Group items tagged

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.

The MIT researchers’ paper, however, suggests that the danger of information cascades may not be as dire as it previously seemed.

a mathematical model that describes attempts by members of a social network to make binary decisions — such as which of two brands of cell phone to buy — on the basis of decisions made by their neighbors. The model assumes that for all members of the population, there is a single right decision: one of the cell phones is intrinsically better than the other. But some members of the network have bad information about which is which.

The MIT researchers analyzed the propagation of information under two different conditions. In one case, there’s a cap on how much any one person can know about the state of the world: even if one cell phone is intrinsically better than the other, no one can determine that with 100 percent certainty. In the other case, there’s no such cap. There’s debate among economists and information theorists about which of these two conditions better reflects reality, and Kleinberg suggests that the answer may vary depending on the type of information propagating through the network. But previous models had suggested that, if there is a cap, information cascades are almost inevitable.

if there’s no cap on certainty, an expanding social network will eventually converge on an accurate representation of the state of the world; that wasn’t a big surprise. But they also showed that in many common types of networks, even if there is a cap on certainty, convergence will still occur.

people in the past have looked at it using more myopic models,” says Acemoglu. “They would be averaging type of models: so my opinion is an average of the opinions of my neighbors’.” In such a model, Acemoglu says, the views of people who are “oversampled” — who are connected with a large enough number of other people — will end up distorting the conclusions of the group as a whole.

What we’re doing is looking at it in a much more game-theoretic manner, where individuals are realizing where the information comes from. So there will be some correction factor,” Acemoglu says. “If I’m seeing you, your action, and I’m seeing Munzer’s action, and I also know that there is some probability that you might have observed Munzer, then I discount his opinion appropriately, because I know that I don’t want to overweight it. And that’s the reason why, even though you have these influential agents — it might be that Munzer is everywhere, and everybody observes him — that still doesn’t create a herd on his opinion.”

the new paper leaves a few salient questions unanswered, such as how quickly the network will converge on the correct answer, and what happens when the model of agents’ knowledge becomes more complex.

the MIT researchers begin to address both questions. One paper examines rate of convergence, although Dahleh and Acemoglu note that that its results are “somewhat weaker” than those about the conditions for convergence. Another paper examines cases in which different agents make different decisions given the same information: some people might prefer one type of cell phone, others another. In such cases, “if you know the percentage of people that are of one type, it’s enough — at least in certain networks — to guarantee learning,” Dahleh says. “I don’t need to know, for every individual, whether they’re for it or against it; I just need to know that one-third of the people are for it, and two-thirds are against it.” For instance, he says, if you notice that a Chinese restaurant in your neighborhood is always half-empty, and a nearby Indian restaurant is always crowded, then information about what percentages of people prefer Chinese or Indian food will tell you which restaurant, if either, is of above-average or below-average quality.

Making the issue even more contentious, some of the more credible skeptics out there (e.g., Lindzen, Spencer) have been arguing that clouds behave quite differently from that predicted by models. In fact, they argue, clouds will stabilize the climate and prevent climate change from occurring (i.e., clouds will provide a negative feedback).

In my new paper, I calculate the energy trapped by clouds and observe how it varies as the climate warms and cools during El Nino-Southern Oscillation (ENSO) cycles. I find that, as the climate warms, clouds trap an additional 0.54±0.74W/m2 for every degree of warming. Thus, the cloud feedback is likely positive, but I cannot rule out a slight negative feedback.

while a slight negative feedback cannot be ruled out, the data do not support a negative feedback large enough to substantially cancel the well-established positive feedbacks, such as water vapor, as Lindzen and Spencer would argue.

I have also compared the results to climate models. Taken as a group, the models substantially reproduce the observations. This increases my confidence that the models are accurately simulating the variations of clouds with climate change.

Dr. Spencer is arguing that clouds are causing ENSO cycles, so the direction of causality in my analysis is incorrect and my conclusions are in error. After reading this, I initiated a cordial and useful exchange of e-mails with Dr. Spencer (you can read the full e-mail exchange here). We ultimately agreed that the fundamental disagreement between us is over what causes ENSO. Short paraphrase: Spencer: ENSO is caused by clouds. You cannot infer the response of clouds to surface temperature in such a situation. Dessler: ENSO is not caused by clouds, but is driven by internal dynamics of the ocean-atmosphere system. Clouds may amplify the warming, and that’s the cloud feedback I’m trying to measure.

