it is not quite true, even among tenured professors in the humanities, that idea-mongers can entirely ignore "external" checks. Even academics want to be respectable, which means they can't entirely ignore the realities that others notice. There were lots of academics talking about the achievements of socialism in the 1970s (I can remember them) but very few talking that way after China and Russia repudiated these fantasies.

THE MOST DISTORTING ASPECT of Sowell's account is that, in focusing so much on the delusions of intellectuals, he leaves us more confused about what motivates the rest of society. In a characteristic passage, Sowell protests that "intellectuals...have sought to replace the groups into which people have sorted themselves with groupings created and imposed by the intelligentsia. Ties of family, religion, and patriotism, for example, have long been rated as suspect or detrimental by the intelligentsia, and new ties that intellectuals have created, such as class -- and more recently 'gender' -- have been projected as either more real or more important."

There's no disputing the claim that most "intellectuals" -- surely most professors in the humanities-are down on "patriotism" and "religion" and probably even "family." But how did people get to be patriotic and religious in the first place? In Sowell's account, they just "sorted themselves" -- as if by the invisible hand of the market.

Let's put aside all the violence and intimidation that went into building so many nations and so many faiths in the past. What is it, even today, that makes people revere this country (or some other); what makes people adhere to a particular faith or church? Don't inspiring words often move people? And those who arrange these words -- aren't they doing something similar to what Sowell says intellectuals do? Is it really true, when it comes to embracing national or religious loyalties, that "the proof of the pudding is in the eating"?

Even when it comes to commercial products, people don't always want to be guided by mundane considerations of reliable performance. People like glamour, prestige, associations between the product and things they otherwise admire. That's why companies spend so much on advertising. And that's part of the reason people are willing to pay more for brand names -- to enjoy the associations generated by advertising. Even advertising plays on assumptions about what is admirable and enticing-assumptions that may change from decade to decade, as background opinions change. How many products now flaunt themselves as "green" -- and how many did so 20 years ago?

If we closed down universities and stopped subsidizing intellectual publications, would people really judge every proposed policy by external results? Intellectuals tend to see what they expect to see, as Sowell's examples show -- but that's true of almost everyone. We have background notions about how the world works that help us make sense of what we experience. We might have distorted and confused notions, but we don't just perceive isolated facts. People can improve in their understanding, developing background understandings that are more defined or more reliable. That's part of what makes people interested in the ideas of intellectuals -- the hope of improving their own understanding.

On Sowell's account, we wouldn't need the contributions of a Friedrich Hayek -- or a Thomas Sowell -- if we didn't have so many intellectuals peddling so many wrong-headed ideas. But the wealthier the society, the more it liberates individuals to make different choices and the more it can afford to indulge even wasteful or foolish choices. I'd say that means not that we have less need of intellectuals, but more need of better ones. 

It’s amazing that the myth of the lone genius has persisted for so long, since simultaneous invention has always been the norm, not the exception. Anthropologists have shown that the same inventions tended to crop up in prehistory at roughly similar times, in roughly the same order, among cultures on different continents that couldn’t possibly have contacted one another.

Also, there’s a related myth—that innovation comes primarily from the profit motive, from the competitive pressures of a market society. If you look at history, innovation doesn’t come just from giving people incentives; it comes from creating environments where their ideas can connect.

The musician Brian Eno invented a wonderful word to describe this phenomenon: scenius. We normally think of innovators as independent geniuses, but Eno’s point is that innovation comes from social scenes,from passionate and connected groups of people.

It turns out that the lone genius entrepreneur has always been a rarity—there’s far more innovation coming out of open, nonmarket networks than we tend to assume.

Really, we should think of ideas as connections,in our brains and among people. Ideas aren’t self-contained things; they’re more like ecologies and networks. They travel in clusters.

ideas are networks

In part, that’s because ideas that leap too far ahead are almost never implemented—they aren’t even valuable. People can absorb only one advance, one small hop, at a time. Gregor Mendel’s ideas about genetics, for example: He formulated them in 1865, but they were ignored for 35 years because they were too advanced. Nobody could incorporate them. Then, when the collective mind was ready and his idea was only one hop away, three different scientists independently rediscovered his work within roughly a year of one another.

Charles Babbage is another great case study. His “analytical engine,” which he started designing in the 1830s, was an incredibly detailed vision of what would become the modern computer, with a CPU, RAM, and so on. But it couldn’t possibly have been built at the time, and his ideas had to be rediscovered a hundred years later.

