Privacy and WEF: Use, Not Collection, Should Be Focus of Data Rules, Report Says

Steve Loh, The New York Times, February 28, 2013

Personal data is a valuable asset that ought to be put to work.

Fluid data markets will benefit economies, societies and individuals.

Privacy rules should focus on how data is used rather than on the widespread collection of personal data.

That is the gist of a new report from World Economic Forum's Personal Data project, "Unlocking the Value of Personal Data: From Collection to Usage."

The modern digital world, with its explosion of data, has made the traditional approach to privacy based on "notice and consent" typically between two parties — a marketer and a consumer — obsolete, in the view of the report's authors.

"The technology has overrun the classical model," said Craig Mundie, a senior adviser to Microsoft's chief executive, Steven A. Ballmer.

Mr. Mundie was on the five-member steering board for the report. All five people represent corporations that stand to gain from tapping personal data.

Privacy advocates and regulators in Europe and the United States have been reluctant to give up on efforts to control the collection of data. Their concern is that once personal data is collected, its use is very difficult to monitor and control. Information brokers that consumers never see — and few know about — market personal data to advertisers, retailers, financial institutions and others. That problem prompted the Federal Trade Commission in a report last year to recommend that Congress enact legislation "to provide greater transparency for, and control over, the practices of information brokers."

But while recognizing the privacy challenges, the companies participating in the World Economic Forum project say what was needed was a careful balance. In a blog post on Wednesday, Raymond J. Baxter, a senior vice president of Kaiser Permanente, a major health care provider and insurer, emphasized the value of personal data, when used properly. He cited Kaiser's use of personal medical data for research.

For example, mining family data and outcomes over years, Kaiser scientists found that the children of women who took anti-depressant drugs while pregnant had more than twice the risk of developing autism disorders. "By discovering this correlation and leveraging this data in new ways, lives are improved," Mr. Baxter wrote.
According to Mr. Mundie of Microsoft, technology can help strike the right balance between individuals' concerns about privacy and the benefit of a fluid market in personal data. He said independent organizations, most likely nonprofits, would develop automated privacy preference services that individuals could subscribe to. A person would check off what he or she wanted his data to be used for and not. Those preferences, he explained, would then be encoded as software tags that traveled with the person's data.

Those preferences, Mr. Mundie added, could vary depending on context. For example, a person might say he or she did not want personal medical data shared beyond a family doctor and one or two specialists — unless the person was taken to an emergency ward.

"You can intelligently use computing technology to provide the benefits and curtail abuse," Mr. Mundie said.

Big Data Book Launch

Big Data: A Revolution That Will Transform How We Live, Work, and Think   Thursday, March 7, 2013 9:30 - 10:30 p.m.  New America Foundation 1899 L St. NW Suite 400 Washington DC, 20036    The Open Technology Institute at the New America Foundation invites you to celebrate the book launch of Big Data: A Revolution That Will Transform How We Live, Work, and Think.  Which paint color is most likely to tell you a used car is in good shape? How did Google searches predict the spread of the H1N1 flu outbreak? How can data help government be more efficient and politicians get elected? The key to answering these questions, and many more, is "big data"-our newfound ability to crunch vast collections of information, analyze them instantly, and draw sometimes-profoundly surprising conclusions from them. Kenneth Cukier and Viktor Mayer-Schonberger, co-authors of Big Data, explain why the revolution is on par with the Internet (or perhaps even the printing press); how it will change the way we think about business, health, politics, education, and innovation; and how we can protect ourselves from its hazards. Featured Speakers Kenneth Cukier Data Editor, The Economist  Co-author, Big Data: A Revolution That Will Transform How We Live, Work, and Think Viktor Mayer-Schonberger Professor of Internet Governance and Regulation, Oxford Internet Institute, Oxford University Co-author, Big Data: A Revolution That Will Transform How We Live, Work, and Think    To RSVP for the event, click on the red button or go to the event page:     http://newamerica.net/events/2013/big_data_book_launch  For questions, contact Stephanie Gunter at New America at (202) 596-3367 or gunter@newamerica.net.  www.NewAmerica.net

Wal-Mart’s Weak Forecast Show how Poor and Middle-Class Is Being Squeezed

Washington Post, February 21, 2013

NEW YORK — As the fortunes of many Americans go, so goes Wal-Mart, so goes the economy.

Even as the world's largest retailer on Thursday reported an 8.6 percent rise in fourth quarter profit during the busy holiday shopping season, it offered a weaker forecast for the coming months. The problem? The poor and middle-class Americans Wal-Mart caters to — and who are big drivers of spending in the U.S. — are struggling with rising gas prices, delayed income tax refunds and higher payroll taxes.