My position is the mainstream one, backed up by decades of research. This mainstream theory is quite successful at simulating almost all of the aspects of ENSO. Dr. Spencer, on the other hand, is as far out of the mainstream when it comes to ENSO as he is when it comes to climate change. He is advancing here a completely new and untested theory of ENSO — based on just one figure in one of his papers (and, as I told him in one of our e-mails, there are other interpretations of those data that do not agree with his interpretation). Thus, the burden of proof is Dr. Spencer to show that his theory of causality during ENSO is correct. He is, at present, far from meeting that burden. And until Dr. Spencer satisfies this burden, I don’t think anyone can take his criticisms seriously.

This is but another example of how climate scientists are being played by the denialists. You attempted to discuss the issue with Spencer as if he were only doing science. But he is not. He is doing science and politics, and he has no compunction about sandbagging you. There is no gain to you in trying to deal with people like Spencer and Lindzen as colleagues. They are not trustworthy.

What seems obvious is sometime just wrong.  This of course is why we actually do research.  So why is it that we shouldn't make what seems to be an obvious connection between these papers and recent disasters, as so many have already done?

First, the Min et al. paper seeks to identify a GHG signal in global precipitation over the period 1950-1999.  They focus on one-day and five-day measures of precipitation.  They do not discuss streamflow or damage.  For many years, an upwards trend in precipitation has been documented, and attributed to GHGs, even back to the 1990s (I co-authored a paper on precipitation and floods in 1999 that assumed a human influence on precipitation, PDF), so I am unsure what is actually new in this paper's conclusions.

However, accepting that precipitation has increased and can be attributed in some part to GHG emissions, there have not been shown corresponding increases in streamflow (floods)  or damage. How can this be?  Think of it like this -- Precipitation is to flood damage as wind is to windstorm damage.  It is not enough to say that it has become windier to make a connection to increased windstorm damage -- you need to show a specific increase in those specific wind events that actually cause damage. There are a lot of days that could be windier with no increase in damage; the same goes for precipitation.

My understanding of the literature on streamflow is that there have not been shown increasing peak streamflow commensurate with increases in precipitation, and this is a robust finding across the literature.  For instance, one recent review concludes: Floods are of great concern in many areas of the world, with the last decade seeing major fluvial events in, for example, Asia, Europe and North America. This has focused attention on whether or not these are a result of a changing climate. Rive flows calculated from outputs from global models often suggest that high river flows will increase in a warmer, future climate. However, the future projections are not necessarily in tune with the records collected so far – the observational evidence is more ambiguous. A recent study of trends in long time series of annual maximum river flows at 195 gauging stations worldwide suggests that the majority of these flow records (70%) do not exhibit any statistically significant trends. Trends in the remaining records are almost evenly split between having a positive and a negative direction.

Absent an increase in peak streamflows, it is impossible to connect the dots between increasing precipitation and increasing floods.  There are of course good reasons why a linkage between increasing precipitation and peak streamflow would be difficult to make, such as the seasonality of the increase in rain or snow, the large variability of flooding and the human influence on river systems.  Those difficulties of course translate directly to a difficulty in connecting the effects of increasing GHGs to flood disasters.

Second, the Pall et al. paper seeks to quantify the increased risk of a specific flood event in the UK in 2000 due to greenhouse gas emissions.  It applies a methodology that was previously used with respect to the 2003 European heatwave. Taking the paper at face value, it clearly states that in England and Wales, there has not been an increasing trend in precipitation or floods.  Thus, floods in this region are not a contributor to the global increase in disaster costs.  Further, there has been no increase in Europe in normalized flood losses (PDF).  Thus, Pall et al. paper is focused attribution in the context of on a single event, and not trend detection in the region that it focuses on, much less any broader context.

More generally, the paper utilizes a seasonal forecast model to assess risk probabilities.  Given the performance of seasonal forecast models in actual prediction mode, I would expect many scientists to remain skeptical of this approach to attribution. Of course, if this group can show an improvement in the skill of actual seasonal forecasts by using greenhouse gas emissions as a predictor, they will have a very convincing case.  That is a high hurdle.

In short, the new studies are interesting and add to our knowledge.  But they do not change the state of knowledge related to trends in global disasters and how they might be related to greenhouse gases.  But even so, I expect that many will still want to connect the dots between greenhouse gas emissions and recent floods.  Connecting the dots is fun, but it is not science.

