John Margurber

Adjusting policy to new dimensions in science, technology and innovation

The greatest dangers in making science, technology and innovation policy lie in over-simplification and in imagining that one-size-fits-all policy principles are appropriate. But some simple principles may help guide efforts to adjust these policies to the evolving context for science, technology and innovation.

by John Marburger, director, Office of Science & Technology Policy, Executive Office of the President, USA

John H Marburger III, science advisor to the president and director of the Office of Science & Technology Policy, attended Princeton University and Stanford University. He served as director of Brookhaven National Laboratory from 1998, and as the third president of the State University of New York at Stony Brook (1980-1994).

Marburger came to Long Island in 1980 from the University of Southern California where he had been a professor of physics and electrical engineering, serving as chairman of the physics department and dean of the College of Letters, Arts and Sciences. In autumn 1994 he returned to the faculty at Stony Brook as a professor. Three years later he became president of Brookhaven Science Associates, a partnership between the university and Battelle Memorial Institute that won the contract to operate Brookhaven National Laboratory.

While at the University of Southern California, Marburger contributed to the field of nonlinear optics. He developed theory for various laser phenomena and was a co-founder of the University of Southern California’s Center for Laser Studies.

Marburger’s presidency at Stony Brook coincided with the opening and growth of University Hospital and the development of the biological sciences as a major strength of the university. Marburger served on numerous boards and committees, including chairing the governor’s commission on the Shoreham nuclear power facility, and chairing the Universities Research Association, which operates the Fermi National Accelerator Laboratory near Chicago.

Linking the words science, technology and innovation suggests that we know more about the relationships of these activities than we really do. It implies a linear progression from scientific research to technology creation to innovative products. More nuanced pictures of these activities break them down into components that interact with each other in a multidimensional socio-technological-economic network.

Science has always functioned on two levels, the curiosity-driven and the need-driven, which interact in sometimes surprising ways. Galileo’s telescope, the paradigmatic instrument of discovery in pure science, emerged from a pragmatic tradition of lens making for spectacles. The industrial revolution gave more to science than it received, at least until the last half of the nineteenth century when the sciences of chemistry and electricity began to produce serious economic payoffs. The flowering of science during the Enlightenment owed much to its links with crafts and industry, but as it gained momentum science created its own need for practical improvements. After all, the frontiers of science are defined by the capabilities of instrumentation, that is, of technology.

Unpredictability and innovation

The needs of pure science are a huge but poorly understood stimulus to the development of disruptive technologies, precisely because these needs do not arise from the marketplace. The innovators who built the world-wide web were particle physicists at CERN, struggling to satisfy their need to share complex information. Others soon discovered ‘needs’, of which they had previously been unaware, which could be satisfied by this innovation. From that point on the web transformed the Internet from a tool for the technological elite into a platform for a new economy.

Necessity is said to be the mother of invention, but in all human societies necessity is a mix of culturally conditioned perceptions and the physical necessities of life. The concept of need, of what is wanted, is the ultimate driver of markets and an essential dimension of innovation. As the example of the world-wide web shows, it’s very difficult to identify a need before it reveals itself in a mass movement. Why didn’t I know I needed a cell phone before nearly everyone else had one? Because until many others had one I did not, in fact, need one. Innovation has this chicken-and-egg quality that makes it extremely hard to analyse. We all know of visionaries who conceive of a society transformed by their invention, and who are bitter that the world has not embraced their idea. Sometimes we think of them as crackpots, or as simply unrealistic about what it takes to change the world. Practical people view the world through the filter of what exists, and fail to anticipate disruptive change. We are nearly always surprised by the rapid acceptance of a transformative idea. If we really want to encourage innovation through government policies, we are going to have to get to grips with the deep unpredictability of the mass acceptance of new concepts.

The innovations we are most interested in are those that become integrated into economies. Their adoption depends on their ability to satisfy some perceived need by consumers, which may be an artefact of marketing, or fashion, or cultural inertia, or ignorance. Some of the most profitable industries in the developed world - entertainment, automobiles, clothing and fashion accessories, health products, children’s toys, grownups’ toys - depend on perceptions of need that go far beyond the utilitarian. And yet these industries depend on sophisticated and rapidly advancing technologies to compete. Of course they do not depend on technology alone. Technologies are part of the environment for innovation, or in a popular and very appropriate metaphor - part of the innovation ecology.