I think there are a lot of ideas today that are ahead of their time. Human cloning, autopilot cars, patent-free law—all are close technically but too many steps ahead culturally. Innovating is about more than just having the idea yourself; you also have to bring everyone else to where your idea is. And that becomes really difficult if you’re too many steps ahead.

The scientist Stuart Kauffman calls this the “adjacent possible.” At any given moment in evolution—of life, of natural systems, or of cultural systems—there’s a space of possibility that surrounds any current configuration of things. Change happens when you take that configuration and arrange it in a new way. But there are limits to how much you can change in a single move.

Which is why the great inventions are usually those that take the smallest possible step to unleash the most change. That was the difference between Tim Berners-Lee’s successful HTML code and Ted Nelson’s abortive Xanadu project. Both tried to jump into the same general space—a networked hypertext—but Tim’s approach did it with a dumb half-step, while Ted’s earlier, more elegant design required that everyone take five steps all at once.

Also, the steps have to be taken in the right order. You can’t invent the Internet and then the digital computer. This is true of life as well. The building blocks of DNA had to be in place before evolution could build more complex things. One of the key ideas I’ve gotten from you, by the way—when I read your book Out of Control in grad school—is this continuity between biological and technological systems.

technology is something that can give meaning to our lives, particularly in a secular world.

He had this bleak, soul-sucking vision of technology as an autonomous force for evil. You also present technology as a sort of autonomous force—as wanting something, over the long course of its evolution—but it’s a more balanced and ultimately positive vision, which I find much more appealing than the alternative.

As I started thinking about the history of technology, there did seem to be a sense in which, during any given period, lots of innovations were in the air, as it were. They came simultaneously. It appeared as if they wanted to happen. I should hasten to add that it’s not a conscious agency; it’s a lower form, something like the way an organism or bacterium can be said to have certain tendencies, certain trends, certain urges. But it’s an agency nevertheless.

technology wants increasing diversity—which is what I think also happens in biological systems, as the adjacent possible becomes larger with each innovation. As tech critics, I think we have to keep this in mind, because when you expand the diversity of a system, that leads to an increase in great things and an increase in crap.

the idea that the most creative environments allow for repeated failure.

And for wastes of time and resources. If you knew nothing about the Internet and were trying to figure it out from the data, you would reasonably conclude that it was designed for the transmission of spam and porn. And yet at the same time, there’s more amazing stuff available to us than ever before, thanks to the Internet.

To create something great, you need the means to make a lot of really bad crap. Another example is spectrum. One reason we have this great explosion of innovation in wireless right now is that the US deregulated spectrum. Before that, spectrum was something too precious to be wasted on silliness. But when you deregulate—and say, OK, now waste it—then you get Wi-Fi.

If we didn’t have genetic mutations, we wouldn’t have us. You need error to open the door to the adjacent possible.

image of the coral reef as a metaphor for where innovation comes from. So what, today, are some of the most reeflike places in the technological realm?

Twitter—not to see what people are having for breakfast, of course, but to see what people are talking about, the links to articles and posts that they’re passing along.

second example of an information coral reef, and maybe the less predictable one, is the university system. As much as we sometimes roll our eyes at the ivory-tower isolation of universities, they continue to serve as remarkable engines of innovation.

Life seems to gravitate toward these complex states where there’s just enough disorder to create new things. There’s a rate of mutation just high enough to let interesting new innovations happen, but not so many mutations that every new generation dies off immediately.

, technology is an extension of life. Both life and technology are faces of the same larger system.

What, then, is the role of public (government) funding of research? Primarily, Wade argues (and I agree), to provide infrastructure for expensive research programs, such as building large colliders.

the more the government invests in basic science and infrastructure, the more winners will emerge that private industry can then capitalize on. This is a good way to build a competitive dynamic economy.

But there is a pitfall – prematurely picking winners and losers. Wade give the example of California investing specifically into developing stem cell treatments. He argues that stem cells, while promising, do not hold a guarantee of eventual success, and perhaps there are other technologies that will work and are being neglected. The history of science and technology has clearly demonstrated that it is wickedly difficult to predict the future (and all those who try are destined to be mocked by future generations with the benefit of perfect hindsight). Prematurely committing to one technology therefore contains a high risk of wasting a great deal of limited resources, and missing other perhaps more fruitful opportunities.