Jessica Weinkle said...

The thing about the nature articles is that Nature itself made the leap from the science findings to damages in the News piece by Q. Schiermeier through the decision to bring up the topic of insurance. (Not to mention that which is symbolically represented merely by the journal’s cover this week). With what I (maybe, naively) believe to be a particularly ballsy move, the article quoted Muir-Wood, an industry scientists. However, what he is quoted as saying is admirably clever. Initially it is stated that Dr. Muir-Wood backs the notion that one cannot put the blame of increased losses on climate change. Then, the article ends with a quote from him, “If there’s evidence that risk is changing, then this is something we need to incorporate in our models.”

This is a very slippery slope and a brilliant double-dog dare. Without doing anything but sitting back and watching the headlines, one can form the argument that “science” supports the remodeling of the hazard risk above the climatological average and is more important then the risks stemming from socioeconomic factors. The reinsurance industry itself has published that socioeconomic factors far outweigh changes in the hazard in concern of losses. The point is (and that which has particularly gotten my knickers in a knot) is that Nature, et al. may wish to consider what it is that they want to accomplish. Is it greater involvement of federal governments in the insurance/reinsurance industry on the premise that climate change is too great a loss risk for private industry alone regardless of the financial burden it imposes? The move of insurance mechanisms into all corners of the earth under the auspices of climate change adaptation? Or simply a move to bolster prominence, regardless of whose back it breaks- including their own, if any of them are proud owners of a home mortgage? How much faith does one have in their own model when they are told that hundreds of millions of dollars in the global economy is being bet against the odds that their models produce?

What Nature says matters to the world; what scientists say matters to the world- whether they care for the responsibility or not. That is after all, the game of fame and fortune (aka prestige).

Second of all, fields naturally select for people who accept their foundational assumptions. People who don't accept those assumptions are likely not to have gone into that field in the first place, or to have left it already.

Sometimes those foundational assumptions are simple enough that an outsider can evaluate them - for instance, I may not be an expert in astrology or theology, but I can understand their starting premises (stars affect human fates; we should accept the Bible as the truth) well enough to confidently dismiss them, and the fields that rest on them. But when the foundational assumptions get more complex - like the assumption that we can reliably model future temperatures - it becomes much harder for an outsider to judge their soundness.

we almost seem to be stuck in a Catch-22: The only people who are qualified to evaluate the validity of a complex field are the ones who have studied that field in depth - in other words, experts. Yet the experts are also the people who have the strongest incentives not to reject the foundational assumptions of the field, and the ones who have self-selected for believing those assumptions. So the closer you are to a field, the more biased you are, which makes you a poor judge of it; the farther away you are, the less relevant knowledge you have, which makes you a poor judge of it. What to do?

luckily, the Catch-22 isn't quite as stark as I made it sound. For example, you can often find people who are experts in the particular methodology used by a field without actually being a member of the field, so they can be much more unbiased judges of whether that field is applying the methodology soundly. So for example, a foundational principle underlying a lot of empirical social science research is that linear regression is a valid tool for modeling most phenomena. I strongly recommend asking a statistics professor about that. 

there are some general criteria that outsiders can use to evaluate the validity of a technical field, even without “technical scientific expertise” in that field. For example, can the field make testable predictions, and does it have a good track record of predicting things correctly? This seems like a good criterion by which an outsider can judge the field of climate modeling (and "predictions" here includes using your model to predict past data accurately). I don't need to know how the insanely-complicated models work to know that successful prediction is a good sign.

And there are other more field-specific criteria outsiders can often use. For example, I've barely studied postmodernism at all, but I don't have to know much about the field to recognize that the fact that they borrow concepts from complex disciplines which they themselves haven't studied is a red flag.

the issue with AGW is less the science and all about the political solutions. Most every solution we hear in the public conversation requires some level of sacrifice and uncertainty in the future.Politicians, neither experts in climatology nor economics, craft legislation to solve the problem through the lens of their own political ideology. At TAM8, this was pretty apparent. My honest opinion is that people who are AGW skeptics are mainly skeptics of the political solutions. If AGW was said to increase the GDP of the country by two to three times, I'm guessing you'd see a lot less climate change skeptics.

set out to model was the development of religious schisms. Such schisms are a recurrent feature of religion, especially in the West. The classic example is the fracturing of Christianity that occured after the reformation.