Driven by complexity

The complexity of innovation and its ecology is conveyed in the first chapter of a current US best-seller called Innovation Nation, by John Kao, an American innovation guru formerly on the faculty of Harvard Business School.

“I define [innovation],” he writes, “as the ability of individuals, companies, and entire nations to continuously create their desired future. Innovation depends on harvesting knowledge from a range of disciplines besides science and technology, among them design, social science, and the arts. And it is exemplified by more than just products: services, experiences, and processes can be innovative as well. The work of entrepreneurs, scientists, and software geeks alike contributes to innovation. It is also about the middlemen who know how to realise value from ideas. Innovation flows from shifts in mind-set that can generate new business models, recognise new opportunities, and weave innovations throughout the fabric of society. It is about new ways of doing and seeing things as much as it is about the breakthrough idea.”

This is not your standard OECD definition. Gurus, of course, do not have to worry about leading indicators and predictive measures of policy success. Nevertheless some policy guidance can be drawn from this definition, and I will do so later.

To reiterate, my first point is that the structural aspects of science, technology, and innovation are imperfectly defined, complex, and poorly understood. There is still much work to do to identify measures, develop models, and test them against actual experience before we can say we really know what it takes to foster innovation.

Managing expectations

The second point is that all three of these complex activities are changing with time. Science changes through the accumulation of knowledge, through revolutions in its theoretical structure, through its ever-improving technology, and through its evolving sociology. The technology and sociology of science are currently being affected by rapidly changing information technology. Technology flows increasingly from research laboratories but the influence of technology on both science and innovation depends strongly on its commercial adoption. Mass manufacturing drives down the costs of technology so it can be exploited in an ever-broadening range of applications. The mass market for precision electromechanical devices such as cameras, printers, and disk drives is the basis for new scientific instrumentation. Innovation is changing too, as it expands its scope beyond individual products to include all or parts of systems such as supply chains, and inventory control, as in the Wal-Mart phenomenon. Apple’s iPod does not stand alone; it is integrated with iTunes software and novel deals with media providers.

With one exception, however, technology changes more slowly than it appears to, because we encounter basic technology platforms in a wide variety of relatively short-lived products. Technology is like a language that innovators use to express concepts in the form of products and business models that serve (and sometimes create) a variety of needs, some of which fluctuate with fashion. The exception to the illusion of rapid technology change is the pace of information technology. It has fulfilled Moore’s Law for more than half a century, and it is a remarkable historical anomaly arising from the systematic exploitation of the understanding of the behaviour of microscopic matter following the discovery of quantum mechanics. The pace would be much less without a continually evolving market for the succession of smaller, higher capacity products it enables. It is not at all clear that market demand will continue to support the increasingly expensive investment in fabrication equipment needed for each new step up the exponential curve of Moore’s Law. The science is probably available to allow many more capacity doublings if markets can sustain them.

Many science commentators have described the twentieth century as the century of physics, and the twenty-first as the century of biology. We now know that this is misleading. While our struggle to understand the ultimate constituents of matter has now encompassed (apparently) everything of human scale and relevance, there are additional frontiers of physical science, such as the frontier of complexity, where physics, chemistry, materials science, biology, and mathematics all come together. This is where nanotechnology and biotechnology reside. These are huge fields that form the core of basic science policy in most developed nations. The basic science of the twenty-first century is neither biology nor physics, but an interdisciplinary mix of these and other traditional fields. Continued development of this domain contributes to information technology and much else.

I believe that the rapid advance of information technology has created unrealistic expectations about all technologies, and encouraged a casual attitude among policy makers toward the capability of science and technology to deliver solutions to difficult social problems. This is certainly true of what may be the greatest technical challenge of all time - the delivery of energy to large developed and developing populations without adding greenhouse gases to the atmosphere. The challenge of sustainable energy technology is much more difficult than many people currently seem to appreciate. Time will make this clear.

Globalisation

Structural complexities and the intrinsic dynamism of science and technology pose challenges to policy makers, but they seem almost manageable compared to the challenges posed by extrinsic forces. Among these are globalisation and the impact of global economic development on the environment. The latter, often called sustainability, is likely to be part of much twenty-first century innovation policy. Measures of development, competitiveness, and innovation need to include sustainability dimensions to be realistic over the long term. Sustainability is an international issue because the scale of development and the globalisation of economies have environmental and natural-resource implications that transcend national borders.

