I told her that I know one or two people (to put it mildly) who might have some opinions in this area. But rather than just send her a bunch of names and papers, I thought I would try to get an online conversation going here on Starclouds, and see if we could do some collective brainstorming.

So—consider this a call for comments. Is our innovation/competitiveness policy on the right course? Are officials even framing the issues in the right way? And if not, what directions should we be going?

Remember-specific, concrete, practical…

The issue looks fascinating, and I look forward to reading it. In the meantime, here is an official blurb about Innovations:

Innovations: People Using Technology to Address Global Challenges

http://mitpress.mit.edu/innovations/ [free content and subscriptions]

Innovations is a journal for, and about, people using technology and novel modes of organization to address global challenges. The journal was launched in the Winter of 2006 as a publication of MIT Press, jointly hosted at Harvard?s Kennedy School of Government (Belfer Center for Science and International Affairs) and George Mason University’s School of Public Policy (Center for Science and Technology Policy). Philip Auerswald and Iqbal Quadir serve as the journal?s co-editors; John Holdren is the chair of the journal’s advisory board. The leadership of the journal is shared and supported by an international editorial board, with guidance from an advisory board whose members (in addition to Holdren) include two former U.S. Presidential Science Advisors, a former NASA Administrator, the chief counsel on the House Science Committee, the publisher of FOREIGN AFFAIRS, and R.K. Pachauri, co-recipient (on behalf of the IPCC) of the 2007 Nobel Peace Prize.

Well, I don’t know how “unexplored” they are, but here are the three questions I sent her:

• Is there really a “ Gathering Storm?” Is the U.S. really falling behind in science and engineering, as so many politicians and reports allege? Figures suggest otherwise. If anything, there is a glut of scientists and engineers in most fields. So the strenuous efforts being undertaken to increase their number may be misguided, and divert attention from more serious problems with American S&E. Leonard Lynn and Hal Salzman at the Urban Institute in Washington have been taking a hard look at this question, and have a long paper coming out within a day or two.
• Do science and technology suffer from a “tragedy of the anti-commons?” Does the patent system, which is supposed to reward innovation, actually get in the way of innovation? In some areas, notably biomedical research, it arguably does just that. Innovation thrives on the free flow of knowledge, which allows researchers to build upon one another’s accomplishments. But in practice, their obsession with priority, publication and patents can create an atmosphere of secrecy and mistrust that greatly hampers that flow. One of the many economists who have worried about this issue is Stephen Maurer at UC-Berkeley.
• Is science really the bedrock of economic performance that it’s claimed to be? The answer is yes-and no. The money being poured into research is definitely necessary for growth and competitiveness. But it’s far from being sufficient-a point that decision-makers often miss, in both the public and private sectors. Just as critical is a host of intangible factors: a company’s (or a society’s) ability to attract and retain good people; to create a supportive climate for innovation; to recognize and seize opportunities when they arise; to enter into fruitful alliances and partnerships-on and on. An economist who has thought a great deal about these intangible factors-and the implications for policy-is Kenan Patrick Jarboe, President of the Athena Alliance here in DC. Also relevant to that last point about alliances and partnership is a recent paper by Lynn and Salzman, Collaborative Advantage.

Comments?

To hear the organizers tell it, the need for such a refuge is even more acute in Europe than it is here. Not only are the disciplines and sub-disciplines even more balkanized and the universities even more bureaucratic, but the national rivalries and language barriers are still very real. Thus IPL’s double-duty, multi-language name: “para limes” is a Greek-Latin hybrid for “across borders.”

IPL was organized in 2004 with physicist Jan Wouter Vasbinder as director, and a list of “founding fathers” that includes five European Nobel laureates. It has gotten enough funding from the Dutch government and elsewhere to start restoration work on a permanent home: a 14^th-century monastery in the town of Doesburg, in central Holland. It has held a series of workshops on several broad research themes, including Genes, Infections and Epidemics, Complexity, Evolution and Learning, Critical Transitions in Complex Systems, and Conceptual Neuroscience. (Another workshop on Robustness is planned for this autumn.) And now it has held this week’s conference. Science without boundaries (October 7-10), had 70 participants, including SFI stalwarts Brian Arthur and John Holland, as well as SFI president Geoffrey West and two Nobelists: biologist Sidney Brenner and physicist Gerard ‘t Hooft. The goal was to review what had come out of the smaller workshops, reach some kind of consensus on IPL’s future agenda-and, not incidentally, introduce IPL to the wider world.