The underlying concept is that science research is a long-term game. Many avenues of research will not pan out, and those that do will take time to inspire specific applications. The media, however, likes catchy headlines. That means when they are reporting on basic science research journalists ask themselves – why should people care? What is the application of this that the average person can relate to? This seems reasonable from a journalistic point of view, but with basic science reporting it leads to wild speculation about a distant possible future application. The public is then left with the impression that we are on the verge of curing the common cold or cancer, or developing invisibility cloaks or flying cars, or replacing organs and having household robot servants. When a few years go by and we don’t have our personal android butlers, the public then thinks that the basic science was a bust, when in fact there was never a reasonable expectation that it would lead to a specific application anytime soon. But it still may be on track for interesting applications in a decade or two.

this also means that the government, generally, should not be in the game of picking winners an losers – putting their thumb on the scale, as it were. Rather, they will get the most bang for the research buck if they simply invest in science infrastructure, and also fund scientists in broad areas.

The same is true of technology – don’t pick winners and losers. The much-hyped “hydrogen economy” comes to mind. Let industry and the free market sort out what will work. If you have to invest in infrastructure before a technology is mature, then at least hedge your bets and keep funding flexible. Fund “alternative fuel” as a general category, and reassess on a regular basis how funds should be allocated. But don’t get too specific.

Funding research but leaving the details to scientists may be optimal

The scientific community can do their part by getting better at communicating with the media and the public. Try to avoid the temptation to overhype your own research, just because it is the most interesting thing in the world to you personally and you feel hype will help your funding. Don’t make it easy for the media to sensationalize your research – you should be the ones trying to hold back the reigns. Perhaps this is too much to hope for – market forces conspire too much to promote sensationalism.

In my post, I mentioned some caveats. Wolfe-Simon isolated the arsenic-loving strain, known as GFAJ-1, by growing Mono Lake bacteria in ever-increasing concentrations of arsenic while diluting out the phosphorus. It is possible that the bacteria’s arsenic molecules were an adaptation to the harsh environments within the experiment, rather than Mono Lake itself. More importantly, there were still detectable levels of phosphorus left in the cells at the end of the experiment, although Wolfe-Simon claimed that the bacteria shouldn’t have been able to grow on such small amounts.

signs emerged that NASA weren’t going to engage with the criticisms. Dwayne Brown, their senior public affairs officer, highlighted the fact that the paper was published in one of the “most prestigious scientific journals” and deemed it inappropriate to debate the science using the same media and bloggers who they relied on for press coverage of the science. Wolfe-Simon herself tweeted that “discussion about scientific details MUST be within a scientific venue so that we can come back to the public with a unified understanding.”

Jonathan Eisen says that “they carried out science by press release and press conference” and “are now hypocritical if they say that the only response should be in the scientific literature.” David Dobbs calls the attitude “a return to pre-Enlightenment thinking”, and rightly noted that “Rosie Redfield is a peer, and her blog is peer review”.

Chris Rowan agreed, saying that what happens after publication is what he considers to be “real peer review”. Rowan said, “The pre-publication stuff is just a quality filter, a check that the paper is not obviously wrong – and an imperfect filter at that. The real test is what happens in the months and years after publication.”Grant Jacobs and others post similar thoughts, while Nature and the Columbia Journalism Review both cover the fracas.

Jack Gilbert at the University of Chicago said that impatient though he is, peer-reviewed journals are the proper forum for criticism. Others were not so kind. At the Guardian, Martin Robbins says that “at almost every stage of this story the actors involved were collapsing under the weight of their own slavish obedience to a fundamentally broken… well… ’system’” And Ivan Oransky noted that NASA failed to follow its own code of conduct when announcing the study.

Dr Isis said, “If question remains about the voracity of these authors findings, then the only thing that is going to answer that doubt is data.  Data cannot be generated by blog discussion… Talking about digging a ditch never got it dug.”

it is astonishing how quickly these events unfolded and the sheer number of bloggers and media outlets that became involved in the criticism. This is indeed a brave new world, and one in which we are all the infamous Third Reviewer.