Melanie M. Burkhardt, a mother of two teenagers who shops at Wal-Mart, is one of those people. Burkhardt, a Waycross, Ga., resident, said she's been hit with a double whammy: the payroll tax hike, which has cut her household monthly income by $260, and higher gas prices.

"We had to do a flip on our budget," said Burkhardt, a legal assistant who plans to cut back on her trips to Wal-Mart. "This is money we used for things like going to a movie or splurging at Olive Garden. Not anymore."

It's widely known that Americans in the lower income brackets continue to struggle even as higher earners benefit from improved housing and stock markets, but Wal-Mart's results signal that matters may be getting worse for the nation's poor and middle-class. Wal-Mart is the latest in a string of big-name companies from Burger King to Zale to say those Americans are being squeezed by new challenges. But since Wal-Mart accounts for nearly 10 percent of nonautomotive retail spending in the U.S., it is a bellwether for the economy.

"Wal-Mart moms are the barometer of the U.S. household," said Brian Sozzi, chief equities analyst at NBG Productions who follows Wal-Mart. "Right now, they're afraid of higher taxes and inflation."

Indeed, while wealthier households have seen their stock portfolios grow, poor and middle-class Americans have struggled to regain their financial footing since the recession ended more than 3 ½ years ago.

Stocks have roughly doubled since June 2009. Dividends and capital gains from stocks, which disproportionately benefit higher-income Americans, are taxed at lower rates compared with ordinary income

And while incomes for most Americans have failed to keep pace with inflation since the recession, that's been particularly true for middle and lower-income earners.

Median household income, adjusted for inflation, fell 1.5 percent to $50,054 in 2011 compared with 2010, the latest periods for which figures are available, according to the Census Bureau. That was down 8.1 percent from 2007, just before the recession began. (The median is the point halfway between the highest and lowest levels.)

But lower and middle-income households fared worse: The share of overall income earned by the bottom 80 percent of households shrank in 2011, while the income for the top 20 percent grew. And in 2012, inflation-adjusted hourly pay barely rose, inching up 0.3 percent.

Another hurdle for lower- and middle-income Americans has been the jump in gas prices since mid-January. The average price for a gallon of gas rose 47 cents in the past month to $3.78 on Thursday, according to AAA.

Tax changes also have hit the nation's lowest earners especially hard. On Jan. 1, Social Security payroll taxes rose 2 percentage points after a temporary tax cut expired. That sliced about $1,000 from the take-home pay of a household earning $50,000. Since the Social Security tax is levied against income only up to $114,000, it disproportionately affects middle- and lower-income households.

An even larger challenge for many lower-income Americans has been the government's delay in processing income taxes and paying refunds. That's because income tax rates weren't set until a last-minute deal between the White House and Congress on Jan. 1. So the IRS pushed back the start of tax-filing season to Jan. 30, two weeks later than usual.

As a result, by Feb. 14 the government had paid only $55 billion in refunds, down from $77 billion at the same time last year, according to an estimate by UBS. That drop of $22 billion is more than twice the impact of the higher payroll tax. Refunds have accelerated recently and will eventually be paid out, but the impact still can be felt by many taxpayers: About 78 percent of taxpayers receive refunds, and the figure rises to 82 percent for those reporting income below $50,000.

Wal-Mart, based in Bentonville, Ark., said while its business has been volatile since December, the month of February, in particular, has been "slower than planned" largely due to the tax refund delay. The company said that resulted in Wal-Mart customers cashing about $1.7 billion in income tax refunds year to date, compared with $3 billion for the same period a year ago.

Bill Simon, president of Wal-Mart's U.S. namesake division, said shoppers used their refund money last year to buy TVs ahead of the Super Bowl. This year, the retailer said it isn't sure how customers will use the additional money when they get it, but some analysts say the most likely scenario is that they'll save it.

Wal-Mart said it's also unclear how the payroll tax will affect customers' spending habits, although Simon said shoppers are "talking about it." JP Morgan estimates that the payroll tax increase will equate to $70 a month less in take home pay for Wal-Mart shoppers, assuming an average annual income of $42,500. As a result, Wal-Mart is offering smaller packaging and less expensive products.

Wal-Mart earned $5.6 billion, or $1.67 per share, during the fourth quarter that ended Jan. 31, up from $5.16 billion, or $1.50 per share, a year earlier. Results were helped by a lower tax rate, which was 27.7 percent, compared with the rate of 30.9 percent a year ago. Net sales rose 3.9 percent to $127.1 billion.
Earnings topped Wall Street estimates of $1.57 per share, but sales fell short of the $127.8 billion analysts were expecting.