I can only wish them well-and not just because rural Holland was so lovely and IPL such a gracious host. The world needs many more places where people can grapple with problems from every perspective at once. The institute has made an impressive start. But the challenge, as always, is money: IPL will need a lot more of it, public and private, to succeed. Here’s hoping they can find it.

I can’t just post the chapter whole, because it’s copyrighted to the Brookings Institution Press. But basically I make two points:

Innovation doesn’t just happen.

As often as not, they originate from very specific efforts to solve immediate, practical problems. It’s only later , when the innovators are really immersed in the thing, that they begin to appreciate the visionary implications. To quote from the chapter:

Back in the 1920s and 1930s, for example, academics tended to be rather contemptuous of raw number crunching, on the theory that a real mathematician or scientist gains insight by abstract reasoning, not reckoning. Slide rules were acceptable, for engineers. But brute number-crunching was just arithmetic, a task for desktop adding machines-women’s work. (The word “computer” was still a job description in the 1920s, and had much the same pink-collar connotation as “typist.”) The building of computing machinery was, by extension, a job for mere tinkerers.

As a result, the road to modern electronic computing began with a handful of very practical pioneers. They were motivated in large part by desperation: modern technology was already beginning to demand calculations on a scale that humans could not manage, even with adding machines.

A prime example is Vannevar Bush,

who orchestrated the Manhattan Project and all the rest of nation’s war-related scientific research during World War II. Bush is probably best known today for his 1945 article about the “Memex,” a hypothetical knowledge-access tool that could link one concept to the next in a manner that anticipated the World Wide Web by nearly half a century. [The Memex story is told in a wonderful book, From Memex To Hypertext.]

But the path that led Bush to the Memex began more than 20 years earlier, when he was an MIT electrical engineering professor trying to analyze the instability of electric power networks. The computers he created were mechanical, analog machines (as the Memex would have been). But for the class of problems they were meant to solve, they were the most powerful calculating machines on the planet in the 1930s.

Other examples:

• The digital, all-electronic ENIAC, widely regarded as the first modern computer, was constructed by engineers at the University of Pennsylvania to calculate artillery trajectories.
• The Hungarian-born mathematician John von Neumann, who pioneered numerical processing and scientific supercomputing in the late 1940s, first became interested in the subject because he was a participant in the super-secret Manhattan Project, and was looking for computing machines that could help out with the horrendous calculations needed in that effort.
• Claude Shannon, the creator of modern information theory, was an AT&T Bell Labs employee who was originally trying to quantify the communications capacity of telephone networks.
• The MIT mathematician Norbert Wiener, who combined information theory, computing, and feedback control in his highly influential 1948 book Cybernetics -a word he coined, and the origin of today’s ubiquitous “cyber” prefix-was originally inspired by his World War II work on the control of anti-aircraft artillery.

The list could go on and on. Tim Berners-Lee originally created the World Wide Web as an easier way for his colleagues at the CERN particle accelerator facility to access online scientific documents. The Google search algorithm started as a grad student project funded under a federal research program on search and retrieval for digital libraries. Facebook was originally just a way for undergraduates at one school-Harvard-to keep track of one another.

I certainly don’t want to claim that innovation always grows out of efforts to solve practical problems; I’m still a huge fan of basic, blue-sky research. (My Ph.D. was in elementary particle physics, which is about as blue-sky as you can get.) But from a policy perspective, this is yet another vote for robust funding of research in Pasteurs Quadrant.

Innovations don’t happen in isolation.