I tried to quell the hype around the study as best I could. I had the paper and I think that what I wrote was a fair representation of it. But, of course, that’s not necessarily enough. I’ve argued before that journalists should not be merely messengers – we should make the best possible efforts to cut through what’s being said in an attempt to uncover what’s actually true. Arguably, that didn’t happen although to clarify, I am not saying that the paper is rubbish or untrue. Despite the criticisms, I want to see the authors respond in a thorough way or to see another lab attempt replicate the experiments before jumping to conclusions.

the sheer amount of negative comment indicates that I could have been more critical of the paper in my piece. Others have been supportive in suggesting that this was more egg on the face of the peer reviewers and indeed, several practicing scientists took the findings on face value, speculating about everything from the implications for chemotherapy to whether the bacteria have special viruses. The counter-argument, which I have no good retort to, is that peer review is no guarantee of quality, and that writers should be able to see through the fog of whatever topic they write about.

my response was that we should expect people to make reasonable efforts to uncover truth and be skeptical, while appreciating that people can and will make mistakes.

it comes down to this: did I do enough? I was certainly cautious. I said that “there is room for doubt” and I brought up the fact that the arsenic-loving bacteria still contain measurable levels of phosphorus. But I didn’t run the paper past other sources for comment, which I typically do it for stories that contain extraordinary claims. There was certainly plenty of time to do so here and while there were various reasons that I didn’t, the bottom line is that I could have done more. That doesn’t always help, of course, but it was an important missed step. A lesson for next time.

I do believe that it you’re going to try to hold your profession to a higher standard, you have to be honest and open when you’ve made mistakes yourself. I also think that if you cover a story that turns out to be a bit dodgy, you have a certain responsibility in covering the follow-up

A basic problem with is the embargo. Specifically that journalists get early access, while peers – other specialists in the field – do not. It means that the journalist, like yourself, can rely only on the original authors, with no way of getting other views on the findings. And it means that peers can’t write about the paper when the journalists (who, inevitably, do a positive-only coverage due to the lack of other viewpoints) do, but will be able to voice only after they’ve been able to digest the paper and formulate a response.

No, that’s not true. The embargo doens’t preclude journalists from sending papers out to other authors for review and comment. I do this a lot and I have been critical about new papers as a result, but that’s the step that I missed for this story.

narrow or biased measures of scientific achievement can lead to narrow and biased science.

Global demand for, and interest in, metrics should galvanize stakeholders — national funding agencies, scientific research organizations and publishing houses — to combine forces. They can set an agenda and foster research that establishes sound scientific metrics: grounded in theory, built with high-quality data and developed by a community with strong incentives to use them.

Scientists are often reticent to see themselves or their institutions labelled, categorized or ranked. Although happy to tag specimens as one species or another, many researchers do not like to see themselves as specimens under a microscope — they feel that their work is too complex to be evaluated in such simplistic terms. Some argue that science is unpredictable, and that any metric used to prioritize research money risks missing out on an important discovery from left field.

It is true that good metrics are difficult to develop, but this is not a reason to abandon them. Rather it should be a spur to basing their development in sound science. If we do not press harder for better metrics, we risk making poor funding decisions or sidelining good scientists.

Metrics are data driven, so developing a reliable, joined-up infrastructure is a necessary first step.

We need a concerted international effort to combine, augment and institutionalize these databases within a cohesive infrastructure.

On an international level, the issue of a unique researcher identification system is one that needs urgent attention. There are various efforts under way in the open-source and publishing communities to create unique researcher identifiers using the same principles as the Digital Object Identifier (DOI) protocol, which has become the international standard for identifying unique documents. The ORCID (Open Researcher and Contributor ID) project, for example, was launched in December 2009 by parties including Thompson Reuters and Nature Publishing Group. The engagement of international funding agencies would help to push this movement towards an international standard.

if all funding agencies used a universal template for reporting scientific achievements, it could improve data quality and reduce the burden on investigators.

Importantly, data collected for use in metrics must be open to the scientific community, so that metric calculations can be reproduced. This also allows the data to be efficiently repurposed.

As well as building an open and consistent data infrastructure, there is the added challenge of deciding what data to collect and how to use them. This is not trivial. Knowledge creation is a complex process, so perhaps alternative measures of creativity and productivity should be included in scientific metrics, such as the filing of patents, the creation of prototypes4 and even the production of YouTube videos.

Perhaps publications in these different media should be weighted differently in different fields.

There needs to be a greater focus on what these data mean, and how they can be best interpreted.

This requires the input of social scientists, rather than just those more traditionally involved in data capture, such as computer scientists.

An international data platform supported by funding agencies could include a virtual 'collaboratory', in which ideas and potential solutions can be posited and discussed. This would bring social scientists together with working natural scientists to develop metrics and test their validity through wikis, blogs and discussion groups, thus building a community of practice. Such a discussion should be open to all ideas and theories and not restricted to traditional bibliometric approaches.

