. A tinkerer tries experiments, whose

outcomes are random. Tinkerers quit if dissatisfied with their progress. By self-selection, the

population of tinkerers tends to consist of those who can make effective progress, and the p in the

model is a long-run average for each member of this selected population. Modeled in this way,

the present value of utility could not be so readily computed analytically. The complexity thereby

introduced would distract from the main points of this model.

Second, the assumption that each tinkerer achieves a high enough rate of progress alone to

motivate his own efforts is not always necessary. Tinkerers could play other roles in a network.

Here are two examples:

• Suppose a tinkerer is in two networks which address different kinds of projects but

23
occasionally some idea in each one is useful to tinkerers in the other. As an information

broker transmitting these cross-cutting ideas, a tinkerer may contribute enough to maintain

membership in both networks.

• A family member or friend of a tinkerer may encourage a tinkerer, express interest in the

project, and bounce ideas back and forth with the tinkerer. This helpful person would not

need to make any specific rate of progress or pay any joining cost to in essence become part

of the network, learn about the project, and seize on useful opportunities to help the tinkerer.

the progress that the tinkerer makes. Among the aerial experimenters there were several

pairs of brothers among the aerial experimenters. At least in the case of the Wrights, the

close collaboration helped the Wrights stick with the project through bad patches in which

there was not much success. Thus other relationships can support a network’s relationships.

Thus the assumption that every individual’s progress outpaces a discount rate is not strictly

necessary. The essential assumptions are that tinkerers are interested in common projects and can

make mutually helpful progress on them according to their own judgement.

IX.B Technological uncertainty

The model assumes an agent cannot sell the technology at a profit, and cannot foresee how

such possibilities could occur in the future. This is an extreme version of technological

uncertainty, described in Tushman and Anderson (1986), Dosi (1988), and Rosenberg (1996). If

there is no technological uncertainty, and the path to a marketable design is clear, then, by perfect

foresight arguments, a profit-seeking firm would do that immediately. So if tinkerers lead the way

technologically to a profitable industry, there must have been technological uncertainty.

The model assumed that tinkerers operate under technological uncertainty and so could not

see how to make a version of A for which there would be enough demand to make a profit. In

24
casual conversation one might say that he does not see a version of A that is “good enough” to

sell, but with radically new products, both supply and demand may be hard to foresee. Several

early aircraft developers did not expect the rapid military adoption of aircraft. Early personal

computer makers dramatically underestimated demand. Tushman and Anderson (1986) used

errors in forecasts by industry analysts as a metric of technological uncertainty.

Because of this, investment and payback for tinkerers are unavoidably subjective in this

model. The experimenters do not know future forms of the output (whereas we can look back at a

well-defined “invention of the airplane”). The improvements include qualitative redesign and

“failed” experiments. A tinkerer may expect to have a better understanding of the activity after an

experiment, whether or not it improves A in functional terms, and it may benefit other tinkerers to

know about that experiment. Therefore the model incorporates subjective progress, and does not

measure progress by engineering or market attributes of A.

IX.C Scale and population size

The model has only one relationship between two tinkerers, but each can be connected to

groups or networks beyond. So the network model can scale up. As modeled, all participants

contribute information to create a positive sum interchange, potentially having positive

externalities. There is positive feedback, because fast progress makes a network more appealing

to join. Its expansion is limited because experimenters in frontier technologies are rare.

For tinkerers of a given level of interest and capability, a larger network makes faster

progress than a small one. So members have an incentive to reduce barriers to communication

within the network, or with people who might join the network. This benefit of scale implies that

networks with fewer barriers will address technical problems better or more quickly than

networks with more barriers. For example, the use of English to discuss an open source project

may improve the speed of development if more potential programmers can participate in English.

25
This suggests that, holding other things constant, open source innovation will tend to be more

successful when tinkerers communicate in a language many people know, and in locations with

less restriction on printing or association with other people. If there are many tinkerers, the

network will probably have greater internal friction, and require administrative structures of

information sharing (as in open source software), which are not modeled here.

IX.D Motivation

The tinkerer is imagined to have intrinsic or altruistic motivations. But a network can form in

support of a profit-making or career effort of the tinkerer too, as long as the other tinkerers do not

see it as a zero-sum competition. So for example a government laboratory’s programmers may

contribute to an open source software development effort if it is useful to their project, or a

cost-reducing effort could be co-sponsored by competing firms.