To quote from the chapter again:

They almost never involve just a single idea, but the convergence of many ideas. And they are not inevitable: they result from social needs and interactions. The modern digital computer, in particular, required the convergence of at least half a dozen innovations-most involving not just another gadget, but a shift in the way people thought about computing. Well into the 1940s, moreover, people were struggling to fit the pieces together in the right way; it took a decade of trial and error (and a war) to get a combination that was workable. Among the most important of these pieces:

Digital computing: solving problems by numerical calculation as opposed to building a physical model of the problem. It was far from obvious in the beginning that digital was the right way to go, especially given the success of analog machines such as Bush’s Differential Analyzer.

Binary mathematics, as opposed to the base-10 arithmetic that humans had been using since they first began counting on their fingers.

Logic: the recognition that a simple on-off switch could embody the notions of true and false, and that a network of such switches could embody all the standard operations of Boolean logic-the operations of binary arithmetic among them. In particular, the network could make comparisons, and thus take alternative courses of action according to circumstances-as in, “If the number X equals the number Y, then do operations P, Q, and R.” That ability, in turn, was what made the digital computer so much more than an ultra-fast adding machine. A switching circuit could add and subtract-but it could also decide. It could work its way through a sequence of such decisions automatically. In a word, it could be programmed.

All-electronic switching, using vacuum tubes for speed as opposed to mechanical switches. Again, the choice of vacuum tubes was far from obvious in the early days, given that a computer would need tens of thousands of them to do anything useful, and that even a single burnt-out tube could bring the system to a halt. How would you ever finish a calculation?

Program control-giving computers the power to carry out long sequences of operations on their own, as opposed to relying on a human operator to press the buttons, watch the meters, load and unload the punch cards, and generally intervene at every step.

Stored program control-that is, storing the program as binary code in the computer’s memory, as opposed to reading it in each time from punch cards or paper tape. Once again, the usefulness of this approach was not entirely obvious at the beginning; many of the early computers, including the ENIAC, had at least some of their programming wired into their physical structure. Implementing the stored-program approach was also a good deal harder than it sounds today, given the primitive state of memory technology at the time; no one was able to field a working stored-program computer until the late 1940s. But the stored-program approach had the obvious advantage of convenience: once all the instructions were stored electronically, so that the problem-solving sequence was entirely separate from the hardware, you could change the function of the computer without having to touch the wiring. Or to put it another way, the act of computation had become an abstraction embodied in what we now know as software.

The history of information technology offers many other examples of invention-by-convergence. Among them:

• The modern concept of information and information processing was a synthesis of insights developed in the 1930s and 1940s by Alan Turing, Claude Shannon, Norbert Wiener, Warren McCulloch, Walter Pitts and John von Neumann.
• The hobbyists who sparked the personal computer revolution in the late 1970s were operating (consciously or not) in the context of ideas that had been around for a decade or more. There was the notion of interactive computing, for example, in which a computer would respond to the user’s input immediately (as opposed to generating a stack of fan-fold printout hours later); this idea dated back to the Whirlwind project, an experiment in real-time computing that began at MIT in the 1940s. There were the twin notions of individually controlled computing (having a computer apparently under the control of a single user) and home computing (having a computer in your own house); both emerged in the 1960s from MIT’s Project MAC, an early experiment in time-sharing. And then there was the notion of a computer as an open system, meaning that a user could modify it, add to it and upgrade it however he or she wanted; that practice was already standard in the minicomputer market, which was pioneered by the Digital Equipment Corporation in the 1960s.
• The Internet as we know it today represents the convergence of (among other ideas) the notion of packet-switched networking from the 1960s; the notion of internetworking (as embodied in the TCP/IP protocol), which was developed in the 1970s to allow packets to pass between different network; and the notion of hypertext-which, of course, goes back to Vannevar Bush’s article on the Memex in 1945.

I think this point is quite generally true about innovation: it’s not just about individual gadgets, but how multiple gadgets-and ideas-are integrated into coherent systems. This is hardly an original insight-but one that a lot of people seem to miss, so it’s worth making again.

From a policy perspective, it argues for doing everything possible to promote the open exchange of knowledge, so that innovators can combine and reuse that knowledge in ways that are impossible to predict. But of course, it also leads to a long and complex debate over the limits to that openness: what’s the right balance with intellectual property protection, national security and the like, all of which act to restrict the free flow of information?

Comments?

