BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR 

Bureau of Labor Statistics 

OFFICE OF PRODUCTIVITY AND TECHNOLOGY 

 

Episodes of Collective Invention 

 

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

 Working Paper 368

 August 2003 

 

Results and conclusions in this exploratory research are the author’s and are not official findings of the U.S. Bureau of 

Labor Statistics.


	





by Peter B. Meyer 

meyer_p@bls.gov 

U.S. Bureau of Labor Statistics 

Office of Productivity and Technology 

4 August 20031

Abstract: 

The process of developing a new technology through open discussion has 

been called collective invention. This paper documents two episodes of 

collective invention and proposes a general model based on search theory. 

The first episode deals with the development of mass production steel in the 

U.S. (1866-1885), and the second with early personal computers (1975-

1985). In both cases technical people openly discussed and sometimes 

shared technology they were developing. Both technologies advanced to the 

point that they supported substantial economic growth. Open source software 

development is partway through a similar process now. 

The episodes have common features. The process begins with an invention 

or a change in legal restrictions. Hobbyists and startup firms experiment with 

practical methods of production and share their results through a social 

network. The members of the network form a new industry or change an 

existing one. The network then disappears if the new firms keep their 

research and development secret. A model of the search for innovations can 

describe this process if it is expanded to include independent hobbyists and 

consultants as well as profit-seeking firms. 



1

 Thanks to Alessandro Nuvolari, John W. Meyer, Leo Sveikauskas, Thomas Maloney, Michael Giandrea, 

Joel Mokyr, Cindy Zoghi, Mike Harper, Marilyn Manser, Aklilu Zegeye, Anastasiya Osborne, and 

participants at the 2002 Social Science History Association meetings for their comments. Further 

comments are welcome; please address them to meyer_p@bls.gov.1. Introduction 

Technological advances are often kept secret or patented, making them the 

intellectual property of their inventors. Scientific advances are more often published 

openly. One reason for the difference is that scientific investigation is driven so much by 

curiosity, whereas technological investigation is clearly functional, driven by the goal of 

producing something and usually to earn a profit. 

There are important exceptions. For example, the open, public character of open 

source software development2

 is unlike the privately-owned structures usually associated 

with technological development. This paper outlines historical episodes analogous to the 

open source phenomenon, and makes generalizations about them to build a theory of 

what happened. In particular it discusses the institutions that supported the open sharing 

of technological information in early U.S. steel production between 1867 and 1881, and 

those helping early microcomputer development from 1975 to 1980. These two 

examples are compared to three other cases which have been called collective invention. 

To think through a general theory it helps to have several cases of the phenomenon to be 

explained. With five cases, this paper outlines a generalized account of this phenomenon. 

The paper will make a useful contribution if it persuades readers that collective invention 

is a recurring phenomenon, that we know of several cases, and that it is especially likely 

and important when new technologies offer great opportunity. 

Sharing can help move a technology forward into applications quickly because many 

developers benefit from one another’s experiments. In each episode, the participating 

individuals have diverse motivations, but the cluster of reasons to experiment and share 

results is the same. In each case, key innovators were excited by the opportunity to do 

something new, were absorbed in technical challenges, hoped to do good for the world, 

hoped to become rich, and hoped to earn prestige and respect. Some of these descriptions 

are remarkably similar whether the individual was an early experimenter in mass 

production steel, steam engines, microcomputers, or open source software.3

 The 



2

 Software source code is the human-readable text which is translated automatically into a program of 

instructions the computer can run directly. Common source languages for this are C, C++, Java, Pascal, 

BASIC, and FORTRAN. Free software development or open-source software development is an approach 

in which the source code is made widely available on a computer network and its use, modification, and 

redistribution by others is welcome. Licenses vary; for examples see Pavlicek (2000) or 

http://opensource.org. The culture of open-source development is distinctive, differing importantly from 

corporate research and development, as discussed for example in Pavlicek (2000) and in the social science 

papers at http://opensource.mit.edu. Proprietary software producers typically hold the source code as a 

trade secret, and as private property, and distribute only an executable (runnable) form of the program. 

3

 There are many examples in the superb Hackers (Levy 1984), Freiberger and Swaine (1984), Cringely 

(1996), and McHugh (1980). Economists thinking formally about income and wealth as motivations, 

sometimes treat the experimenters behavior as idiosyncratic and surprising, but it is clear in the descriptions 

that the behavior is common, recurring, and can be understood by natural impulses to (a) do things that are 

interesting or fun, (b) earn prestige or respect, (c) try to get rich, and (d) do good for others. The subject of 

this paper is not why do they experiment but why do they publish their results? What problem does sharing 

help them solve? I argue that it looks like a search-for-innovations problem.


institutions through which the experimenters share their results differ among these cases. 

Such institutions include journals, clubs, consultancies, and job turnover. 

The discussion below describes these institutions to show the common aspects among 

them and to develop a theory of the environments in which they appear. 

1.1 The idea of collective invention 

As societies have adapted to waves of new technology there have been phases where 

technological information was openly and enthusiastically shared. We will look here at 

certain cases where technological developments were published openly to some 

community, and we consider why it happened and what institutions made it work. 

Robert C. Allen (1983, p. 2) used the term collective invention to describe “the free 

exchange of information about new techniques and plant designs among firms in an 

industry.” Allen saw that this had happened among iron producers in Britain’s 

Cleveland district. Richard Nelson (1982, p. 468) encouraged research into the subject. 

Schrader (1991) and von Hippel (1987) documented explicit, informal “know-how 

trading” among mini-mill steel makers in the U.S. in the 1980s. Nuvolari (2002) showed 

that steam engine engineers in Cornwall maintained a collective invention approach.

Harhoff, Henkel, and von Hippel (2002) collected several recent examples of “freely 

revealed” innovations in recent years and modeled a game between innovators in which 

revealing could be an optimal choice by an innovator because it helps diffuse the 

technology. A number of investigators have described open-source software projects as 

examples of collective invention; relevant papers are at the web site opensource.mit.edu. 

One might take the view that a technology is truly invented only once, and 

subsequent applications of it are innovations which are part of a separate process of 

diffusion. If so, collective invention is not the right term – collective innovation or user 

innovation would be better. But in the cases we will consider, the first versions of the 

technology are not applied for long. The invention as generally perceived afterward 

includes the many improvements from the early discussion. In these cases the form of 

the technology that turns out to matter is the one slowly invented over the course of 

years, so for this paper’s purposes we will say it was collectively invented. 

There are several differences between collective invention environments and those 

depending on legal enforcement through patents. A patent system tolerates bogus 

submissions or unclearly described inventions, since an inventor benefits from users who 

pay for a license or are otherwise dependent. On the other hand, in a collective 

information environment, the innovator does not have an incentive to hide essential 

details. Patenting environments encourage the inventor to invest in and develop 

inventions, whereas collective information environments do not give an inventor a direct 

financial incentive to develop it further. Either environment could give innovators the 

satisfaction of seeing their innovations widely used. Which one will generate more 


technological advance depends on context. Below I argue that when the technology’s 

future is uncertain, collective invention does better at advancing the technology. 

As we shall see, restricting the technical community to for-profit firms makes it hard 

to use the term in other episodes. Therefore let us enlarge the definition for the purposes 

of historical comparison: 

• Include hobbyists and workers in not-for-profit organizations such as universities 

and government agencies in the community of technologists. 

• Include job changes by people working with the technology as one of the 

mechanisms of interchange of technical information. 

• Include cases in which collective invention practices co-exist with some firms 

who do secret research and development. Even if some firms keep their findings 

secret, collective invention may still occur in some subset of an industry. 

 

So collective invention is defined here to be a process in which improvements or 

experimental findings about a production process or tool are regularly shared. Put this 

way, collective invention seems to be an important and regular feature of the historical 

process by which societies adapt to radically new technologies. It is part of a larger 

picture in which the new technology turns into new products and the producers, 

consumers, and markets are jointly developed. 

Tushman and Anderson (1986) called the adjustment phase a “period of ferment,” 

describing the long period after an invention but before a substantial industry has 

stabilized around it. For example, the Bessemer steel-making process was first tried in 

the U.S. in 1858 but it took twenty years before there was there a strong industry built 

around it. Hobbyists made microcomputers as early as 1975, but it took until the 1990s 

before national productivity statistics were clearly affected. Open-source software by 

various names has existed since the 1960s but companies built around it appeared first in 

the 1990s. In each of these cases, the intervening period included an open, documented 

technical discussion. It could be that an open phase of this kind is necessary for adapting 

new technologies to useful purposes. Perhaps without the sharing phase, these 

technologies would not be developed to the point that they were beneficial to the general 

population. 

