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Value-free science? Many scientists think that science is about objectivity and “just the facts, ma’am.” Not so fast, philosophers, historians and sociologists of science have argued now for a number of decades. While I certainly have no sympathy for the extreme postmodernist position exemplified by the so-called “strong programme” in sociology of science — that science is entirely the result of social construction — there are several interesting and delicate facets of the problem to explore.

there are values embedded in the practice of science itself: testability, accuracy, generality, simplicity, and the like. Needless to say, few if any of these can be justified within science itself — there is no experiment confirming Occam’s razor, for instance.

Then there are the many moral dimensions of science practice, both in terms of ethical issues internal to science (fraud) and of the much broader ones affecting society at large (societal consequences of research and technological advances).

There is also the issue of diversity in science. Until very recently, and in many fields still today, science has largely been an affair conducted by white males. And this has historically resulted in a large amount of nonsense — say about gender differences, or ethnic differences — put forth as objective knowledge and accepted by the public because it has the imprimatur of science. But, you might say, that was the past, now we have corrected the errors and moved on. Except that such an argument ignores the fact that there is little reason to think that only we have gotten it just right, that the current generation is somehow immune from an otherwise uninterrupted history of science-based blunders.

Regarding Occam's Razor, there is a justification for it based on probability theory, see:http://www.johndcook.com/blog/2011/01/12/occams-razor-bayes-theorem/http://telescoper.wordpress.com/2011/02/19/bayes-razor/http://www.stat.duke.edu/~berger/papers/ockham.html

another interesting dimension of the relationship between values and science concerns which scientific questions we should pursue (and, often, fund with public money). Scientists often act as they ought to be the only arbiters here, and talk as if some questions were “obviously” intrinsically important. But when your research is costly and paid for by the public, perhaps society deserves a bit more of an explanation concerning why millions of dollars ought to be spent on obscure problems that apparently interest only a handful of university professors concentrated in one or a few countries.

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.

But the problem is deeper than simply intellectual inertia. It goes back, ultimately, to the unconsidered differentiations we make—at every moment when we distinguish among objects—between those in the foreground of our consciousness and the background places in which the objects happen to be situated. Moreover, this distinction creates a hierarchy of objects. We are conscious not only of the skin that encloses and defines the object, but of bits and pieces of that object, each of which must have its own “skin.” That is the problem of anatomization. A car has a motor and brakes and a transmission and an outer body that, at appropriate moments, become separate objects of our consciousness, objects that at least some knowledgeable person recognizes as coherent entities.

Evelyn Fox Keller sees “The Mirage of a Space Between Nature and Nurture” as a consequence of our false division of the world into living objects without sufficient consideration of the external milieu in which they are embedded, since organisms help create effective environments through their own life activities.

The central point of her analysis has been that gender itself (as opposed to sex) is socially constructed, and that construction has influenced the development of science:If there is a single point on which all feminist scholarship…has converged, it is the importance of recognizing the social construction of gender…. All of my work on gender and science proceeds from this basic recognition. My endeavor has been to call attention to the ways in which the social construction of a binary opposition between “masculine” and “feminine” has influenced the social construction of science.

major critical concern of Fox Keller’s present book is the widespread attempt to partition in some quantitative way the contribution made to human variation by differences in biological inheritance, that is, differences in genes, as opposed to differences in life experience. She wants to make clear a distinction between analyzing the relative strength of the causes of variation among individuals and groups, an analysis that is coherent in principle, and simply assigning the relative contributions of biological and environmental causes to the value of some character in an individual

It is, for example, all very well to say that genetic variation is responsible for 76 percent of the observed variation in adult height among American women while the remaining 24 percent is a consequence of differences in nutrition. The implication is that if all variation in nutrition were abolished then 24 percent of the observed height variation among individuals in the population in the next generation would disappear. To say, however, that 76 percent of Evelyn Fox Keller’s height was caused by her genes and 24 percent by her nutrition does not make sense. The nonsensical implication of trying to partition the causes of her individual height would be that if she never ate anything she would still be three quarters as tall as she is.

In fact, Keller is too optimistic about the assignment of causes of variation even when considering variation in a population. As she herself notes parenthetically, the assignment of relative proportions of population variation to different causes in a population depends on there being no specific interaction between the causes.

Keller’s rather casual treatment of the interaction between causal factors in the case of the drummers, despite her very great sophistication in analyzing the meaning of variation, is a symptom of a fault that is deeply embedded in the analytic training and thinking of both natural and social scientists. If there are several variable factors influencing some phenomenon, how are we to assign the relative importance to each in determining total variation? Let us take an extreme example. Suppose that we plant seeds of each of two different varieties of corn in two different locations with the following results measured in bushels of corn produced (see Table 1). There are differences between the varieties in their yield from location to location and there are differences between locations from variety to variety. So, both variety and location matter. But there is no average variation between locations when averaged over varieties or between varieties when averaged over locations. Just by knowing the variation in yield associated with location and variety separately does not tell us which factor is the more important source of variation; nor do the facts of location and variety exhaust the description of that variation.

the query "stem cells" might produce diametrically opposed results for scientists who support stem-cell research and activists who oppose it.

"Proof of climate change" might turn up different results for an environmental activist and an oil-company executive.

majority of us assume search engines are unbiased. But that may be just because they're increasingly biased to share our own views. More and more, your computer monitor is a kind of one-way mirror, reflecting your own interests while algorithmic observers watch what you click. Google's announcement marked the turning point of an important but nearly invisible revolution in how we consume information. You could say that on 4 December 2009 the era of personalisation began.

We are predisposed to respond to a pretty narrow set of stimuli – if a piece of news is about sex, power, gossip, violence, celebrity or humour, we are likely to read it first. This is the content that most easily makes it into the filter bubble. It's easy to push "Like" and increase the visibility of a friend's post about finishing a marathon or an instructional article about how to make onion soup. It's harder to push the "Like" button on an article titled "Darfur sees bloodiest month in two years". In a personalised world, important but complex or unpleasant issues – the rising prison population, for example, or homelessness – are less likely to come to our attention at all.

As a consumer, it's hard to argue with blotting out the irrelevant and unlikable. But what is good for consumers is not necessarily good for citizens. What I seem to like may not be what I actually want, let alone what I need to know to be an informed member of my community or country. "It's a civic virtue to be exposed to things that appear to be outside your interest," technology journalist Clive Thompson told me. Cultural critic Lee Siegel puts it a different way: "Customers are always right, but people aren't."

Personalisation is based on a bargain. In exchange for the service of filtering, you hand large companies an enormous amount of data about your daily life – much of which you might not trust friends with.

To be the author of your life, professor Yochai Benkler argues, you have to be aware of a diverse array of options and lifestyles. When you enter a filter bubble, you're letting the companies that construct it choose which options you're aware of. You may think you're the captain of your own destiny, but personalisation can lead you down a road to a kind of informational determinism in which what you've clicked on in the past determines what you see next – a web history you're doomed to repeat. You can get stuck in a static, ever- narrowing version of yourself – an endless you-loop.