Far-sighted action can ensure that metrics goes beyond identifying 'star' researchers, nations or ideas, to capturing the essence of what it means to be a good scientist.

Some European leaders, including Angela Merkel, may be backtracking fast on their commitment to nuclear power, but despite yesterday's escalation of the ongoing crisis in Fukushima, Japan, there appear to be no signs that India, China and other emerging economies will succumb to a similar backlash. For the emerging economies, overcoming poverty and insecurity of supply remain the overriding priorities of energy policy.

3. Collaboration at scale Across many economies, the number of people who undertake knowledge work has grown much more quickly than the number of production or transactions workers. Knowledge workers typically are paid more than others, so increasing their productivity is critical. As a result, there is broad interest in collaboration technologies that promise to improve these workers' efficiency and effectiveness. While the body of knowledge around the best use of such technologies is still developing, a number of companies have conducted experiments, as we see in the rapid growth rates of video and Web conferencing, expected to top 20 per cent annually during the next few years.

4. The growing ‘Internet of Things' The adoption of RFID (radio-frequency identification) and related technologies was the basis of a trend we first recognised as "expanding the frontiers of automation." But these methods are rudimentary compared with what emerges when assets themselves become elements of an information system, with the ability to capture, compute, communicate, and collaborate around information—something that has come to be known as the "Internet of Things." Embedded with sensors, actuators, and communications capabilities, such objects will soon be able to absorb and transmit information on a massive scale and, in some cases, to adapt and react to changes in the environment automatically. These "smart" assets can make processes more efficient, give products new capabilities, and spark novel business models. Auto insurers in Europe and the United States are testing these waters with offers to install sensors in customers' vehicles. The result is new pricing models that base charges for risk on driving behavior rather than on a driver's demographic characteristics. Luxury-auto manufacturers are equipping vehicles with networked sensors that can automatically take evasive action when accidents are about to happen. In medicine, sensors embedded in or worn by patients continuously report changes in health conditions to physicians, who can adjust treatments when necessary. Sensors in manufacturing lines for products as diverse as computer chips and pulp and paper take detailed readings on process conditions and automatically make adjustments to reduce waste, downtime, and costly human interventions.

5. Experimentation and big data Could the enterprise become a full-time laboratory? What if you could analyse every transaction, capture insights from every customer interaction, and didn't have to wait for months to get data from the field? What if…? Data are flooding in at rates never seen before—doubling every 18 months—as a result of greater access to customer data from public, proprietary, and purchased sources, as well as new information gathered from Web communities and newly deployed smart assets. These trends are broadly known as "big data." Technology for capturing and analysing information is widely available at ever-lower price points. But many companies are taking data use to new levels, using IT to support rigorous, constant business experimentation that guides decisions and to test new products, business models, and innovations in customer experience. In some cases, the new approaches help companies make decisions in real time. This trend has the potential to drive a radical transformation in research, innovation, and marketing.

Using experimentation and big data as essential components of management decision making requires new capabilities, as well as organisational and cultural change. Most companies are far from accessing all the available data. Some haven't even mastered the technologies needed to capture and analyse the valuable information they can access. More commonly, they don't have the right talent and processes to design experiments and extract business value from big data, which require changes in the way many executives now make decisions: trusting instincts and experience over experimentation and rigorous analysis. To get managers at all echelons to accept the value of experimentation, senior leaders must buy into a "test and learn" mind-set and then serve as role models for their teams.

6. Wiring for a sustainable world Even as regulatory frameworks continue to evolve, environmental stewardship and sustainability clearly are C-level agenda topics. What's more, sustainability is fast becoming an important corporate-performance metric—one that stakeholders, outside influencers, and even financial markets have begun to track. Information technology plays a dual role in this debate: it is both a significant source of environmental emissions and a key enabler of many strategies to mitigate environmental damage. At present, information technology's share of the world's environmental footprint is growing because of the ever-increasing demand for IT capacity and services. Electricity produced to power the world's data centers generates greenhouse gases on the scale of countries such as Argentina or the Netherlands, and these emissions could increase fourfold by 2020. McKinsey research has shown, however, that the use of IT in areas such as smart power grids, efficient buildings, and better logistics planning could eliminate five times the carbon emissions that the IT industry produces.