2.0 Three established cases of collective invention 

This section summarizes three large scale cases of collective invention documented 

by other authors. 

2.1 Blast furnaces in Britain’s Cleveland district 

Allen (1983) found that from the 1850s through the 1870s iron-making companies in 

northeast England’s Cleveland district allowed visitors and consultants to see the insides 

of plants and to write about the way their blast furnaces made usable iron from ore. Wellknown researchers at the time such as Isaac Lowthian Bell, Thomas Whitwell, and J.G. 


Beckton published information about the designs, size, temperature, and contents of blast 

furnaces. The information came from observing production, not from formal research 

efforts. Publications and well-informed consultants helped establish which blast furnace 

designs used fuel most efficiently. Plant designs evolved to have taller furnace stacks 

(filled with the input materials) and toward hotter and hotter furnaces. The design 

changes were not generally patentable because of the nature of the technology. 

Since plant design was a natural area of competition one might have thought the 

owners of blast furnaces would each prefer to keep this information secret. But Allen 

concluded that through the sharing process firms could reasonably expect to learn more 

valuable information than they gave up, and therefore each firm preferred such 

information be made public over taking the risk of shutting it down by withdrawing. The 

collective invention regime substituted for research and development spending. Furnace 

efficiency improved over time. Allen found that little of this improvement was caused by 

private research and development efforts. He attributed most of the productivity 

improvement in blast furnace practice over time to the sharing of information which he 

called collective invention. 

2.2 Steam engines discussed in Lean’s Engine Reporter (1811-1904) 

Steam engines had been around since 1712 but in 1769 James Watt patented a new, 

much more efficient design for them. Despite legal attacks on his patent, it was upheld 

until 1800. Mine owners in the Cornwall region of England used steam engines to pump 

water out of mines, sometimes using illegal copies of Watt’s design. Mine owners and 

steam engine makers resented Watt’s unwillingness to license the invention cheaply. 

After Watt’s patent expired they could legally make modifications to the design. There 

was an explicit debate on alternative forms of intellectual property rights among the 

steam engine engineers. Few actually filed patents. Rather, there was a collective 

invention environment, as shown by Nuvolari (2002). 

Starting in 1811 there was a publication read by the Cornish steam engine makers, 

called Lean’s Engine Reporter for its editor, Joel Lean. Its contents were technical 

comparisons of operating steam engines. Nuvolari (2001) establishes that the efficiency 

of steam engines improved substantially in Cornwall through this period, probably 

through many minor or unattributed innovations and discoveries by the steam engine 

engineers. Collective invention sustained by the Reporter thus supported useful 

engineering improvements. 

2.3 Open source software development since the 1980s 

A contemporary example of collective invention is open source software 

development. In such a project the human-readable source code files are published on a 

computer network. Source code files, usually written in a standard computer language, 


are given to specialized development tool programs, such as compilers, assemblers, 

interpreters, and linkers, which generate the machine-readable executable program. 

Sharing the source code is useful insofar as it makes possible ongoing improvements 

by many programmers. Users may alter the program for their specific purposes. 

Sponsors of open source projects usually copyright the software in such a way that other 

developers cannot copyright programs using the open source code. 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 moderators of changes in a chunk of source code, also called its owners, 

determine the final choices in released versions of the software. Users may make a 

version different from the released, certified one. One criterion of a moderator’s success 

is whether the moderator can avoid the project’s source code “forking” into permanently 

divergent, partly-incompatible versions. If that happens, the mutual benefits of having 

one standard which improves over time are partly lost. 

Several roles and institutions support collective invention in open source projects: 

• Web servers are a venue for storing and distributing the technology. 

• Special copyrights apply to most open-source projects, and intellectual 

property issues are confronted explicitly.4

 

• The relevant programmers have similar development tools and the skills 

needed to use them. 

• Source control programs track which programmer is changing the software. 

• Moderators decide and control which of those changes stay in the source code. 

• Culturally, experimentation is welcome, and the developers are not scheduled 

or in other ways restricted from experimenting. This is one way that open 

source projects differ from many corporate projects. 

Collective invention is easier in open-source software than in the earlier episodes. 

Software technology is quicker and easier to transfer over a network between participants 

than were the other technologies which were shared through documents. Also, shared 

source code is relatively easy to convert into a usable program, whereas a blueprint or 

description of a blast furnace cannot automatically generate a plant. In software, the 

content under discussion is the technology itself, not descriptions of it. 

These three accounts, of blast furnaces, steam engines, and open source software, 

were drawn from an established literature. The next sections discuss two additional 

cases. 



4

 Pavlicek (2000) and opensource.org have examples of these special licenses to which a user must agree to 

legally use the source code. In the U.S. source code files are automatically copyrighted at the time they are 

first written, in the absence of specific rules otherwise. 


3.0 Bessemer steel in the U.S. (1866-1885 and beyond)

British inventor Henry Bessemer announced a new steel making process in 1856. He 

correctly foresaw it would be a quick, high volume, fuel efficient approach and trod 

lightly on the fact that it hadn’t actually worked yet. After some further innovations with 

others, it developed into a thriving business in the 1860s. It took longer to transplant the 

technology to the U.S., which took another wave of innovations. 

Several institutions served to aid collective invention in the U.S. technology. First, 

several new industrial journals and organizations arose during this period when mass 

production steel technologies were being adopted. The open-discussion environment in 

the British iron and steel industry may have been a model for its extension in the U.S., 

leading to collective invention on both sides of the Atlantic. Second, famous engineer 

Alexander Holley ran a consulting practice which pooled the key patents for Bessemer 

steel manufacture so members of one licensing organization would have access to them 

all. Holley himself designed most of the first fifteen Bessemer steel plants though they 

had different owners. Third, job turnover was high in the industry, so many employees 

had diverse experiences drawn from previous employers. 

Huge demand for steel rails for railroad construction sustained the industry through a 

depression that started in 1873. Production quantities rose dramatically, as shown in 

Figure 1. The technologies in use improved quickly, and the price of Bessemer steel fell 

from over $100 per gross ton of rails in 1870 to about $60 in 1880. Below we consider 

the institutions that enabled this collective invention. 

Figure 1. U.S. production of steel 

Bessemer and acid open-hearth were new methods of steel production introduced in the late 

1860s. Another method, called basic open hearth, was introduced in 1878. Steel production 

grew dramatically over the period. The data is from Historical Statistics, p. 694.

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

1867186918711873187518771879188118831885

Year

Short tons

B essem er steel

O pen-hearth steel

C ru c ib le s te e l
	

3.1 New journals and organizations

Metallurgical knowledge in U.S. industry was very imperfect in 1865. Standard 

textbooks on iron metallurgy in the U.S. had errors (Gordon, 1996, pp. 214-219, 297-8, 

and 314-316). It was not fully understood that the smelting process by which iron ore 

was converted to usable iron involved not only melting the iron but also chemical 

transformations which burned the carbon away. Iron and steel managers did not 

generally acknowledge the value of having chemists researching the subject, particularly 

in the U.S. By 1880, the situation had changed permanently. Standard textbooks had 

improved greatly and the subject of iron metallurgy was better divided into what was in 

fact known, and what was not understood but under study. 

These improvements in knowledge in the industry occurred partly because of active 

investigation by professionals into iron and steel making, great demand from the railroads 

for iron products, and a set of iron and steel-making methods that was more diverse than 

before or afterward. Several professional associations were formed. Active discussion 

about metallurgy was documented in their journals. Institutionalizing innovation and 

communication in these ways could have helped change interaction to make it look more 

scientific rather than craft-oriented or industrial. Thus these journals and associations 

supported collective invention. 

In Britain at the same time, the Iron and Steel Institute was formed and began to 

publish its periodical, the Journal of the Iron and Steel Institute, in 1869. It is possible 

that this publication inspired the American parallels, but it is more likely that the 

Americans were responding to a similar challenge, which was the need to understand and 

develop iron and steel technology better. Because of the new Bessemer, open hearth, and 

other processes there was more diversity in iron and steel production techniques than 

previously. The diversity later declined in a kind of shakeout. 

The American Institute of Mining Engineers began in 1871 to publish the 

Transactions of the American Institute of Mining Engineers (TAIME). This journal was 

devoted principally to metallurgical subjects, especially iron and steel. Membership grew 

rapidly. The Transactions listed its members, about 700 in 1878 and about 1000 in 1880. 

To understand the nature of the publication, I reviewed the parts of 1878-1880 issues that 

discussed iron metallurgy and industrial processes. 