Globalisation is not new. Science has been global for centuries and we ought to be studying it more closely as a model for effective responses to the globalisation of our economies. What is striking about science is its strong imperative to share ideas through every conceivable channel to the widest possible audience. If you had to name one key characteristic of science it would be empiricism. If you had to name two, the other would be the open communication of data and ideas. The power of open communication in science cannot be overestimated. It has established, uniquely among human endeavours, an absolute global standard. And it effectively recruits talent from every part of the globe to labour at the frontiers of science. Science is the ultimate example of an open innovation system.

Science practice has received much attention from philosophers, social scientists, and historians during the past half-century, and some of what has been learned holds valuable lessons for policy-makers. It is fascinating how quickly countries that provide avenues to advanced education are able to participate in world science. The barriers to a small but productive scientific activity appear to be quite low and whether or not a country participates in science appears to be discretionary. A small scientific establishment, however, will not have significant direct economic impact. Its value at the early stages of development is indirect, bringing higher performance standards, international recognition, and peer role models for a wider population. A science program of any size is also a link to the rich intellectual resources of the world scientific community. The indirect benefit of scientific research to a developing country far exceeds its direct benefit, and policy needs to recognise this. It is counterproductive to base support for science in such countries on a hoped-for direct economic stimulus.

Keeping in mind that the innovation ecology includes far more than science and technology, it should be obvious that within a small national economy, innovation can thrive on a very small indigenous science and technology base. But innovators, like scientists, require access to technical information and ideas. Policies that favour innovation will enable access to education and encourage free communication with the world technical community. Anything that encourages awareness of the marketplace and all its actors on every scale will also encourage innovation.

This brings me back to John Kao’s definition of innovation. His vision of “the ability of individuals, companies, and entire nations to continuously create their desired future” implies conditions that create that ability, including most importantly educational opportunity. The notion that “innovation depends on harvesting knowledge from a range of disciplines besides science and technology” implies that innovators must know enough to recognise useful knowledge when they see it. It also implies that they have access to knowledge sources that range from news media and the Internet to technical and trade conferences. If innovation truly “flows from shifts in mind-set that can generate new business models, recognise new opportunities, and weave innovations throughout the fabric of society”, then the fabric of society must be loose-knit enough to accommodate new ideas. Innovation is about risk and change, and deep forces in every society resist these. A striking feature of the US innovation ecology is the positive attitude toward failure, which encourages risk-taking and entrepreneurship.

Policy implications

All this gives us insight into the policies we need to encourage innovation. Innovation policy is broader than science and technology policy, but must be consistent with it to produce a healthy innovation ecology. Innovation requires a predictable social structure, an open marketplace, and a business culture amenable to risk and change. It requires an educational infrastructure that produces people with a global awareness and sufficient technical literacy to harvest the fruits of current technology. What innovation does not require is the creation by governments of a system that defines, regulates, or even rewards innovation except through the marketplace or in response to evident success. Some regulation of new products and ideas is required to protect public health and environmental quality, but innovation needs lots of freedom. Innovative ideas that do not work out should be allowed to die so the innovation community can learn from the experience and replace the failed attempt with something better.

Do we understand innovation well enough to develop policy for it? If the policy addresses very general infrastructure issues such as education, economic and political stability and the like, the answer is perhaps. If we want to measure the impact of specific programs on innovation, the answer is no. Studies of innovation are at an early stage where anecdotal information and case studies are probably the most useful tools for policymakers.

We should also pay more attention to the science of science policy - the systematic quantitative study of the subset of our economy called science and technology - including the construction and validation of micro- and macro-economic models for science and technology activity. The OECD has been a valuable player in this enterprise, and can do much by its example to encourage deeper knowledge of the innovation ecology and thus provide better tools for policy makers. The deep effort the OECD is making to gather information about innovation is a welcome and valuable enterprise that must continue over a long period to be successful. Innovators and those who finance them need to identify their needs and the impediments they face. Eventually we may learn enough to create reliable indicators by which we can judge the health of our innovation ecosystems. The goal is well worth the sustained effort that will be required to achieve it.

Adapted from a keynote speech given at the OECD high-level meeting of the Committee for Scientific and Technological Policy, held in Oslo, Norway, 4 March 2008

John Marburger

Director of the Office of Science & Technology Policy