Their model made two simple assumptions. Firstly, that religions that are highly dominant actually induce some people to want to break away from them. When a religion becomes overcrowded, then some individuals will lose their religion and take up another.

Second, they assume that every religion has a value to the individual that is composed of it's costs and benefits. That value varies between religion, but is the same for all individuals. It's a pretty simplistic assumption, but even so they get some interesting results.

Now, this is a very simple model, and so the results shouldn't be over-interpreted. But it's a fascinating result for a couple of reasons. It shows how new religious 'species' can come into being in a mixed population - no need for geographical separation. That's such a common feature of religion - from the Judaeo-Christian religions to examples from Papua New Guinea - that it's worth trying to understand what drives it. What's more, this is the first time that anyone has attempted to model the transmission of religious ideas in evolutionary terms. It's a first step, to be sure, but just showing that it can be done is a significant achievement.

The value comes because it shifts the focus from thinking about how culture benefits the host, and instead asks how the cultural trait is adaptive in it's own right. What is important is not whether or not the human host benefits from the trait, but rather whether the trait can successfully transmit and reproducing itself (see Bible Belter for an example of how this could work).

Even more intriguing is the implications for understanding cultural-genetic co-evolution. After all, we know that viruses and their hosts co-evolve in a kind of arms race - sometimes ending up in a relationship that benefits both.

You could argue that modern economics really began with David Hume’s Of the Balance of Trade, whose core is a gloriously clear thought experiment

The aim of the Pall et al paper was to examine a specific event – floods in the UK in Oct/Nov 2000. Normally, with a single event there isn’t enough information to do any attribution, but Pall et al set up a very large ensemble of runs starting from roughly the same initial conditions to see how often the flooding event occurred. Note that flooding was defined as more than just intense rainfall – the authors tracked runoff and streamflow as part of their modelled setup. Then they repeated the same experiments with pre-industrial conditions (less CO2 and cooler temperatures). If the amount of times a flooding event would occur increased in the present-day setup, you can estimate how much more likely the event would have been because of climate change. The results gave varying numbers but in nine out of ten cases the chance increased by more than 20%, and in two out of three cases by more than 90%. This kind of fractional attribution (if an event is 50% more likely with anthropogenic effects, that implies it is 33% attributable) has been applied also to the 2003 European heatwave, and will undoubtedly be applied more often in future. One neat and interesting feature of these experiments was that they used the climateprediction.net set up to harness the power of the public’s idle screensaver time.

The second paper is a more standard detection and attribution study. By looking at the signatures of climate change in precipitation intensity and comparing that to the internal variability and the observation, the researchers conclude that the probability of intense precipitation on any given day has increased by 7 percent over the last 50 years – well outside the bounds of natural variability. This is a result that has been suggested before (i.e. in the IPCC report (Groisman et al, 2005), but this was the first proper attribution study (as far as I know). The signal seen in the data though, while coherent and similar to that seen in the models, was consistently larger, perhaps indicating the models are not sensitive enough, though the El Niño of 1997/8 may have had an outsize effect.

Both papers were submitted in March last year, prior to the 2010 floods in Pakistan, Australia, Brazil or the Philippines, and so did not deal with any of the data or issues associated with those floods. However, while questions of attribution come up whenever something weird happens to the weather, these papers demonstrate clearly that the instant pop-attributions we are always being asked for are just not very sensible. It takes an enormous amount of work to do these kinds of tests, and they just can’t be done instantly. As they are done more often though, we will develop a better sense for the kinds of events that we can say something about, and those we can’t.

There is always concern that the start and end points for any trend study are not appropriate (both sides are guilty on this IMO). I have read precipitation studies were more difficult due to sparse data, and it seems we would have seen precipitation trend graphs a lot more often by now if it was straight forward. 7% seems to be a large change to not have been noted (vocally) earlier, seems like there is more to this story.

Of course, while there are similarities between the positions adopted in the "positivism/scientism versus non-positivism/scientism" and "Western medicine versus alternative medicine" debates, I suppose that one main difference is however that the latter is not just a theoretical debate but involves people's health and lives.

As was mentioned earlier, the general impression is perhaps that while Western medicine, which generally has its roots in Western societies, is scientifically proven, TCM, or alternative medicine, is however not as scientifically proven. The former is thus regarded as the dominant mainstream model of medical treatment while non-Western medical knowledge or treatment is regarded as "alternative medicine".