The contents of the Transactions were technical, as opposed to discussing the affairs 

of the Institute, industrial personnel, or business matters. Readers were assumed to have 

a serious interest in the subject, and rarely did articles have an introduction or conclusion 

designed to draw in a passing reader. A number of the papers were presented at Institute 

conferences. Commentaries by discussants in the Transactions contained frank 

disagreements, politely but sharply written. The publication includes articles by directors 

of research, chemists, superintendents, and engineers. Membership included both PhDs 

(identified as such in the journal) and practical engineers who had not finished secondary 



school.5

 The text was formally written and readers were presumed to have some 

knowledge about the production and use of iron materials and rails, but not much of 

chemistry. Writers referred frequently to numerical evidence and to their experience. 

Many of the writers were well known figures in the industry rather than scientific 

specialists. The articles tended to be very empirical and specific about details of 

measurement. 

Many articles had graphs or tables of data. Occasionally they had diagrams of plant 

or equipment. Some of the diagrams were so large that the reader had to unfold a page 

many times to see it completely. Other articles had a number of chemical formulas or 

mathematical equations. Diverse sources of specialized knowledge were welcome, 

clearly including input from steel users and customers. 

Here are a few short examples of articles in the TAIME in the 1878-1880 period, and 

one extended example. 

• There was a discussion of what technical training apprentices in technical professions 

should receive. Comparisons were made to the practices in Germany. 

• A 94-page glossary of mining and metallurgical terms was published. 

• The superintendent of a steel smelting department described specific improvements 

his firm had made to the ladles that held molten iron or steel for casting.6

 A 

discussant identified one that seemed to be novel and important. Apparently it was 

not patented. 

• On the basis of blast furnace evidence a professor showed evidence supporting a 

theory of the heat generated by combustion as a function of carbon and oxygen inputs 

and rejecting a widely held alternative theory.7

• Well-known engineer Alexander Holley described a new machine at the U.S. Arsenal 

in Watertown, Massachusetts which could test metal parts, and advocated that his 

fellow engineers try to persuade Congress to fund its availability to make machinery 

better and safer.8 His text took on some energy: “Machinery in vessels, on railways, 

in the floors of great factories and theatres plunge down among broken columns, 

torturing and killing men and women in their debris. Is it not probable that the tenth 

part of the money damages paid for these disasters, if expended in the means of 

prevention indicated . . . would very largely reduce this record of bankruptcy and 

death?” By its rules the AIME could not take a stance on a political question so 

Holley asked his colleagues to act as individuals and as representatives of companies. 



 For example, John Fritz, a noted inventor and the superintendent of one of the largest steel firms, had 

worked as a blacksmith’s apprentice starting at age sixteen. Engineer and inventor Captain Bill Jones 

published in the journal although he had apprenticed to an ironworks at the age of ten. These men seemed 

never to have returned to school (McHugh, p. 219 and p. 235). 

6

 Herrick, J.A. “Improvements in the appliances for venting molten steel or iron from a casting-ladle or 

shoe.” TAIME, 1878, pp. 13-15. 

7

 Church, John A. “The mode of combustion in the blast-furnace hearth.” TAIME, 1878, pp 33-44 

8

 “The United States testing machine at Watertown Arsenal”, TAIME, 1878, p. 261.


A kind of technical community had formed, with some warmth, like a freewheeling 

email discussion in the open source software context. 

One particular set of articles is especially relevant to the developments in iron and 

steel. Pennsylvania Railroad’s research director Charles B. Dudley wrote a lengthy paper 

on the subject of how impurities affected the survival of steel rails. The paper and 

comments by others were printed in the TAIME journal. The subject drew a dozen 

further commentaries in a later issue, and was central to the growing success of 

steelmakers in feeding the demands of the growing railroads. The superintendent of a 

prestigious and successful plant, Robert W. Hunt, wrote to say Dudley’s sample was too 

small and to dispute specifics of Dudley’s work with his own numerical support, and 

gently impugning the value of a laboratory contribution to an industry with practical 

experts such as himself. Well-known engineer W.R. Jones, of a competing plant, 

supported Dudley with certain reservations. Jones compared Dudley’s findings to those of 

French metallurgists. He expressed the concern that American steelmakers were too 

secretive and shared what he thought was the best formula. A Columbia University 

professor deemphasized the importance of chemistry, which Dudley had studied, 

compared to the physical and mechanical microstructure of a rail in determining its 

robustness. This point of view was justified by later developments, according to Gordon 

(1996). Holley commended Dudley but recommended testing a sample a hundred times 

larger, and made a comparison to the practices of European rail makers. Another steel 

works engineer had another small sample of steel rails analyzed by three different 

chemists and submitted the results in his commentary. Another expert ran a least-squares 

regression, apparently by hand, on Dudley’s data. There were 23 observations of three 

measures of reliability (the dependent variables) on the quantities of silicon, manganese, 

carbon, and phosphorus (the independent variables). A subsequent commentary 

criticized any analysis that did not take quantities of sulfur and copper into account. 

Several other professors and professionals wrote in with varying sources of authority. 

The discussion took over 130 pages.9

It is useful to note the diverse backgrounds of the participants, the diverse sources of 

information, and the process of this particular discussion. Participants included obscure 

experts as well as well-known ones. Attention was focused firmly on the problem of 

what made good steel railroads. The seriousness and enthusiasm of the participants 

suggests that they were making serious efforts to win the respect of one another through 

this process. A reader of this discussion can expect that the discussion would not get 

mired forever in detail. Through the open discussion, empirical findings would become 

established in a few years and a consensus on the facts or a design standard would 

emerge. 

Other professional associations related to the developments in iron and steel appeared 

in the 1865-1880 period. In the late 1860s a professional association of civil engineers 

began. Alexander Holley co-founded the professional association of mechanical 

engineers in the 1870s. Its journal, the Transactions of the American Society of 



9

 “Discussion on steel rails,” TAIME, 1879, pp 357-413 and 529-608. The series of articles reads like a 

thread on a Usenet bulletin board, but the phrasing is more formal. 


Mechanical Engineers, began publication in 1879. And in 1880 the Journal of the United 

States Association of Charcoal Iron Workers began. Each of these had discussions about 

iron and steel materials, equipment, and processing. 

3.2 The Bessemer patent pool and Holley’s consulting practice 

Litigation over the scope and definition of key patents held by inventors William 

Kelly, Henry Bessemer, and Robert F. Mushet delayed the development of Bessemer 

steel in the U.S. until after it was an established technology in Britain. A series of 

agreements beginning in 1866 defined a “patent pool” arrangement in which licenses to 

key U.S. patents were held in common by the Bessemer Association which paid fees 

through to the inventors and existing licensees. For $5000, a firm could join the 

Association and not have to worry about specific patent rights. This was a small fraction 

of the setup costs of a plant (at least $80,000) and was therefore not prohibitive. In 

practice member firms also licensed their own patents on improvements to the 

Association and thus its other members. American engineer Alexander Holley, a 

principal organizer of this patent pool, patented ten improvements in Bessemer plant 

design and process over the next fifteen years and made them available through the 

association. Holley published technical reports through the Association about approaches 

and experiments in applying these technologies. The presence of a newsletter did not 

mean it was easy to keep up, because much of essential knowledge was tacit, requiring 

experience with the new technology itself. 

Holley’s reports were intended for licensees only, but they may have been more 

widely available since the existing licensees would receive revenue from any new 

licensee. Holley was asked to join many projects. His role as an inspirational figure was 

analogous to some highly visible editors of early scientific journals. For example, 

entrepreneurial editor Frederick Oldenbourg personally financed the first major English 

scientific periodical Philosophical Transactions for many years starting in 1665 

(Kronick, 1976). Holley also visited experts at their own plants, sometimes without 

warning, when he was thinking about a hard problem (McHugh, p. 230, note 20). 

Holley’s reports were brief and technical, and explicitly intended for his clients, not 

for widespread publication. One (Holley, 1875) described the steel works plant of the 

British firm Brown, Bayley, and Dixon. It began: "I will . . . confine my remarks to those 

features of machinery and practice which we may advantageously copy or avoid." The 

18-page document was functional and instrumental, densely packed with descriptions of 

structures and equipment. Scores of measurements, percentages, weights, and costs for 

materials were listed. Holley thought highly of some aspects of the plant design: “The 

arrangement for removing slag, etc., from the pit is the best I have seen anywhere” (p. 4). 

Holley wrote that the pressure pump was troublesome, like other English pumps, and that 

with respect to the steel-making process: “There is no novelty whatever in the process as 

conducted here. Spectrum analysis is never used” (p. 12). It is not clear what Holley 

exchanged to the firm to get permission to see their plant, but perhaps he shared previous 

reports with them or provided consulting services. 