A tinkerer’s motivation could include not only the possible honor of making a major

invention, but also the possible second-best prize of being recognized and cited by the final

inventor. Such streams of payoffs can be viewed as a portion of a rate of progress p. The model

also excludes any payoffs experienced in sharing what one has done with others, although

innovators report the opportunity to share is beneficial and satisfying.

In the examples that motivate this model, the tinkerers do not yet know much about how to

make a good A because the technology cannot yet be usefully implemented. The number of

tinkerers who can make experimental progress on a particular type of project is limited to those

with the knowledge, wealth, and tools to attempt it. Many people could value experimenting with

new aircraft, but few like it enough, and are good enough at it, and have the resources, to bother.

Those few have opportunities to make something that looks like progress to them. One might

imagine that values of a0, the original payoff of activity A, were drawn from a distribution, and

the few people with a sufficiently positive value for a0 would be tinkerers. In the aircraft case,

26
even successful experimenters considered quitting, and many did.

Once a technology is established and competitively produced, the basic uncertainties have

been resolved, so the model loses relevance. Today, there is an established spectrum of

technologies for making aircraft and personal computers and delivering services to and from

them. Technological uncertainty still exists within narrower domains that could be relevant for the

model.

IX.E Enforceability

In the model, tinkerers would be willing to agree to an enforced open-source contract rather

than work alone. But in the case of gliders leading to the airplane, there was no exogenous

enforcement. There are several sources of incentive besides technological progress to support an

open-source pattern which were not modeled here.

• Innovators may feel an obligation to share with the group, so enforcement is internal to

each tinkerer. Meyer (2003) discusses some examples.

• Innovators may want their peers to see their work because they are proud of it and will be

favorably recognized for it, as discussed in Raymond (2001) and Levy (2001). Unlike a0 in

the model, this payoff directly supports open-source relationships, and does not depend on

an information flow back from the other person.

• If an invention delivers more output when it has more users, inventors may benefit more by

giving it away than by keeping it secret or charging a fee to examine or use it. For example,

since Web browsers were invented, many have been given away to make collaboration and

information tracking easier (Berners-Lee, 1999).

• In the model of Bessen and Maskin (2006), profit-making firms are willing to share

innovation information openly with one another if they are following different paths of

27
research or if the innovations they expect to make will be useful to achieving future ones.

For tinkerers, one might model this by raising the rate of progress each tinkerer expects if

more sources of information are available.

• Tinkerers may gain more from interaction if they are familiar with one another’s work.

Adapting this model, f could rise over time as the network’s members develop longer

histories together.

• To a tinkerer who anticipates someday selling a product or service, the population of

tinkerers inside the network may be the natural market of early adopters for it. Interaction

with others helps the tinkerer know what customers will want, and creates an opportunity to

earn their trust. At first only specialists could understand and appreciate the aircraft,

personal computers, and new types of software discussed earlier.

Given incentives like these, sustaining the agreement can be rational for each individual

participant. The enforceable contract in this paper is a modeling shortcut. More accurate stories

may require a more complex model.

IX.F Frictions, diseconomies of scale, or other costs

As written, the model incorporates the extreme assumption that there are no economies or

diseconomies of sharing with more and more people. For example, it is implicitly assumed that

there is no time constraint in keeping up with the relevant literature, nor cost for communicating

to yet another person, nor changing marginal cost to enforce the sharing agreement. One could

incorporate such influences by making f a function of the number of participants to generate

increasing returns to scale (encouraging evangelism) or decreasing returns to scale (inducing

pressure to reduce or exclude members).

Many aspects of the environment affect f. If, for example, communication between the

28
tinkerers are noisy or clogged with unhelpful communication, such as email spam, f is lower. If

the languages of technical communications are different, f is lower. An American experimenter

working on gliders may naturally choose not to read a French journal about balloon developments,

even if the balloon work is productive in its own terms (measured by p2), either because he cannot

read French, or because he thinks balloon innovations are unlikely to apply to gliders.

The model implies there are no expensive capital or training requirements. In the examples,

tinkering was not usually capital-intensive. It appears that once expensive equipment is necessary

for some activity, that activity falls beyond the boundaries of a network of tinkerers, except

perhaps inside an organization. Also, tinkering tends to arise in environments where not much

specialized training is required.

X Conclusion

A network of tinkerers model applies to innovative processes in which:

• Individuals communicate novel technical findings and designs about a technology to one

another without explicit compensation.

• Experimenters do not have extrinsic rewards, largely because they are working on

something that has no obvious price or does not fit into an existing, standard product market

at the time they enter the field.