The process by which the above impression was created was, according to the postcolonial theorists, a highly political one. Essentially, it may be argued that along with their political colonisation of non-European territories in the past, the European/Western colonialists also colonised the minds of those living in those territories. This means that along with colonisation, traditional forms of knowledge, including medical knowledge, and cultures in the colonised terrorities were relegated to a non-dominant, if not inferior, position vis-a-vis Western knowledge and culture. And as postcolonial theorists may argue, the legacy and aftermath of this process is still felt today and efforts should be made to reverse it.

In light of the above, the increased push to have non-Western forms of medical treatment be recognised as an equally valid model of medical treatment besides that of Western medicine may be seen as part of the effort to reverse the dominance of Western knowledge and culture set in place during the colonial period. Of course, this push to reverse Western dominance is especially relevant in recent times, in light of the economic and political rise of non-Western powers such as China and India (interestingly enough, to the best of my knowledge, when talking about "alternative medicine", people are usually referring to traditional Indian or Chinese medical treatments and not really traditional African medical treatment).

Here, it is worthwhile to pause and think for a while: if it is recognised that Western and non-Western medicine are different but equally valid models of medical treatment, would they be complimentary or competing models? Or would they be just different models?

Moving on, so far it would seem that , for at least the foreseeable future, Western medicine will retain its dominant "mainstream" position but who knows what the future may hold?

Carlo Rovelli, a physicist at the University of Aix-Marseille, emphasised the uselessness of certainty. He said that the idea of something being "scientifically proven" was practically an oxymoron and that the very foundation of science is to keep the door open to doubt.

"A good scientist is never 'certain'. Lack of certainty is precisely what makes conclusions more reliable than the conclusions of those who are certain: because the good scientist will be ready to shift to a different point of view if better elements of evidence, or novel arguments emerge. Therefore certainty is not only something of no use, but is in fact damaging, if we value reliability."

physicist Lawrence Krauss of Arizona State University agreed. "In the public parlance, uncertainty is a bad thing, implying a lack of rigour and predictability. The fact that global warming estimates are uncertain, for example, has been used by many to argue against any action at the present time," he said.

however, uncertainty is a central component of what makes science successful. Being able to quantify uncertainty, and incorporate it into models, is what makes science quantitative, rather than qualitative. Indeed, no number, no measurement, no observable in science is exact. Quoting numbers without attaching an uncertainty to them implies they have, in essence, no meaning."

Neil Gershenfeld, director of the Massachusetts Institute of Technology's Centre for Bits and Atoms wants everyone to know that "truth" is just a model. "The most common misunderstanding about science is that scientists seek and find truth. They don't – they make and test models," he said.

Building models is very different from proclaiming truths. It's a never-ending process of discovery and refinement, not a war to win or destination to reach. Uncertainty is intrinsic to the process of finding out what you don't know, not a weakness to avoid. Bugs are features – violations of expectations are opportunities to refine them. And decisions are made by evaluating what works better, not by invoking received wisdom."

writer and web commentator Clay Shirky suggested that people should think more carefully about how they see the world. His suggestion was the Pareto principle, a pattern whereby the top 1% of the population control 35% of the wealth or, on Twitter, the top 2% of users send 60% of the messages. Sometimes known as the "80/20 rule", the Pareto principle means that the average is far from the middle.It is applicable to many complex systems, "And yet, despite a century of scientific familiarity, samples drawn from Pareto distributions are routinely presented to the public as anomalies, which prevents us from thinking clearly about the world," said Shirky. "We should stop thinking that average family income and the income of the median family have anything to do with one another, or that enthusiastic and normal users of communications tools are doing similar things, or that extroverts should be only moderately more connected than normal people. We should stop thinking that the largest future earthquake or market panic will be as large as the largest historical one; the longer a system persists, the likelier it is that an event twice as large as all previous ones is coming."

Kevin Kelly, editor-at-large of Wired, pointed to the value of negative results. "We can learn nearly as much from an experiment that does not work as from one that does. Failure is not something to be avoided but rather something to be cultivated. That's a lesson from science that benefits not only laboratory research, but design, sport, engineering, art, entrepreneurship, and even daily life itself. All creative avenues yield the maximum when failures are embraced."