 In 1877, the eleven surviving Bessemer steelmaking firms in the U.S. combined 

patents to form a joint subsidiary, and ceased to advertise licenses for the remaining 

Bessemer patents.10 New entrants did eventually appear, but they had to pay higher 

royalties than the existing ones. Prices for steel and for rails continued to fall despite 

output quotas set by the rail pool because production costs fell and new sources of raw 

materials were discovered. 

This is a case in which institutions of collective invention – Holley’s reports and 

consulting practice – were shut down by an increasingly oligopolistic industry. In 1881, 

Andrew Carnegie’s firm began acquisitions which resulted in the 1900 merger that 

created the giant firm U.S. Steel. 

3.3 Job turnover 

In the 1870s, iron and steel managers and technical experts often changed employers. 

Almost any experienced worker had worked somewhere else earlier (McHugh, 1980). 

Employers seem to have treated this as acceptable and usually remained on friendly terms 

with their former staff. Those who did not leave knew other engineers outside the firm. 

Indeed “the five or six top engineers of the industry [met frequently] to discuss common 

problems” (Temin, 1964, p. 133). 

This is an aspect of collective invention even if the firms would have preferred that it 

did not happen. No one firm or person controls turnover. An ongoing flow of departures 

support a general collective invention regime, and this may be the result of institutional 

structures or an equilibrium outcome. 

It has likewise been observed that Silicon Valley firms learn from one another 

through the rapid movement of employees and contractors between them, perhaps against 

their will. In social science research it has been found that employees with longer tenure 

at their current employer are more productive, but here we see a situation in which 

productivity-enhancing efforts to the region are aided by high turnover (thus low

measured job tenure) for the individual at a particular job. This could be a general 

phenomenon in these environments or just arises from the selection of these perhaps 

ambitious individuals who seize new opportunities. 

4.0 The Homebrew Computer Club (1975-1985) 

When computer chips were first made small, powerful, and cheap enough to make it 

possible to build calculators and something like computers with them, a number of clubs 

of hobbyists appeared across the U.S. to do this and discuss it. The Homebrew Computer 



10 Accused by a newspaper of forming a monopoly, industry titans pleaded (a) that they feared 

overproduction, (b) that they were being taken advantage of, and (c) that in any case they had not arrived at 

an agreement. It is clear that there was an attempt to agree, though its success is not known. All the facts 

of this paragraph are taken from Temin (1964), p. 175.


Club was an important one. It met at Stanford University starting in 1975, following the 

invention of the microprocessor but before there was a personal computer industry. 

Computer designer Lee Felsenstein moderated meetings. The group also had a 

newsletter. The Homebrew club was described vigorously by Freiberger and Swaine 

(1984) (henceforth FS84) and Levy (2001) from which the quotes below were drawn. 

“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.” (FS84, p. 106) Homebrew meetings included a presentation, often of a 

demonstration of a club member’s latest home 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. It worked brilliantly, and numerous companies 

were formed. A remarkable amount of information was exchanged at those meetings, 

and much information had to be exchanged; they were all in unfamiliar territory.” (FS84, 

p. 106) Members were drawn to the hands-on experience of making computers and 

understanding the component parts. Few focused on the theory of computing, or even the 

social effects of computing. 

Hundreds attended regularly by the end of 1975.11 Some members foresaw great 

potential for microcomputers. One member described himself and other Homebrewers as 

a pivotal in something like the industrial revolution. 

The Homebrew Computer Club was not merely the spawning ground of many 

Silicon Valley microcomputer companies. It was also the intellectual nutrient in 

which they first swam. Presidents of competing companies and chief engineers 

would gather there to argue design philosophy and announce new products. 

Statements made at Homebrew changed the directions of corporations. Homebrew 

was a respected critic of microcomputer products. The Homebrewers were sharp, 

and could spot shoddy merchandise and items that were difficult to maintain. They 

blew the whistle on faulty equipment and meted out praise for solid engineering and 

convivial technologies. . . . 

After Homebrew meetings, the most fanatical of the members went to a local beerand-burger place known as the Oasis – everyone just called it ‘the O.’ They sat in 

wooden booths at wooden tables, surrounded by the deeply carved initials of 

generations, and drank beer and argued computer design. They ignored the fact that 

they were competitors. There were a lot of things to learn in developing this new 

kind of product, and they weren’t about to let economic issues get in the way of 

learning all they could. (FS84, pp. 108-111) 

There were great differences in the talents, prior experiences, and resources of various 

members. Steve Wozniak’s early experience there was that he didn’t know enough: “The 

others were talking about the latest chips: the 8008 and the 8080. Woz felt lost. He 

hadn’t heard of them. But he had designed a video terminal and the club was interested. . 



11 Saxenian, p. 34. At one meeting it was estimated that there were 750 people attending. (FS84, p. 106). 

This author attended a Homebrew meeting in the late 1980s which had perhaps 150 attendees. By then 

most demonstrations were conducted by companies, not by hobbyists. 


. . ‘It changed my life,’ Woz would later recall. ‘My interest was renewed, and every two 

weeks the club meeting was the big thing in my life.’ . . . Woz couldn’t afford an Altair 

[the first microcomputer kit], but he watched with fascination.” (FS84, p. 211) 

Later he built his own. Wozniak “brought his computer to Homebrew and passed out 

photocopies of his design so that others could duplicate it. Like a perfect hobbyist, Woz 

believed in sharing information. The other hobbyists were indeed impressed . . . . He 

called his machine an Apple.” (FS84, p. 212) Wozniak and Jobs formed the Apple 

Corporation and hired fellow Homebrew members Chris Espinosa and Randy Wigginton. 

Writing about the innovative strength of Silicon Valley, Saxenian (1994, p. 34) 

identified the Homebrew Club as one of many expressions of a culture of sharing 

technology. That Silicon Valley culture had been described as free-flowing before the 

Homebrew Club existed and was not caused by the club. But the Homebrew Club was 

one important institution among many that supported collective invention. The products 

of this environment later supported tremendous economic growth. Not only did the club 

make technical information available and interpretable, but it was part of the general 

enterprise for innovators and potential innovators to meet. It was generally understood 

that the most effective way to benefit financially from an innovation was to get a product 

to market quickly, and to set engineering standards if possible. Legal protections like 

patents were viewed with suspicion and took more time to arrange and to use as defenses. 

Homebrew evolved. Members who had started companies stopped coming, partly 

because keeping company secrets would be uncomfortable at Homebrew. Keeping 

secrets for private advantage violated what Levy (2001) called the Hacker Ethic -- that 

information should be freely available. 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. . . . they had secrets to keep. 

[One former Homebrewer said] “. . . 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 two young friends, Espinosa and 

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


5.0 Common elements of these histories 

Let us back out from the specifics of the steel case and the personal computers case, 

to think about the general phenomenon and how the term “collective invention” applies. 

In these cases, engineers were optimistic about the future of the technology but did not 

know for sure how it would develop. The future nature of products, production 

processes, and markets was not clear, or was not commonly known. Visions of that 

future varied and at least some were wrong. This is technological uncertainty, which 

characterizes the cases discussed above.12 

To see this clearly, suppose instead that industry participants could forecast perfectly 

what technology they would be using ten years later. Then sharing technology through 

collective information-sharing institutions would be a bad idea -- sharing would not gain 

anything but could let trade secrets got out. Suppose a less-extreme thing, that industry 

participants didn’t know the future course of the technology but were confident which 

research and development would find it. Then it would be most efficient and profitable 

for them to conduct that research and development privately, perhaps with partners, and 

to keep the results secret or to patent them. Collective invention does not seem to occur 

in contexts like those but rather when uncertainty is great, and the players cannot predict 

well at all. 

These collective invention waves began with a new opportunity -- a new invention, or 

the expiration of a patent. Before the period of collective invention, either too little is 

known to interest those in search of a better product or process, or they are legally 

prevented from getting the resources to participate in the collective search. Once that 

opportunity is recognized, players are drawn in. 

The collective search process creates an externality which creates a network effect: 

member A derives some indirect benefit and perhaps cost when member B joins. The 

expected gain of members probably grows with the size of the network. In the long run 

there could be gains to the larger society from the new technology through lower prices, 

employment opportunities, and the opportunity to tax new products. 

A stylized time ordering of a wave of collective invention follows. 

• A new opportunity opens from an invention, or the expiration of a patent. 

• Then there may be a quiet period, in which some interested parties are aware of 

the uncertain potential of the newly adaptable invention. 

