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We could continuously sample a news source’s output to produce ongoing accuracy estimates, and build social software to help the audience report and filter errors. Meticulously applied, this approach would give a measure of the accuracy of each information source, and a measure of the efficiency of their corrections process (currently only about 3% of all errors are corrected.)

Real world reporting isn’t always clearly “right” or “wrong,” so it will often be hard to decide whether something is an error or not. But we’re not going for ultimate Truth here,  just a general way of measuring some important aspect of the idea we call “accuracy.” In practice it’s important that the error counting method is simple, clear and repeatable, so that you can compare error rates of different times and sources.

Subjective errors, though by definition involving judgment, should not be dismissed as merely differences in opinion. Sources found such errors to be about as common as factual errors and often more egregious [as rated by the sources.] But subjective errors are a very complex category

One of the major problems with previous news accuracy metrics is the effort and time required to produce them. In short, existing accuracy measurement methods are expensive and slow. I’ve been wondering if we can do better, and a simple idea comes to mind: sampling. The core idea is this: news sources could take an ongoing random sample of their output and check it for accuracy — a fact check spot check

Standard statistical theory tells us what the error on that estimate will be for any given number of samples (If I’ve got this right, the relevant formula is standard error of a population proportion estimate without replacement.) At a sample rate of a few stories per day, daily estimates of error rate won’t be worth much. But weekly and monthly aggregates will start to produce useful accuracy estimates

the first step would be admitting how inaccurate journalism has historically been. Then we have to come up with standardized accuracy evaluation procedures, in pursuit of metrics that capture enough of what we mean by “true” to be worth optimizing. Meanwhile, we can ramp up the efficiency of our online corrections processes until we find as many useful, legitimate errors as possible with as little staff time as possible. It might also be possible do data mining on types of errors and types of stories to figure out if there are patterns in how an organization fails to get facts right.

I’d love to live in a world where I could compare the accuracy of information sources, where errors got found and fixed with crowd-sourced ease, and where news organizations weren’t shy about telling me what they did and did not know. Basic factual accuracy is far from the only measure of good journalism, but perhaps it’s an improvement over the current sad state of affairs

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.

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.

perhaps it would have been nice to not side-step the question of why are these games, as a whole, important as art. Considering this is the first major American art institution to put on a video game show, I would have liked to see a more powerful statement about the medium.

crowd sourced public policy

The missing ingredients are software packages, which are available at no cost but have to be made easy to use. “You would have a whole system with privacy and security built in for the civil world we are living in,” he said. “It stores everything you care about.” Put free software into the little plug server in the wall, and you would have a Freedom Box that would decentralize information and power, Mr. Moglen said. This month, he created the Freedom Box Foundation to organize the software.

In the first days of the personal computer era, many scoffed at the idea that free software could have an important place in the modern world. Today, it is the digital genome for millions of phones, printers, cameras, MP3 players, televisions, the Pentagon, the New York Stock Exchange and the computers that underpin Google’s empire.

Social networking has changed the balance of political power, he said, “but everything we know about technology tells us that the current forms of social network communication, despite their enormous current value for politics, are also intensely dangerous to use. They are too centralized; they are too vulnerable to state retaliation and control.”

investors were said to have put a value of about $50 billion on Facebook, the social network founded by Mark Zuckerberg. If revolutions for freedom rest on the shoulders of Facebook, Mr. Moglen said, the revolutionaries will have to count on individuals who have huge stakes in keeping the powerful happy.

“It is not hard, when everybody is just in one big database controlled by Mr. Zuckerberg, to decapitate a revolution by sending an order to Mr. Zuckerberg that he cannot afford to refuse,” Mr. Moglen said. By contrast, with tens of thousands of individual encrypted servers, there would be no one place where a repressive government could find out who was publishing or reading “subversive” material.

So who produces all this bulk jive? Business Insider, the business-news site, has provided a forum to a half dozen low-paid content farmers, especially several who work at AOL’s enormous Seed and Patch ventures. They describe exhausting and sometimes exploitative writing conditions. Oliver Miller, a journalist with an MFA in fiction from Sarah Lawrence who once believed he’d write the Great American Novel, told me AOL paid him about $28,000 for writing 300,000 words about television, all based on fragments of shows he’d never seen, filed in half-hour intervals, on a graveyard shift that ran from 11 p.m. to 7 or 8 in the morning.

Mr. Miller’s job, as he made clear in an article last week in The Faster Times, an online newspaper, was to cram together words that someone’s research had suggested might be in demand on Google, position these strings as titles and headlines, embellish them with other inoffensive words and make the whole confection vaguely resemble an article. AOL would put “Rick Fox mustache” in a headline, betting that some number of people would put “Rick Fox mustache” into Google, and retrieve Mr. Miller’s article. Readers coming to AOL, expecting information, might discover a subliterate wasteland. But before bouncing out, they might watch a video clip with ads on it. Their visits would also register as page views, which AOL could then sell to advertisers.

commodify writing: you pay little or nothing to writers, and make readers pay a lot — in the form of their “eyeballs.” But readers get zero back, no useful content.

You can’t mess with Google forever. In February, the corporation concocted what it concocts best: an algorithm. The algorithm, called Panda, affects some 12 percent of searches, and it has — slowly and imperfectly — been improving things. Just a short time ago, the Web seemed ungovernable; bad content was driving out good. But Google asserted itself, and credit is due: Panda represents good cyber-governance. It has allowed Google to send untrustworthy, repetitive and unsatisfying content to the back of the class. No more A’s for cheaters.

the goal, according to Amit Singhal and Matt Cutts, who worked on Panda, is to “provide better rankings for high-quality sites — sites with original content and information such as research, in-depth reports, thoughtful analysis and so on.”

Google officially rolled out Panda 2.2. Put “Whitey Bulger” into Google, and where you might once have found dozens of content farms, today you get links to useful articles from sites ranging from The Boston Globe, The Los Angeles Times, the F.B.I. and even Mashable, doing original analysis of how federal agents used social media to find Bulger. Last month, Demand Media, once the most notorious of the content farms, announced plans to improve quality by publishing more feature articles by hired writers, and fewer by “users” — code for unpaid freelancers. Amazing. Demand Media is stepping up its game.