Michael Shermer, publisher of the Skeptic Magazine, wrote about the importance of thinking "bottom up not top down", since almost everything in nature and society happens this way.

But most people don't see things that way, said Shermer. "Bottom up reasoning is counterintuitive. This is why so many people believe that life was designed from the top down, and why so many think that economies must be designed and that countries should be ruled from the top down."

Roger Schank, a psychologist and computer scientist, proposed that we should all know the true meaning of "experimentation", which he said had been ruined by bad schooling, where pupils learn that scientists conduct experiments and if we copy exactly what they did in our high school labs we will get the results they got. "In effect we learn that experimentation is boring, is something done by scientists and has nothing to do with our daily lives."Instead, he said, proper experiments are all about assessing and gathering evidence. "In other words, the scientific activity that surrounds experimentation is about thinking clearly in the face of evidence obtained as the result of an experiment. But people who don't see their actions as experiments, and those who don't know how to reason carefully from data, will continue to learn less well from their own experiences than those who do

Lisa Randall, a physicist at Harvard University, argued that perhaps "science" itself would be a useful concept for wider appreciation. "The idea that we can systematically understand certain aspects of the world and make predictions based on what we've learned – while appreciating and categorising the extent and limitations of what we know – plays a big role in how we think.

"Many words that summarise the nature of science such as 'cause and effect', 'predictions', and 'experiments', as well as words that describe probabilistic results such as 'mean', 'median', 'standard deviation', and the notion of 'probability' itself help us understand more specifically what this means and how to interpret the world and behaviour within it."

Second, the challenges of short-term behavior. The Gulf's Loop Current acts as a conveyor belt, moving from the Yucatan through the Florida Straits into the Atlantic. Usually, the current curves northward near the Louisiana and Mississippi coasts—a configuration that would have put it on track to pick up the oil and transport it into open ocean. However, the current’s short-term movements over a few weeks or even months are chaotic and impossible to predict. Sometimes small eddies, or mini-currents, peel off, shifting the position and strength of the main current. To determine the threat to the Atlantic, the research team studied averages of the Loop Current’s past behavior in order to simulate its likely course after the spill and ran several dozen computer simulations under various scenarios. Fortunately for the East Coast, the Loop Current did not behave in its usual fashion but instead remained farther south than usual, which kept it far from the Louisiana and Mississippi coast during the crucial few months before the oil degraded and/or was dispersed with chemical treatments.

The Loop Current typically goes into a southern configuration about every 6 to 19 months, although it rarely remains there for very long. NCAR scientist Synte Peacock, who worked on the projection, explains that part of the reason the current is unpredictable is “no two cycles of the Loop Current are ever exactly the same." She adds that the cycles are influenced by such variables as how large the eddy is, where the current detaches and moves south, and how long it takes for the current to reform. Computer models can simulate the currents realistically, she adds. But they cannot predict when the currents will change over to a new cycle. The scientists were careful to explain that their simulations were a suite of possible trajectories demonstrating what was likely to happen, but not a definitive forecast of what would happen. They reiterated that point in a peer-reviewed study on the simulations that appeared last August in Environmental Research Letters. 

So who was at fault?  According to Hosansky it was those dummies in the media: These caveats, however, got lost in much of the resulting media coverage.Another perspective is that having some of these caveats in the press release might have been a good idea.

patents are designed to make that sort of thing more difficult, because it assumes that the initial act of invention is the key point, rather than all the incremental innovations built on top of it that all parties can benefit from.

the report points to numerous studies that show, when given the chance, many companies freely share their ideas with others, recognizing the direct benefit they get.

Even more importantly, the paper finds that due to technological advances and the ability to more rapidly and easily communicate and collaborate widely, these forms of innovation (innovation for direct use as well as collaborative innovation) are becoming more and more viable across a variety of industries, which in the past may have relied more on the old way of innovating (single company innovative for the profit of selling that product).

because of the ease of communication and collaboration these days, there's tremendous incentive for those companies that innovate for their own use to collaborate with others, since the benefit from others improving as well help improve their own uses. Thus, the overall incentives are to move much more to a collaborative form of innovation in the market. That has huge implications for a patent system designed to help the "old model" of innovation (producer inventing for the market) and not the increasingly regular one (collaborative innovation for usage).

no one is saying that producer-based innovation (company inventing to sell on the market) doesn't occur or won't continue to occur. But it is an open policy question as to whether or not our innovation policies should favor that model over other models -- when evidence suggests that a significant amount of innovation occurs in these other ways -- and that amount is growing rapidly.