• Collective invention institutions form, creating a social network. 

• A flow of adaptations (microinventions) follows, from hobbyists and firms. 

• New firms appear, seizing opportunities to apply the new technology for profit. 



 A collective invention regime can exist in the absence of technological uncertainty. A firm might want to 

join a collective invention regime if it could not afford to conduct the required research, or if the payoff 

expected from conducting private research would not cover its costs. The information exchanges among 

mini-mill steel plants discussed by Schrader (1991) were of this type. But the cases discussed here have a 

different flavor.


• With the establishment of a profitable industry, technological uncertainty is 

reduced and the collective invention process evaporates. Surviving firms run 

private research and development. If that is expensive, few firms survive. 

Consider the similarities across cases in Table 1. 

Table 1. Episodes of collective invention 

Steam engine case Cleveland 

district iron 

blast furnaces 

U.S. mass 

production of 

steel case

Microcomputer 

club, 

Homebrew 

example

Open source case 

Linux example

Instigating or 

enabling 

events

Watt’s patent 

expiration 1800 

 Patent pool 

agreement in U.S., 

1867 

Microprocessors 

available, 1971 

Internet (circa 

1970), AT&T 

breakup, (1984) 

Common 

institutions 

or 

publications

Lean’s Engine 

Reporter, 1811-

1904 

Books and 

consultants, 

1850s-1870s 

Prof. journals like 

TAIME and 

Bessemer Assoc. 

publications, 

starting 1871 

Homebrew 

computer club 

meetings and 

newsletter, 

1975-1980s 

Internet bulletin 

boards and the 

Linux source code 

itself, starting 1991 

Price of entry 

or 

restrictions 

on entry

Not available. Informal 

inclusion 

$5000 till 1877, 

then $80,000. 

(Temin) 

Zero Access to Internet 

bulletin boards 

Tacit 

knowledge, 

skills, and 

prerequisite 

tools

Steam engine 

engineering or 

operation 

 Investment capital 

and background in 

furnaces or rolling 

mills 

Practical 

electronics 

knowledge 

Unix development 

software 

(compiler, linker) 

Readers Mine managers in 

Cornwall region of 

southwest England 

Iron makers in 

Cleveland district 

of northeast 

England 

Bessemer patent 

licensees in U.S. 

Silicon Valley 

computer 

hobbyists 

Unix 

programmers, 

connected by the 

Internet 

# of 

contributors

Approximately 

two dozen 

 Dozens Many dozens Thousands 

(Pavlicek, p. 63) 

# of readers around 1000 

AIME members 

Several hundred Thousands 

Editor or 

moderator

Joel Lean, then his 

sons 

Isaac Lowthian 

Bell, and others 

AIME, other 

professional 

associations, and 

Holley 

Lee Felsenstein, 

Gordon French 

Linus Torvalds 

In most of these cases there was a common publication all the participants could read, 

though they did not need to read it to be participants. Participants could not name all the 

other participants, as colleagues in a single laboratory could. Yet in these environments 

the networks seem to arise naturally, that is, not because of a powerful organizer or 

common prior interests, but because of interest in the subject. 

Once technological and market uncertainty is in the air, some people believe they see 

an opportunity to do something new and better than ever before. People believe, 

sometimes correctly, that they have a unique contribution to make which can give them 

joy and perhaps wealth. Partly because of that sense of opportunity, collective invention 


can arise naturally. Because there is no established market or technology, participants in 

a club or journal lose little by sharing what they know, but they could gain a lot from the 

contributions of others built onto their own designs or insights. So, some of them share 

freely. Participants search for improvements and share their findings. 

Economic predictions are associated with technological uncertainty and therefore 

potentially with collective invention projects. Tushman and Anderson (1986), Dosi 

(1988), Rosenberg (1996), Greenwood and Yorukoglu (1997), and Meyer (2002) drew 

these links between technological uncertainty and its economic effects, using various 

terminologies: 

• Technological uncertainty and perhaps collective invention coincide with heavy 

investment in research and development and therefore low profits in the industry. 

• Income inequality in affected sectors rises temporarily because opportunities for 

financial success and failure are created by the uncertain situation. Analogously 

on the profit side, many firms are wiped out even as others grow rapidly. 

• Productivity improvement, as measured by current inputs and output, slows down 

as the industry or economy adapts to the new situation. Productivity statistics 

undercount useful work done by collective invention since information is 

generated and given at a low cost. But measured productivity may rise greatly 

after engineering standards are set and the relevant industries and technologies are 

established. 

These economic observations generally do apply to the steel case and the 

microcomputer case discussed above. 

5.1 The social network perspective 

Open-source software projects can be modeled as social networks, made up of 

developers with links to the project or to the other developers.13 To stay consistent with 

the theoretical literatures, members of social network are called actors, whereas in search 

contexts they are called agents or, in a game theory context, players. These terms always 

describe the technology’s developers. The earlier cases of steel development and 

microcomputer creation could also be described this way. 

Liebeskind et al (1996) made a useful definition of social networks applicable to 

information interchanges like those in collective invention environments. “A social 



13 Examples include Cowan and Jonard (2000), Madey, Freeh, Tynan, and Hoffman (2003), and the sources 

cited in Scacchi (2003, p. 19). An empirical regularity of such networks shown by Madey, Freeh, and 

Tynan (2002), is that power laws describe the distributions of (a) the number of projects a particular 

developer joins, and (b) the number of projects with n developers. If one thinks of each previous historical 

episode as one case, the number of examples given in this paper is too small to test this hypothesis on the 

cases presented. A more precise approach would be to take each journal, in the steel case, or each club in 

the microcomputer case, and treat it as a network. That would also reduce the serious selectivity problem 

in this paper, which discusses only those cases of collective invention which succeeded on a large scale and 

were well known after the fact. 
	

network can be defined as a collectivity of individuals among whom exchanges take place 

that are supported only by shared norms of trustworthy behavior.” Communication 

through networks is thus more like scientific communication (exploratory, and not 

explicitly paid) than like commercial communication. They went on to compare 

networks to hierarchies and to markets compactly and elegantly. Three components are 

relevant here: 

• “Unlike hierarchies, but like markets, social networks involve exchanges between 

legally distinct entities.” Network links are external to each organization. 

• “Unlike markets, but like hierarchies, social networks support exchanges without 

using competitive pricing or legal contracting.” They depend instead on shared 

norms. 

• “Social networks can [enable organizations to flexibly] switch from one source of 

knowledge to another without incurring the costs or commitments inherent in either 

hierarchical or market exchanges.”14 

In a collective invention network, actors contribute streams of information into pools 

which may then be controlled by editors or moderators. Members can include hobbyists, 

employees, or corporations willing to share. For sharing to be useful the participants 

must have different expertise, experiences, or tools. Useful knowledge in a collective 

invention network accumulates over time. 

Actors may share their findings because that is ethical behavior, and they feel good 

about it. They also build a reputation, which provides psychic benefits, prestige and 

entrepreneurial opportunity. The cost of sharing is sometimes low, especially in the 

open-source case. In the open-source case each actor can help ensure that the benefits of 

future fixes and features developed by others apply to the player’s own system by giving 

the others enough code so they are running systems compatible with the player’s own. 

Lurkers (non-contributors) impose few costs and may provide future benefits. 

One kind of network is an information brokerage, which is a process “by which 

intermediary actors facilitate transactions between other actors lacking access to or trust 

in one another” (Marsden, 1982). Arrangements other than a star-shaped network, in 

which an actor at the middle of the network controls what new information is distributed 

to the others, would delay information transmission, and introduce new intermediaries 

and associated principal-agent problems. (Gould and Fernandez, 1989). The editor of 

the journal, at the center of the star-shape, is serving many authors and readers. This 

induces principal-agent problems, because the editor is an agent of so many principals, 

but this person is monitored by the entire population of players. So the arrangement with 

one monitored central editor publishing a journal is perhaps the most efficient shape of 

the network, or may minimize principal-agent problems relative to some other structure. 

It may be acceptable to the other players that the central player gets rich; for them the key 

thing is to stay informed. 



 Liebeskind, Oliver, Zucker, and Brewer (1996) pp. 430-439. Their subject was the development of 

biotechnology across universities and firms. This was a collective invention process too, with dramatic 

technological uncertainty, but they did not use these terms.



In both the network of steel developers and the network of computer builders, certain 

individuals were central. Alexander Holley was a charismatic central figure of steel 

development who wrote frequently in the journals. He encouraged participation, 

suggested appropriate behavior, and actively traveled and discussed the technology. He 

was an enabler for their work. Lee Felsenstein was the principal moderator of the 

Homebrew club, and made sure people not only listened to presentations but also talked 

to one another. A related advantage of the collective invention process in principle is that 

an extremely insightful or productive specialist is made useful to all the developers. In a 

privatized R&D structure only one cluster of developers would get that person’s 

feedback. 