During the current quarter, Wal-Mart says it expects earnings to range from $1.11 to $1.16 per share, below the $1.18 per share analysts polled by FactSet are expecting. For its namesake U.S. business, Wal-Mart expects first-quarter revenue at stores open at least a year, a measure of a retailer's health, to be unchanged from a year ago. The pace of revenue growth has slowed in recent quarters, and some analysts believe Wal-Mart's forecast could be too optimistic.

For the year, Wal-Mart expects earnings of between $5.20 and $5.40 per share, while analysts expect $5.38 per share.

Despite the subdued forecast, investors were bracing for a weaker report after Bloomberg published a story Friday that leaked an email from an executive characterizing the first two weeks of February as "a total disaster." Shares fell that day, but investors appeared to be relieved on Thursday that Wal-Mart's outlook wasn't worse. Shares rose about 1 percent, or $1.05 per share, on Thursday to close at $70.26.

Less Innovation, More Inequality

Edmund S. Phelps, The New York Times, February 24, 2013

ONE source of the outsize inequalities in America is the dynamism that made economic activity so rewarding. An economy open to new concepts and novel ventures is bound to generate unequal gains. To tax all of those gains would close off the prospects for success that many entrepreneurs need if they are to undertake ambitious ventures — a big mistake. But it would also be a mistake to misunderstand the relation of inequality and innovation. It is less innovation — not more — that has widened inequality in the United States in recent decades.

America's peak years of indigenous innovation ran from the 1820s to the 1960s. There were a few financial panics and two depressions, to be sure. But in this period, a frenzy of creative activity, economic competition and rapid growth in national income provided widening economic inclusion, rising wages for all and engaging careers for most. Innovations gave workers better tools to work with and better products to make, thus lifting their wages. Then this innovation began to retreat, most of it to an area of land along the West Coast. In the early 1970s the rate of indigenous innovation (as measured by its estimated contribution to the rate of growth in labor productivity) dropped by about half — to around 1 percent since then, from about 2 percent before then.

The economist Robert J. Gordon has noted this slowdown in innovation, which he lays to the end of big breakthroughs. My view is that innovation has declined in the everyday processes that businesses tinker with incrementally as they try to become more productive over time. This decline of innovation across many fields — with notable exceptions like Silicon Valley, biotechnology and clean energy — has set back much of the earlier gains in productivity in American history.

It forced a broad devaluation of business assets, including employees. Wage restraint and reduced hiring followed, especially in the heavy manufacture of capital goods. As a result, wages of workers on the low rungs of the ladder slowed more than the wages of those in the middle, and the rising gap leveled off only in the 1990s. Unemployment rates tend to rise and fall in roughly equal proportion at all rungs of the ladder, and that happened between 1973 and 1985. (Over that time, the rate for white men went from 4.3 to 6.2 percent, for black men from 9.4 to 15.1 percent.) But the rise in unemployment was of greater consequence to those on the bottom rung, since their economic precariousness had been higher to start with. This is the heart of the inequality story.

The gap between the less advantaged and the more advantaged widened as the gap between high-school graduates and dropouts and Americans with college and graduate degrees rose. Many of the more advantaged could opt to retire on their resources, which fewer of the less advantaged could do. No wonder that between the mid-1970s and the mid-1990s, the labor force participation rate of white men — who were already relatively advantaged — drifted down while, in contrast, the participation rates of black and Latino men did not. (The rise of female participation rates, white and nonwhite, is another story.)

The inequality gaining attention recently is different in scale but not kind. In my 1997 book "Rewarding Work: How to Restore Participation and Self-Support to Free Enterprise," I wrote that wages had declined in the middle of the income distribution — "approximately at the border between the working class and the middle class" — relative to the affluent.

In short, there has been a widening between the middle and the top in both employment and wages. This "decompression," as some economists call it, began swelling with the return, around 2004, of the meager innovation and slow growth that had plagued the economy since the 1970s until the brief respite offered by the Internet boom that began in the mid-1990s.

The question that confronts policy makers is what steps to take.

There has been a drumbeat for investment in infrastructure. Advocates in business and government assert that such projects would create more work while they last and leave productivity higher in the end — though maybe not high enough to earn the revenue to cover the cost. The impact on jobs is clear over the short run. Economists have been sifting data for evidence that cities and states have won measurable gains in productivity from their capital projects. Whatever the answer, the debate has missed the point that the government will have to keep on finding new projects as old ones are completed. Such an endless series of projects will run into the law of diminishing returns. And even if returns hold up, bricks and mortar are not a solution to the decline in dynamism that — largely if not wholly — lies behind the slowdown in innovation.
Two ideas about how to revive the nation's dynamism are much in discussion.

