Title: Social Networks in the History of Innovation and Inventions
Author: Francis C. Moon
Scope: 4 stars
Readability: 3 stars
My personal rating: 4 stars
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Topic of Book
The author uses modern social network theory to understand the history of technological innovation.
- Innovation does not come from a lone genius. Innovation comes from a network of people with related skills and institutions that coordinate them.
- The rate of innovation is correlated the growth of the network. The larger the number of people and institutions in the network, the faster the rate of innovation.
- Transportation/communication infrastructure, education and research institutions facilitate the growth of networks.
- As a technology is applied to other sectors of the economy, the network grows and the rate of innovation accelerates. Eventually, all applications of a technology are fully explored, members of network come to believe that the field is declining in relevance. The result is that the network begins to shrink.
Important Quotes from Book
In this book we examine the interactions between the genius-inventor and his or her social network using new ideas centered on modern networking systems. We also present evidence that invention is not just a moment of epiphany in a lone genius inventor, but is a culmination of an evolutionary process resulting from a network of people and institutions.
Another interpretation of these innovation stories is the ‘avalanche’ model where the historical network of the information commons becomes sufficiently complete that someone in the network is able to piece the puzzle together and create a new idea or product.
We propose in this book that the exponential growth for different innovation fields, are correlated with the growth in the network. Accumulated knowledge feeds the growth of the network and vise versa. Thus one of the tools for studying growing networks, is the integrated measures of innovation information. In modern scientific fields, we can measure the growth in published research papers.
Exponential growth is like a chain reaction where the larger the number of nodes the larger the growth rate in the network. We will find this phenomenon in many of the innovation examples in this book.
As historical networks advance, not only does the number of nodes grow, but so also does the number of links between the nodes.
In the period between the Renaissance and the Industrial revolution, there is evidence of growth in machine technology knowledge but it grew in a linear manner, most likely because the information and transportation networks that underpin modern technological development were not in place until the nineteenth century.
(i) New ideas in science and technology are often the product of both evolution and complex social networks.
(ii) New technical artifacts are usually preceded by the emergence of other technologies, e.g. the internal combustion engine preceded aviation.
(iii) Secrecy and patents do not seem to inhibit the exponential growth of new ideas.
(iv) Both physical communication and transportation infrastructure networks are necessary to the growth of innovation social networks.
(v) Although artisans played a major role in late eighteenth and early nineteenth C. technologies, educational and research institutions have become important nodes in modern innovation networks.
(vi) Behind every emergence of a new technical product is a growing social network associated with that technology.
One broad outline of such a theory imagines the social innovation network as a set of players in a technical innovation market. When some new technology comes along, such as the steam engine or internal combustion engine, players on the sideline with relevant knowledge or capital begin to come into the market by connecting with other players. We might assume that there is a reservoir of technical knowledge, generated from other innovations, that can at some point be transferred to the new technology.
As the network grows through linkages, new players come into the network with new knowledge or capital that in turn stimulates more linkages with other players in the knowledge reservoir.
One of the important components of the exponential rise in technologies at the dawn of the Industrial Revolution is the talents of skilled artisans and systems to train them. The workshop system in Britain during the early machine age was characterized by a close relationship between master mechanics and young engineers through apprenticeships that generated a social network of machine builders.
By the end of the century, the route of professional education of many famous German engineers started in the university with a background of mathematics and engineering science as well as practical training.
In the network of machine builders, another set of nodes that facilitated technical communication in the nineteenth century was the international exhibitions that attracted millions of visitors. The first major event was at London’s Crystal Palace in 1851. His professional life coincided with new communications networks such as overseas mail and the telegraph that linked the growing industrial world with the first Internet.
The steam engine of the eighteenth century was created by craftsmen and artisans, while the internal combustion engine was designed and built by a new breed of polytechnic trained engineers at the end of the nineteenth century. The exceptions were Lenoir and Otto.
The importance of international meetings in creating innovation networks is illustrated in many of the case studies in this book. Another venue is professional societies.
The Cornell University aviation node shows the increasing importance of research universities in technology innovation networks beginning at the middle of the nineteenth century.
Finally the spread of the knowledge of wireless and radio was accelerated by the publication of new books and magazines even as the technology was being transformed.
Our point is that innovations and inventions by their nature demand a societal network to come before the stage of history. Economic, political and societal factors cast the ultimate votes for acceptance.
Evolution in biology took many branched paths and technical and scientific evolution is probably no different.
One idea that emerges from this study is that patents and copyrights rarely slow the progress of human society in embracing some new idea or device. Sometimes it is not necessary for an outsider to gain direct knowledge of a trade secret. It is possible that the ‘fact’ that someone has accomplished some feat or created some new process is enough to motivate an outsider to try and duplicate, improve or optimize the invention or innovation. Imitation is an important feature of human learning.
There had been a continuous decrease in innovation growth doubling time across technologies and spanning several centuries.
As the internal combustion engine network was growing, there were earlier nodes and links that were disappearing. We call this phenomenon fading memory. Like an old man who knows the latest football players but forgets the names of his old friends, innovation networks have both growing and dying branches. Another way of saying this is that the information commons for any given field can shrink as well as expand.
Innovation networks die in parts as well as grow. When the old branches die faster than new nodes are added, the network dies completely as one can easily find in the debris of technology and science as in the fields of vacuum tube electronics and wave propagation in the ether.
In constructing social historical networks, we have found that the hero-inventor is often a node that generates a large number of incoming and outgoing links. It is in this sense that he or she is crowned a pioneer or leader in the field. It is not the one who is first with an innovation who is awarded the mantle of the hero-inventor, but the one who brings together both new and old knowledge, (generating incoming links) and inspires others to copy and improve his or her invention and innovation (generating outgoing links).