Actors transmit messages containing partial results from experiments through the 

network and privately. They receive messages that pass through the filter provided by 

the newsletter editor, or private communications specifically addressed to one another. 

There is a cost to sending and receiving messages, but within the network it is low. 

The information brokerage process with the star shape is not characteristic of 

diffusion, in which a long, slow sequence of adoption interactions take place. Here, the 

activity under study is not the diffusion of any particular innovation, but the process of 

examining one after another. The information brokerage structure does not strictly 

describe the innovation process because is it clear in each historical case that important 

conversations between happen outside the newsletter too. 

It does not follow from this process that the best ideas prevail, although they do have 

an advantage. A design could dominate even if it were not the most efficient. The choice 

may be made early (as in the QWERTY typewriter keyboard case) and not be 

reconsidered since the members of the network are not organized in such a way as to 

make a collective decision. 

The information brokerage structure is not competitive, so as long as it is oriented 

around a hobbyist research problem it is not properly modeled as an industry. But as the 

technology improves, there could be profits to be earned from the technology, and then 

the interests of members become differentiated. Some clubs may become firms. That is 

a club could incorporate and commit itself to using some particular technology and 

equipment, in order to make profits. Firms or other subgroups could decide to keep their 

information secret. Without contributions from these subgroups, other players may lose 

interest in the network. So if members break away, the network may collapse from lack 

of interest or resources. If the member firms competed for profits the network could 

break up and become a competitive industry. The network could thus start an industry 

whether the network continues on or not. Homebrew alumni developed several personal 

computer standards including the Apple II, the Osborne, and the Macintosh, which were 

competing products. The Homebrew club survived into the 1990s and the Apple 

Corporation continues to sell descendants of the original Macintosh. 

In the cases we have considered, important innovations come from people within the 

network, which indeed is best understood as a network not a market or hierarchical 


phenomenon in the language of Liesbeskind et al. A model can leave out people in the 

economy who are not in the network. But studying the network per se leaves out some 

deeper questions. Let us think out next why the individuals want to create the network. 

6.0 The search for innovations 

The network exists, I argue, because it reduces the costs of searching. The institution 

is a kind of search equilibrium outcome. Participants build the relationships in the 

network because of particular conditions in the environment. We can describe these in 

supply and demand terms. In brief, participants demand flows of information to improve 

their technology because they believe there is an opportunity to do something new and 

useful. There is a supply of information about relevant innovations because the 

technology is in fact new and people are trying to figure it out. Both of these result from 

technological novelty and uncertainty. 

A market could appear for this information. This is more likely if the information is 

immediately useful in a predictable way in production, particularly to profit-seeking 

firms. It is also more likely if the bits of information are homogenous and therefore can 

have a standard price. But under conditions of great technological uncertainty these 

conditions do not apply. We observe that many of the participants are not directly profitseeking, and that the bits of information going through the network are heterogenous and 

unique, and that they are not priced. We discuss how to model these attributes below. 

6.1 Seekers and the innovative search 

Search theory in economics normally describes agents who sample a distribution 

repeatedly, stopping optimally when further search appears on average unprofitable. The 

search might be for job opportunities, applicants for a job, or marriage partners. Search 

theory has been used to algebraically characterize searches for innovations too. In search 

models of innovation, a profit-seeking enterprise decides when to conduct formal 

research and development (R&D). A sophisticated example is in Jovanovic and Rob 

(1990). In these models innovative search is a gamble whose possible payoffs are 

averaged together, and if the average outcome is profitable, the firm devotes resources to 

the attempt to innovate. The firm weighs the costs of an experiment or search attempt 

and the distribution of possible profits from it. The balance or equilibrium between these 

in the model predicts an amount of R&D effort that is more than zero but not infinite. 

What would have to change to make such theories applicable to the early periods of 

innovative search? Several possible changes to these R&D models could adapt them to 

describing the early search for a useful form of a new technology. 


6.2 The subsidy implicit in enthusiasm 

Allen (1983) and others have offered many hypotheses to explain why self-interested 

private firms or individuals would release proprietary information. The list can be 

overwhelming. Allen (1983) has examples supporting the first six. 

• First, hobbyists and people within firms have ambitions that they can help meet by 

releasing technical information – prestige, fame, or employment opportunities. 

• Second, firms garner publicity by making their successes known, and it motivates 

their staff to compete to improve. 

• Third, it can be costly to keep information secret especially when there is substantial 

movement of employees between firms. 

• Fourth, a firm’s release of information could increase the value of some asset it had. 

For example when better ironmaking methods were introduced, ore deposits owned 

by a British iron firm gained value, whether or not that firm used the new methods. 

• A related (fifth) idea is that cooperating with other firms to improve production might 

be expected to induce some improvement to one’s own firm, either as a side effect or 

as a payback by the recipient firm. In computer and software contexts especially 

there are advantages from establishing engineering standards by giving away designs 

or software. This was one reason Web browsers were given away. 

• Sixth, although each firm competed against other local firms, collectively they 

compete against other regions. They have an incentive to work together to make 

local production as efficient as possible and the remote regions irrelevant. They 

might do this by allowing consultants to consult with other firms as well as their own, 

by encouraging local suppliers to gain economies of scale, by building a common 

transportation infrastructure, or by agreeing to engineering standards. 

• Publications in an open environment give employers a way to judge the contribution 

or skills of a researcher which may be hard to judge directly. Publications approved 

by editors serve as a sort of certification signal as suggested by P. David (1998). 

Hobbyists have other reasons, too: 

• For them, playing with the new technology can be fun and absorbing, and many 

hobbyists thought it was virtuous15 and exciting to share their findings. 

• Lastly, some technologists may have a particular need they are trying to satisfy. 

Programmers may be willing to write a fix or feature in software because of a 

particular itch the programmer wishes to scratch.16 

Combined, these motivations over-explain the phenomenon. There are many reasons 

to share information, and a state of technological uncertainty and opportunity contributes 

to most of them. In a static industry with unchanging technology in which the players 

anticipated little change, these incentives would not apply. But once the opportunity 



Many innovators express this thought in the descriptive literature. They say they are doing what is right

(because software should be shared, “like recipes” in Stallman’s language) or are trying to achieve 

something good (because society can be made better through this kind of contribution). This view does not 

usually conflict with paid employment or secrecy in other parts of their lives. 

16 This is Eric Raymond’s vivid phrasing, from http://catb.org/~esr/writings/cathedral-bazaar/cathedralbazaar/ar01s02.html, and later in Raymond (2001), p. 23.
appears, there are many reasons to participate in sharing mechanisms for their 

information. Sharing and experimenting are complementary. Experimenters would be 

more likely to give up one if they did not have the opportunity to do the other. 

In the early research discussed above, there were just a few individual experimenters. 

Most of the early experimenters lose money. In modeling terms this is different from the 

R&D equilibrium of an innovation search theory. Experimenters subsidize the search 

with their own enthusiasm and other resources. The enthusiasm flows from the belief 

that there is a great opportunity. This subsidy does not prevent a search model from 

making predictions. For example, as in the other models, there would be more searching 

if costs of experimenting were lower. It is possible the model could predict that less 

searching would be subsidized as profits appear and profit-minded R&D explores the 

unknown possibilities, displacing the hobbyists. 

The subsidy can be measured sometimes. Moskowitz and Vissing-Jørgensen (2001) 

found that financial returns to entrepreneurs they studied were lower on average than a 

risk-reward tradeoff with stock markets would imply, once one takes into account that 

entrepreneurs were so often poorly diversified, with most of their holdings in their own 

firm, and were therefore carrying extra risk. These are observations of a kind of subsidy, 

made perhaps because the entrepreneur is focused on other things, or is very optimistic 

about the future of a particular venture, or is institutionally unable to diversify from it. 

Consider all the factors that historically have motivated researchers into early 

technologies that are exogenous to the search discussion. Some, like Bessemer, 

underestimate the difficulty of turning the technology to a profit. Others are driven by 

prestige, not the desire to enhance production or earn a profit. Some believe they can 

change the world, and see this as an opportunity, rather than the opportunity to start a 

firm. Tim Berners-Lee, the inventor of the Web, seems to have taken this perspective. 