The first step in establishing risk-adjusted premiums is to know the likelihood of the depth of flooding at each address. This information has to be address-specific because the severity of flooding can vary widely over small distances, for example, from one side of a road to the other.

A litany of reasons is given for withholding data. At times it seems that refusal stems from a view that insurance is innately evil. This is ironic in view of the gratuitous advice sometimes offered by politicians and commentators in the aftermath of extreme events, exhorting insurers to pay claims even when no legal liability exists and riverine flood is explicitly excluded from policies.

Risk Frontiers is involved in jointly developing the National Flood Information Database (NFID) for the Insurance Council of Australia with Willis Re, a reinsurance broking intermediary. NFID is a five year project aiming to integrate flood information from all city councils in a consistent insurance-relevant form. The aim of NFID is to help insurers understand and quantify their risk. Unfortunately, obtaining the base data for NFID from some local councils is difficult and sometimes impossible despite the support of all state governments for the development of NFID. Councils have an obligation to assess their flood risk and to establish rules for safe land development. However, many are antipathetic to the idea of insurance. Some states and councils have been very supportive – in New South Wales and Victoria, particularly. Some states have a central repository – a library of all flood studies and digital terrain models (digital elevation data). Council reluctance to release data is most prevalent in Queensland, where, unfortunately, no central repository exists.

Second, models of flood risk are sometimes misused:

many councils only undertake flood modelling in order to create a single design flood level, usually the so-called one-in-100 year flood. (For reasons given later, a better term is the flood with an 1% annual likelihood of being exceeded.)

Inundation maps showing the extent of the flood with a 1% annual likelihood of exceedance are increasingly common on council websites, even in Queensland. Unfortunately these maps say little about the depth of water at an address or, importantly, how depth varies for less probable floods. Insurance claims usually begin when the ground is flooded and increase rapidly as water rises above the floor level. At Windsor in NSW, for example, the difference in the water depth between the flood with a 1% annual chance of exceedance and the maximum possible flood is nine metres. In other catchments this difference may be as small as ten centimetres. The risk of damage is quite different in both cases and an insurer needs this information if they are to provide coverage in these areas.

The ‘one-in-100 year flood’ term is misleading. To many it is something that happens regularly once every 100 years — with the reliability of a bus timetable. It is still possible, though unlikely, that a flood of similar magnitude or even greater flood could happen twice in one year or three times in successive years.

The calculations underpinning this are not straightforward but the probability that an address exposed to a 1-in-100 year flood will experience such an event or greater over the lifetime of the house – 50 years say – is around 40%. Over the lifetime of a typical home mortgage – 25 years – the probability of occurrence is 22%. These are not good odds.

Can any Singaporean honestly say the she/he can conceive of a fellow Singaporean setting himself or herself on fire along Orchard Road or Shenton Way, as a result of desperate economic pressures or financial constraints? Can we even fathom the social and economic pressures that mobilized a whole people to protest and overthrow a corrupt, US-backed regime? (that is, not during elections time) Singapore has real problems, the People’s Action Party has its real problems, and there is indeed much room for improvement. Yet such irresponsible reporting by one of the esteemed newspapers from the UK is utterly disappointing, not constructive in the least sense, and utterly misrepresents our political situation (and may potentially provoke more irrationality in our society, leading people to ‘believe’ their affinity with their Arab peers which leads to more radicalism).

crowd sourced public policy

Moreover, the reforms are cabined by these authoritarian nations' fear of true critical freedom. In Singapore, nobody even attempts to use the new techniques when teaching about politics and contemporary problems. "Citizenship education" typically takes the form of analyzing a problem, proposing several possible solutions, and then demonstrating how the one chosen by government is the right one for Singapore.

One professor of communications (who has since left Singapore) reported on a recent attempt to lead a discussion of the libel suits in her class: "I can feel the fear in the room. …You can cut it with a knife."

Singapore and China are terrible models of education for any nation that aspires to remain a pluralistic democracy. They have not succeeded on their own business-oriented terms, and they have energetically suppressed imagination and analysis when it comes to the future of the nation and the tough choices that lie before it. If we want to turn to Asia for models, there are better ones to be found: Korea's humanistic liberal arts tradition, and the vision of Tagore and like-minded Indian educators.