7. Imagining anything as a service Technology now enables companies to monitor, measure, customise, and bill for asset use at a much more fine-grained level than ever before. Asset owners can therefore create services around what have traditionally been sold as products. Business-to-business (B2B) customers like these service offerings because they allow companies to purchase units of a service and to account for them as a variable cost rather than undertake large capital investments. Consumers also like this "paying only for what you use" model, which helps them avoid large expenditures, as well as the hassles of buying and maintaining a product.

In the IT industry, the growth of "cloud computing" (accessing computer resources provided through networks rather than running software or storing data on a local computer) exemplifies this shift. Consumer acceptance of Web-based cloud services for everything from e-mail to video is of course becoming universal, and companies are following suit. Software as a service (SaaS), which enables organisations to access services such as customer relationship management, is growing at a 17 per cent annual rate. The biotechnology company Genentech, for example, uses Google Apps for e-mail and to create documents and spreadsheets, bypassing capital investments in servers and software licenses. This development has created a wave of computing capabilities delivered as a service, including infrastructure, platform, applications, and content. And vendors are competing, with innovation and new business models, to match the needs of different customers.

8. The age of the multisided business model Multisided business models create value through interactions among multiple players rather than traditional one-on-one transactions or information exchanges. In the media industry, advertising is a classic example of how these models work. Newspapers, magasines, and television stations offer content to their audiences while generating a significant portion of their revenues from third parties: advertisers. Other revenue, often through subscriptions, comes directly from consumers. More recently, this advertising-supported model has proliferated on the Internet, underwriting Web content sites, as well as services such as search and e-mail (see trend number seven, "Imagining anything as a service," earlier in this article). It is now spreading to new markets, such as enterprise software: Spiceworks offers IT-management applications to 950,000 users at no cost, while it collects advertising from B2B companies that want access to IT professionals.

9. Innovating from the bottom of the pyramid The adoption of technology is a global phenomenon, and the intensity of its usage is particularly impressive in emerging markets. Our research has shown that disruptive business models arise when technology combines with extreme market conditions, such as customer demand for very low price points, poor infrastructure, hard-to-access suppliers, and low cost curves for talent. With an economic recovery beginning to take hold in some parts of the world, high rates of growth have resumed in many developing nations, and we're seeing companies built around the new models emerging as global players. Many multinationals, meanwhile, are only starting to think about developing markets as wellsprings of technology-enabled innovation rather than as traditional manufacturing hubs.

10. Producing public good on the grid The role of governments in shaping global economic policy will expand in coming years. Technology will be an important factor in this evolution by facilitating the creation of new types of public goods while helping to manage them more effectively. This last trend is broad in scope and draws upon many of the other trends described above.

lack of regulations has meant that even prisoners can be exploited in this virtual world for profit.

The emergence of gold farming as a business in China – whether in prisons or sweatshops could raise new questions over the exporting of goods real or virtual from the country."Prison labour is still very widespread – it's just that goods travel a much more complex route to come to the US these days. And it is not illegal to export prison goods to Europe, said Nicole Kempton from the Laogai foundation, a Washington-based group which opposes the forced labour camp system in China.

Even though he believes in his method, he ignores the huge body of evidence that shows radon gas is no more reliable than any other “predictor”.

If the victims insist on suing someone, they should leave the seismologists alone and look into the construction of some of those buildings. The stories out of L’Aquila suggest that the death toll was much higher because of official corruption and shoddy construction, as happens in many countries both before and after big quakes.

much of the construction is apparently Mafia-controlled in that area—good luck suing them! Sadly, the ancient medieval buildings that crumbled were the most vulnerable because they were made of unreinforced masonry, the worst possible construction material in earthquake country

what does this imply for scientists who are working in a field that might have predictive power? In a litigious society like Italy or the U.S., this is a serious question. If a reputable seismologist does make a prediction and fails, he’s liable, because people will panic and make foolish decisions and then blame the seismologist for their losses. Now the Italian courts are saying that (despite world scientific consensus) seismologists are liable if they don’t predict quakes. They’re damned if they do, and damned if they don’t. In some societies where seismologists work hard at prediction and preparation (such as China and Japan), there is no precedent for suing scientists for doing their jobs properly, and the society and court system does not encourage people to file frivolous suits. But in litigious societies, the system is counterproductive, and stifles research that we would like to see developed. What seismologist would want to work on earthquake prediction if they can be sued? I know of many earth scientists with brilliant ideas not only about earthquake prediction but even ways to defuse earthquakes, slow down global warming, or many other incredible but risky brainstorms—but they dare not propose the idea seriously or begin to implement it for fear of being sued.