Like some other technology developers, Berners-Lee also had a particular application – 

sharing physics research among government institutions – for which it was reasonable to 

think the Web would pay off straightforwardly. Some early innovators see that there is 

an opportunity to set engineering standards or to be one of the first to sell a product, and 

purposely forego any efforts to obtain intellectual property protection. A number, in the 

Homebrew case, understood that once they developed relevant skills and were recognized 

in the new arena of microcomputer hardware and software, they would be very 

employable afterward regardless of whether their own design or innovation were 

valuable. This list is not complete – in fact, the motivations of early technology 

developers are many and diverse. So for purpose of modeling a search equilibrium in 

which the players share their findings, the experiments are exogenously subsidized. 

6.3 Experiments that can create productive capital 

Members of the social network may have weak links to one another which support 

potentially valuable matches. Granovetter (1973) found in a case study that a wide 

network of friends seemed to be more effective in finding a job than a few close, 

committed friends. Contacts in the network can help members reach desired goals. 


Members discuss problems and possible solutions with one another. In a matching 

process, members evaluate whether others have useful tools and skills. The interactions 

have other dimensions as well, but these are key components. 

Members of the network conduct private experiments based on their own resources, 

interests, and insights. This is productive work but does not generally increase current 

output because it is experimental.17 It can have financial value immediately however and 

eventually improve productivity. See Appendix A for an example of how to treat the 

experiments as productive, in expectation. 

A player might have nothing to share before experimenting. The player might not 

experiment if there were not a venue in which to share the findings. Sharing and 

experimenting are complementary activities to achieve the individual’s objectives. Each 

person may have some objective other than to search. For example, a player may want an 

operating system which gives the user control over which processes running on the 

computer have high priority, and the player is willing to edit the operating system code to 

achieve it. This person might not wish to have to make a better operating system, but 

may be willing to go that direction if that is the only way to get the better operating 

system. There is a collective search process, and in the search, sharing and 

experimentation are complementary inputs. Each person who wants to make progress in 

the search, experiments more and finds that it is optimal to share it. And players who 

participate in sharing more, find it is optimal to experiment more. 

In the open-source software context, Lerner and Tirole (2002) asked “Why should 

thousands of top-notch programmers contribute freely to the provision of a public good?” 

There is an answer, here. The interpretation here is that programmers who do this would 

have experimented with the technology anyway, and they share because this makes 

experimentation more efficient. As posed by Lerner and Tirole, the situation sounds like 

one in which the programmer is giving capital or labor away. If we assume that the 

programmer has some objective to be met by experimenting with the technology, the 

programmer’s decision to share seems more natural, and in all these historical cases the 

ones who share were experimenting. Within the context of all the experimental searches 

for innovations, there will be programmers, hackers or other technology developers who 

follow a “freely revealing” strategy (Harhoff, Henkel, and von Hippel, 2002). 

6.4 A production function in which pure innovation is possible 

Let f(X, T) be a production function. Its value is the amount of some kind of output – 

in the Bessemer case an amount of usable steel. X is a vector of current input material 

quantities, including pig iron, iron ore, and the manganese compound called spiegeleisen 

which turned out to be an essential ingredient. T is a vector of measures of design and 



17 An individual’s purposes need not match the phrasing here for this description to fit. What seem like 

experiments in this structure may have entirely predictable consequences to the actor. An insight may be 

erroneous, but lead to a useful experiment. The technology may seem usable to some experimenters but 

not others. And many dimensions of X and T may not be clear to anyone at the time of an experiment. The 

history may be chaotic, but the description here imposes an after-the-fact perspective and coherence. 


technological attributes such as the height of the furnace, and the duration of the air blow 

through it. T is held constant in the classic economic problem of optimizing output 

quantity for each set of inputs, or for minimizing inputs to obtain a fixed quantity of 

output, or for minimizing input costs to obtain a certain level of outputs. Let us assume 

here that optimizing over X is not too difficult in principle (whether or not it is feasible 

with the resources available). Instead let us concentrate on the problem of searching the 

space of possible Ts. For example, the agents might seek the steelmaking design which 

produces the best steel output given as input one ton of pig iron. 

The search for innovative adaptations of a new production process is like a search for 

a desirable T in a region of RN, though the searcher may not know the number of 

dimensions N. Each point in the region represents a particular T and therefore a 

production function. Each dimension characterizes the choices for some attribute of the 

production function. Bessemer converters made steel by blowing air through molten pig 

iron so such design dimensions might include the height of the converter or furnace, the 

quantity of manganese added (which was zero by default until it was discovered that it 

improved the product), the duration of the air blast, and whether and how to integrate a 

blast furnace (to make molten iron) into a steel production process. 

Let us assume members share an optimistic belief that there are more useful or 

profitable locations of f(X,T) than have yet been seen. Each player has a private image of 

f(X, T), based on imperfect information, which we might call fi,t(X,T) to index it by 

player and time. Most of the information is missing and some is untested or could be 

incorrect. A particular hobbyist-agent may have some goal other than learning about 

f(X,T) but read the journal anyway.18 The presence of a journal about f(X,T) focuses 

attention on specific technological problems, and makes it feasible to share news about 

f(X,T). The resulting social network of readers and authors is not an organism with its 

own objectives, but an institution that helps hobbyist-agents achieve their own ends. 

Historians of technology treat “success” and “failure” as ambiguous and socially 

constructed terms. (Lipartito, 2003, pp 53-58) A technology can be a failure in the 

technical sense that it didn’t work; in the financial sense that it did not pay off to its 

inventor; or in the market share sense that it was dominated by some other technology. 

Along with these three dimensions of success, there is another here. A technological 

experiment exposes the behavior of the technology with the design choices in the 

particular vectors X and T that the experimenter attempted. If there is a social network 

through which the result is shared with others, the experiment can help the group find 

success. Thus the ambiguity of success and failure here in this model of technological 

history is consistent with the ambiguity in the world the model should describe. 

The seekers in the network learning about f(X,T) are members of a larger population. 

Because the technology is uncertain, some people do not believe that f(X,T) can be 



18 Each player has private information of various kinds. Each player has psychological and financial 

objectives which might be met by combining private and public information. A field of science can be 

viewed this way; Joel Mokyr suggested the term “collective discovery” to parallel “collective invention.” 


improved, but do not affect the history of the search. Indeed they could be forgotten by 

history.19 Many of the searchers are forgotten too. 

In some regions f(X, T) is smooth and single-peaked, so analytic optimization over T 

is possible if the function can be estimated. But f(X, T) is not defined at every point, and 

has discontinuities. For discrete choices, f() is defined only for certain T values, e.g., 1 or 

0 for yes or no. An experiment may be a shot in the dark which lights up little space. 

At the beginning not much is known about the dimensions of the vector T. This 

induces technological uncertainty among the network members and between themselves 

and the larger population. Uncertainty is intrinsic to the situation since the design 

problem is hard and not well known. In Table 2, the issues defining the dimensions of T 

were each uncertain in the empirical sense that at some time some participants bet on 

different choices but eventually one choice became an industry standard. 

Table 2. Searches for technological and organizational design 

Bessemer steel, 1856-1881 microcomputer hardware, 

1971-1982 

open-source operating 

system 

Output y, 

where 

y=f(X,T) 

Quality-adjusted quantity of 

usable steel 

Quality-adjusted capability of 

computer hardware to store, 

compute, and communicate 

Ease-of-use- and qualityadjusted capabilities 

dimensions 

of X 

(inputs to 

production) 

Rates of input of 

ingredients: iron, 

speigeleisen; 

Is it made from standard 

electronic parts? 

Were they new or used? 

Does it have an operating 

system? 

Can it multitask? 

How well can it share files 

with other operating 

systems? 

dimensions 

of T 

(design 

attributes 

of 

production) 

Is manganese added? (0/1) 

How near is the steel plant 

to the Great Lakes by 

water? 

How near is it to lowphosphorus iron ore? 

How near is it to coal? 

Did a chemist work there? 

Is iron of several types 

mixed together? 

Is steelmaking integrated 

with iron-making in blast 

furnaces? 

Is the architecture open? 

Can hackers make 

compatible hardware? 

Is it a smart terminal, or does 

it stand alone? 

Were programs stored on 

paper tape, magnetic tape, 

or disks? 

Can a modem plug in? 

Do customers want a smart 

terminal, or a standalone 

computer? 

Are graphics possible? 

Are screen pixels mapped 

directly to memory 

locations? 

Which programmers can 

work on it? 

Do they use source control? 

Can a company make 

money selling software, or 

by offering services? 

Which licensing scheme is 

used? 

How does the moderator 

prevent the forking of one 

project into two? 