I mention this because one of the chapters is mine: I use a lot of examples from computer history to explain why it’s hard to do forecasting even for IT, where you’d expect things like Moore’s Law to make it easy. But I’m actually going to save that discussion for a later post. My immediate concern is that this chapter was actually just the middle part of a longer paper (and longer conference presentation) about the perils of technological forecasting in general, which I did in collaboration with Caroline Wagner at George Washington University. Not surprisingly, I think it contains some pretty good stuff. Here’s the original summary passage:

We define the notion of deep uncertainty, which makes prediction effectively impossible in most cases. We explain why that’s OK-because an effort to explore the future can give you a great deal, even without prediction. And then we explore how we can still confront the future quite effectively by embracing deep uncertainty.

Unfortunately, for reasons too mundane to bear repeating, everything besides that central section wound up on the cutting room floor. So, with Caroline’s permission, I’d like to rescue those portions here. Enjoy.

Embracing Deep Uncertainty

By Caroline Wagner and M. Mitchell Waldrop

Never has our species been more obsessed with the future. For most of human history, after all, change was imperceptible: our ancestors lived by the endlessly repeating cycle of the seasons, in much the way their own ancestors had. But today, in an age of rampant globalization and technological ferment, change is constant, unpredictable-and often, it seems, beyond anyone’s control.

Thus our fascination with formal methods of planning and forecasting, with their implicit promise to reduce the uncertainty and give us at least some control over our future.

Given their track record, unfortunately, it is not at all clear that these methods can deliver on that promise. Their roots go back to the late 1940s, when the first wave of enthusiasm for programmed planning and technology forecasting was at least partly inspired by America’s spectacular success in World War II, both on the battlefield and in the laboratory. Indeed, the decades immediately following were a period of striking technocratic optimism in the United States-or striking technocratic hubris, depending on one’s point of view. (See, for example, Robert J. Samuelson’s book, The Good Life and its Discontents.) Rapid advances in economic theory, game theory, social science, and business practices convinced a generation of intellectuals that the world could be rationalized, mathematized, reasoned about, and managed. And in the heady afterglow of victory overseas, followed by years of post-war prosperity at home, that conviction was widely embraced in both government and industry. We now had the tools to maintain the Cold War balance of power, tame the business cycle, end poverty, ensure prosperity, and even suppress insurgencies in far-off lands. In particular, thanks to a variety of forecasting methods, we could now anticipate the direction of technology development in plenty of time to head off any deleterious social impacts.

Things didn’t work out that way, to put it mildly. After chaotic experiences such as Vietnam, globalization, the AIDS epidemic, the unexpected fall of communism and the rise of international terrorism-not to mention the continued existence of poverty-the world has come to seem like a far messier and more unruly place than it did in the post-war years. Messiness has proved to be the norm even in the seemingly clear-cut world of technology forecasting. Mid-century predictions about the year 2000 included assertions that nuclear power would make energy that was too cheap to meter, that the U.S.S.R. would be a dominant superpower, and that the university would replace the firm as the institution most central to economic growth-none of which came to pass. They also included the computer models described in the Club of Rome’s 1972 book, Limits to Growth, which famously painted such a grim picture of future starvation that some pundits were led to consider triage of huge swaths of the human population who were doomed anyway. That hasn’t come to pass, either (yet.) Virtually no one foresaw the coming of interconnected personal computers, the green revolution, climate change, wireless networking or globalization-or the lack of progress in energy. And the few people who did stumble over these possibilities may have been lucky rather than prescient.

The accuracy of the predictions hasn’t improved much since then the 1970s, despite any number of studies, books and methodological developments. To put it most simply, technological forecasting has failed to anticipate the future in any reliable way because it uses linear logic to understand a highly nonlinear process. Or to put it a slightly different way, the forecasters have failed to embrace deep uncertainty.

Ordinary uncertainty is the kind we get in, say, a weather report: “There’s a 30% chance of rain tomorrow.” Many things about the future are unknown, but at least they are known unknowns. We have a good idea of where the uncertainties lie, and a framework for thinking about them.