• Some participants specialize in managing or expanding the network.

• The activity evolves over time, in response to events that participants interpret as progress,

such as discoveries or inventions. For example, when Hargrave reported results from his

box kite experiments, other aeronautical experimenters learned and adapted to the findings

29
without imitating his experiments. Thus they behaved as if they were responding to a

discovery of a natural law or invention, not performing for others or engaging in a sport.

Given such a situation, the model predicts that participants specialize in aspects of the

technology, and standardize on some tools, as opportunities permit. The framework assumes

predictions about the future form or importance of the technology are diverse and uncertain in the

sense of Dosi (1988) and Rosenberg (1996). It predicts that members who do not plan to sell a

related product quickly will object to intellectual property impositions. And it predicts the kind of

ferment which can lead participants to jump out into entrepreneurial ventures, whose value is

difficult to predict in the sense used by Tushman and Anderson (1986). The model predicts

progress would be slowed by high costs to invest in experimentation or to find and join networks.

Examples of this process occurred in the invention of the airplane, the invention of the

personal computer, and in the development of open source softare programs. There is a spectrum

of similar kinds of innovative development:

• Shared creative content can be like open-source software. The Wikipedia, for example, is a

public domain encyclopedia written principally by unpaid users. The collection of video

content at YouTube.com is donated by users. In these cases the pooled content is not made

up of functional engineering achievements. Instead the developers are sharing text,

reasoning, and media. The library grows with contributions from many users to advance in

a direction they more or less agree upon. Such shared content is similar to open source

software.

• Open science. The rise of “open science” institutions which motivate, support, and enforce

open publication of scientific findings, was supported by competition between patrons to

employ prestigious and effective scientific innovators (David, 1998). Through the network

of tinkerers story, open science would also be supported by people who wish to speed the

progress of science.

30
• User innovation, in which a company produces a product and its users generate user

innovations, as defined and discussed in von Hippel (2006), which also provides many

examples.

• “Skunkworks”, projects of creative engineering inside corporations in which engineers

work around an employer’s hierarchy, rather than obeying it. Their goal may be for the

organization to succeed despite its managers. The model offers a way to think of such

actors less as shirking, and more like visionaries, as they think of themselves.

• The British Industrial Revolution after 1750 occurred in a place and time when printers

were allowed more freedom about what to print than printers elsewhere. This helped

support an estimated 1020 technical and scientific societies (Inkster, 1991, pp 71-79).

Workshops of the time were often open to visitors, and they generated, by one account, ”a

wave of gadgets [which] swept [over] Britain” (Mokyr, 1993, p. 16, citing Ashton). Mokyr

(1993, p. 33) concluded: “The key to British technological success was that it had a

comparative advantage in microinventions.”

• The Internet and the Web expand the opportunities for technological discussion.

This model provides a formal structure to describe developers of radically new technology.

Some innovators make fast progress on their own, although it may not look like progress to other

people. Others make sufficient progress to perservere, if they have exogenous links to one

another. Independent innovators have an incentive to join networks and share flows of

information. An innovator with comparative advantages in recruitment, publicity, moderating

conversation, publishing, or editing journals may end up doing those things because that moves

the project forward faster than if this person worked directly on the technology. Much of this can

be understood and discussed in terms of progress rates p, fractional flows of useful information f,

and differentiated opportunities for each person. Players may imagine profitable future exit

opportunities but if these are remote or improbable enough, joining the network makes sense until

31
participants actually see those opportunities.

Tinkerers in the model choose to combine their information to maximize the combined flow

of useful innovations. The speedup in the flow of innovations is therefore endogenous. In the

model, purposeful choice generates flows of innovation that other economic models of

technological change often take as given. When technological uncertainty is great, tinkerers

networks can eventually form an industry, once enough tinkerers can anticipate commercial

possibilities from their activities.

The desire of people to make their world a better place is a kind of natural resource. The

environment affects their effectiveness. If publishing a journal, forming an association, and

traveling are costly or officially discouraged, innovators facing technological uncertainty would

be less effective. In this model that would reduce their utility. In the real world they might

respond by reducing effort, keeping innovations secret, or emigrating to a location where the

environment was more favorable. So noisy or restricted communications channels can reduce the

flow of innovations both by reducing the flow of communication, and by driving tinkerers away.

In this model, innovation is generated by individuals not organizations. One benefit of

modeling innovation this way is that the predictions and intuitions often apply outside the context

of businesses and hierarchies, as in production prior to capitalism, or communications inside or

between organizations.

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