19 The histories of collective invention that are best remembered are those which succeeded in a big way, 

such as the examples of steel and the personal computer. Ideally a theory here would have a way of 

describing the process when the invention did not succeed, or when there were people who thought it 

would not. A good example is that of the hot-air balloon, as discussed in Mokyr (1990). Some people 

thought this dramatic new invention would be useful and the technology was explored in France in the late 

1800s. But no greatly profitable application was invented.

The definition of f(X, T) is in terms of input measures of various kinds and of output 

quantities. It is not known at the beginning of the search whether f(X, T) could be 

profitable. Different views may exist on which aspects of an existing technology are 

problematic. After the fact, a historian may confidently write that the problem was that 

the iron was excessively oxidized and the solution was to add a manganese compound, 

but beforehand the definition of the problem might well be subject to dispute. 

“Problems” are defined in discussions which include visions of possible “solutions.” 

Once a better design is clear, it becomes common knowledge that there was a problem 

which was solved. Until then, the process is not definitively known to be improvable. 

The players have diverse resources, opportunities, insights, abilities, interests, skills, 

and agendas. Each one may have something unique to bring to an experimental 

opportunity, so pairs of participants can do experiments that the individual participants 

could not do. They have different capabilities in this exploration production function 

which produces stochastic results. If they hit a result that pays off, they may spin out into 

a business, and search f(X,T) further on their own, keeping their own experimental results 

secret, or patent them. Capabilities that individuals develop from experience may outlast 

the experiments too. 

The complete f(X, T) is never known, because it is too complicated, many aspects are 

of no interest, and disputes remain. Other problems take over, such as profit 

maximization, satisficing for the survival of an organization or an employee. So the 

history we see does not include an explicit declaration of f(X,T) whose mysteries are all 

uncovered; rather, we see a series of technologies and experiments. Errors are corrected, 

anomalous results investigated, and doubtful results established to general satisfaction. 

Players have ways of extrapolating information and beliefs about f(X, T) into the space 

that is not known. Clubs may form around various visions of the future discoveries about 

f(X, T). A club may then become a business. 

Once a number of clubs form, the network may collapse or change its focus. The 

project of exploring f(X, T) may no longer be of interest to a wide readership since (a) the 

best information about f(X,T) is now private property; (b) there is little prospect of new 

entry against established competition; (c) there is no purpose in being a hobbyist any 

more since progress in the industry’s technology is sustained by profit. Technological 

uncertainty has then been reduced enough that the hobbyists are gone and search over T 

has been taken over by corporate R&D. 

6.5 The option of creating new firms 

As increasingly profitable locations in the domain of f(X,T) are found, corporate 

R&D is more likely to appear. The resources of a profitable producer make it possible to 

build a patent portfolio and a hierarchical organization which is private property. Such 

an organization explores industrial dimensions of production outside f(), which describes 

production technological choices only. Among the organization’s choices are: to define 

the product; how to sell it; how to describe it in advertising; what customers to seek; what 

prices to charge; whether to behave monopolistically or litigiously; whether to patent; 


whether to keep secrets, and so forth. A firm making such choices often does so 

privately, and if several do this, the collective invention network could evaporate. 

There are examples of the collective invention regime disappearing. In the example 

of U.S. Bessemer steel development, the industry locked down its patents and charged a 

high price to new entrants. Large firms merged starting in 1881 and for the rest of the 

century. Around 1900 the last giant merger formed the monolithic U.S. Steel company. 

In the microcomputer case, many companies arose out of the Homebrew Computer Club, 

including Apple and Cromemco. These companies had secret projects, such as the one at 

Apple that produced Macintosh computers, and Homebrew lost its central importance. In 

the steam engine case, the developments shared in Lean’s Engine Reporter eventually 

slowed. The Reporter ended publication in 1904. 

6.6 Prospects for innovation search theory 

The modeling approaches described above (hobbyists subsidizing the search; a 

production function with design and technological attributes; experiments that create 

capital; profitable departures from the network) could lead to an expanded version of 

innovation search theory in which (for example) restrictions on free speech, publication, 

or reductions in the scale of market production would reduce collective invention. 

Expansions of patentability could also reduce it. Technologies like the Web that make 

information-sharing easier should increase effort devoted to collective invention in a 

model. In such a theory time alone could make the social network collapse because its 

participants could break out to run profitable, secretive, enterprises. As described here, 

collective invention periods are valuable to society at large but are delicate, because they 

depend on the subsidy of the enthusiasts. The institutions are most important when there 

is uncertainty along many dimensions, which creates the possibility of truly major 

innovations. 

7.0 Conclusion 

Collective invention institutions discussed here include clubs, informal newsletters, 

formal journals, conferences, consultancies, anti-copyright rules, patent pools, 

engineering standards, and whatever institutions sustain high job turnover in 

technologically uncertain times. These institutions sustained social networks through 

which findings and innovations were shared without formal intellectual property rights to 

restrict them. 

The incomplete framework here defines an innovative search problem and a social 

network of experimenters who try new production processes. Sharing and 

experimentation are complementary inputs to the search process. The network or sharing 

process is a way of searching more efficiently. The participants choose to search for a 

variety of reasons. Some believe they can get rich, and a few do. The network could 

break up into an industry of profit-seeking enterprises. 
	

Although hobbyist activity can be productive, government actions rarely measure it or 

support it. Government policy can however help or impede collective invention 

processes. For example, governments have a variety of ways to encourage or prevent the 

interchange of electronic information, movement between jobs, and new company 

startups. 

Productivity statistics do not count informal or unpaid research. In fact, if a hobbyist 

invests time and material inputs into an experiment, the hobbyist’s costs could be counted 

as consumption even if the resulting new computer started an industry. Hobbyist, 

collective, or open-source efforts may be an essential phase of technology improvement. 

Early automobiles and airplanes seem to have developed along a collective invention 

path. Perhaps all capital goods industries experience this phase and it could be detected 

in productivity statistics. 

If the institutions in the environment did not support a phase of collective invention, 

some technological developments would stall, and some technologies would never 

develop to become generally useful. Put this way, the idea is obvious. But this tells us 

something about where in today’s world technological developments actually occur. It 

also helps us understand where technological development occurred historically. 

Technical journals, a free press, hobbyists, and collective enthusiasm are hardly 

mentioned in the classic historical works comparing the economic development of the 

medieval west to medieval Islam or dynastic China. The collective invention idea is not a 

major component of these accounts, but perhaps it needs to be. 

This paper contributes to the literature on collective invention in several ways. It 

documents two more cases of the phenomenon – early steelmaking and personal 

computer building. It systematically lists similarities between all the cases, and it 

proposes a search theory approach to relate these similarities to the environments that 

produce them. The idea that experimenters personally subsidize the search is new and 

may be an essential component of models that explain the “period of ferment” in which a 

radical new technology finds practical applications. 



Appendix A. 

The financial value of an experiment can be modeled this way. Suppose a piece of equipment 

produces q units of output every period, the price of a unit of output is fixed at one, and the 

interest rate at which an employer can borrow or lend is fixed at r (e.g., .05 per year). Define the 

present value of a worker’s output to be V on the basis of forecasted prices of inputs and outputs. 

The relation between the present value of the equipment and its output each period can then be 

shown as an annuity: 

The terms after the first are equal to (1+r)-1V so a more compact equation is possible: 

 Consider now the value of having conducted an experiment that discovered a design change 

which would raise output. Let us say it costs c to implement the change, takes time t to have an 

effect on production, and raises output by ∆q. The present value of this experiment is 

Estimates of c, ∆q, and t at the time of the experiment may naturally be poor. 

If the hobbyist pays (through the subsidy engendered by enthusiasm) a cost cexperiment to 

conduct the experiment, and its chance of producing the output improvement is pimprovement, then 

the mean value of the experiment to a producer of the good, in advance of knowing whether it 

was successful, is 

There is also a social gain not modeled here. Experiments help the collective search by 

expanding the knowledge of T-space. Both successful and unsuccessful experiments therefore 

raise the probability that future experiments will succeed.20 This value is increased much more if 

the information is publicly disclosed to other possible experimenters than if it is secret. 

The value of experiments is greatest when there has been a potentially great new invention, 

and technological uncertainty is in the air – that is, the possibility is real that better locations 

within T-space exist than have yet been found. It has been argued that periods like this are 

associated with rises in income inequality and asset return inequality and volatility, because both 

predicted and after-the-fact values of experimenting vary so much.

21 Holding all else constant, 

the value of experimenting is lower if r and t are high. High interest rates discourage 

experimenting, and so do institutions that would delay implementation of a technological 

improvement, e.g. some kinds of regulation or collective bargaining.





	

 
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