Deep uncertainty, a term coined in 2001 by Steven Popper of the Rand Corporation, is when we don’t even have the concept of “rain,” or “tomorrow.” The future is a fog of unknown unknowns. We can’t begin to agree on what our mental framework ought to be, or what the probabilities are, or whether a given outcome is good or bad. And even if we could, the real world is rife with complexity-meaning not just “complicated,” but full of non-linear and emergent phenomena that make long-range prediction all but impossible. (”For want of a nail, the shoe was lost…”)

This might sound like a counsel of despair for anyone trying to anticipate the future-which, at some level, is all of us. But in fact, the lesson is not that we should give up. Quite the opposite: foresight is essential, and deserving of constant effort. The lesson is that we should give up any notion that foresight will help us predict and control the future. Instead, we should embrace deep uncertainty. Accept the fact that we’re always going to be exploring our way into the future-and that we should value foresight for what can give us even without prediction.

For example, our efforts to anticipate the future can help us-

• Assess the evolving situation and identify critical factors;
• Think through the significance of those factors;
• Reveal and challenge hidden assumptions;
• Identify the warning signs (or hopeful signs) to look out for;
• Think through our alternatives for response, both tactical and strategic;
• Identify and begin to build a network of experts, partners and allies.

Indeed, by giving up on prediction as the goal, our efforts to look at the future can open the way to a different, and arguably more fruitful, approach to dealing with deep uncertainty. Some principles:

Maintain Eternal Vigilance. Foresight shouldn’t be seen as just a one-time effort, nor should the process be treated as a black box that delivers The Answer. Foresight should be an on-going process, with predictions that are constantly updated, reevaluated, and reassessed. A good model is what psychologists call “situation awareness,” an individual’s evolving picture of his or her surroundings. According to one commonly accepted definition, situation awareness involves three levels: perceiving the critical factors in the environment; understanding what those factors mean, particularly in relation to the decision maker’s goals; and projecting what is likely to happen in the near future. All three levels have to be maintained constantly if we’re to function in a timely and effective manner.

Minimize Potential Regret. Foresight shouldn’t be seen as a way to anticipate the most likely future, or set of futures, as the basis of planning, which is how decision-makers typically use it today. It should be seen as a way to avoid entrapment, by choosing strategies that are adaptive and robust over a broad range of scenarios. What policy courses are available to us, and how do they fare under many alternative futures? What are their failure modes? What are the warning signs? And how can we maximize our ability to respond to the unfolding of events?

The good news here is that information technology, computing, and modeling have greatly advanced since the early days of forecasting, as has our understanding of human decision-making, complexity, and social organization. The result is a new generation of tools that can help for more insightful and robust analysis. One example is the Computer-Assisted Reasoning system. Developed by Lempert, Bankes, and Popper, and described in the RAND report Shaping the Next One Hundred Years, it allows users to map hundreds of possible futures against a landscape of desirable and undesirable outcomes.

Seek Collaborative Advantage. In a world governed by complexity and deep uncertainty, no organization (or nation) can ever be big enough and strong enough to go it alone all the time. Sooner or later, the organization will need more resources, information and expertise than it has in-house. And to get them, it will need to build alliances, coalitions, and partnerships-that is, to seek collaborative advantage rather than competitive advantage.

This is certainly the case when it comes to assessing the future, where there is always another perspective that could prove vital. Indeed, this “million minds” approach is deeply related to how the scientific community makes progress, and how other open systems move towards consensus. Entertaining multiple points of view and multiple forms of expertise not only helps expose error, but also can enable groups to pose questions that the experts never would have thought to include. It also helps us focus on the actions we might actually take, rather than on the outcome of events we cannot control.

Forecasting has been dogged by lack of precision in predicting the future, foiled by the very complexity it tried to contain. But there is great social value in the very process of pulling people together, and engaging in a joint discussion of what actions taken today might increase positive outcomes in the future. Given the challenges facing 21^st century societies, attaching greater value to the wisdom of the crowd may be a prudent direction to take. This could take place in a series of town meetings that combine human reasoning with computer technology to ask what we hope the world will look like in the future, and to recommend policy steps that could create a world we would want to live in. Visions of positive outcomes in the future are critical to social development. As the old saying goes, if you don’t know where you’re going, you’ll probably get there.