Some observers attribute the bulk of the American economy's innovation — new products and new methods — to commercial applications of recent scientific advances. This pool of scientific advances, the argument goes, has pretty much run dry, with little replenishment in recent years, and so American innovation has run out of fuel. These scientists then say that stepping up government funding for scientific research could refill the pool, creating new possibilities for innovation into the indefinite future.

But this thesis is based on a mistaken premise. There is no evidence that innovating in America is or has been tethered to scientific advances. Some historians find that innovations largely ran ahead of scientific advances in the 19th century. The myriad new products of recent decades were mostly created by new commercial ideas and tinkering, not by new scientific advances.

A second approach, advocated by some economists and policy makers, is the explicit adoption of an industrial policy. It is argued that the government can spark innovation in the private sector by providing finance for development and marketing of new products or methods in companies or industries that offer promise, at least in the government's view, of boosting innovation. President Obama has implicitly endorsed such an approach. In his State of the Union address last month, he vowed to make America "a magnet for new jobs and manufacturing," favorably cited a "manufacturing innovation institute" in Youngstown, Ohio, that engages in 3-D printing, and announced the start of three more such manufacturing hubs, which will work with the Defense and Energy Departments "to turn regions left behind by globalization into global centers of high-tech jobs."

But this thesis that the government can adequately make the decisions once made by a well-functioning private sector raises serious doubts. Granted, enterprises in the private sector are prone to making mistakes when deciding to develop new products — since feasibility, cost and market reception are all unknown. The difficulty with a national industrial policy is that it places those decisions in the hands of government officials who are remote from the local expertise and insights that companies draw on for dynamic innovation. It is hard enough for venture capitalists and early-stage investors to make the right choices. It is unimaginable that the government can do it well. Besides, there is a moral hazard. Operating an industrial policy runs the risk that government officials — perhaps unconsciously — will do what is best for their political prospects rather than what they might agree was best if they were not directly involved.

What, then, can be done to address the slowdown in innovation and the attendant rise in inequality? There is no question that effective initiatives can be taken to address particular inequalities. Subsidies for employers to hire low-wage workers is one initiative that could be taken to address a particularly serious inequality. But there is no way to restore the sense of equality that prevailed as late as the 1960s without remedying the ills that caused inequalities to widen: the narrowing of high innovation to a handful of industries and the consequent slowing of economic growth to a snail's pace.

I am convinced that a return to the productivity growth and broad economic inclusion of the past will require nothing less than a revival of the high dynamism that underpinned that performance.

The needed revival will require a reform of the financial sector and of the business sector. In the financial sector it is necessary to put an end to the short-term thinking that unduly focuses on hitting quarterly earnings targets instead of aiming for long-range profitability and growth. Financial institutions' addiction to liquidity has made lending to business less attractive, while an addiction to diversified investments has left very few financial institutions willing to make money the old-fashioned way — by lending, or investing in projects for new products and methods.

In the business sector, it is necessary to put an end to infighting in established companies and the shortsightedness of chief executives who know they have only a few years in which to haul in some big bonuses. Better corporate oversight by boards and by government regulators is also essential.

Little of this will happen, however, and any government reforms will be undermined without a wider embrace of the old ethos of imagination, exploration, experiment and discovery. It is that ethos that laid the foundation for the broad-based prosperity of the American middle class in the postwar years, and without its revival, no amount of government intervention can fully mitigate the widening inequality that the slowdown in innovation has helped create.

Edmund S. Phelps, a Nobel laureate in economics, is director of the Center on Capitalism and Society at Columbia University and author of the forthcoming book "Mass Flourishing: How Grassroots Innovation Created Jobs, Challenge and Change."

______________________________________________________________
Stefaan G. Verhulst
Chief Research and Development Officer
The Governance Lab


Wagner Graduate School of Public Service
New York University
The Puck Building
295 Lafayette Street
New York, NY 10012
Tel: (212) 998 7530
Cell: (646) 573 1361

The Promise and Peril of the 'Data-Driven Society'

Steve Lohr, The New York Times, February 25, 2013

A small group of academics, business executives and journalists gathered at the M.I.T. Media Lab last Thursday, and the purpose was to toss out ideas and discuss the concept of "Data-Driven Societies." A daunting topic, ambitious and vague at once, it seems.

Up to now, the focus on the power and implications of Big Data technology has been involved social media, business decision-making and online privacy. Those are big subjects in their own right. So it's not surprising that the notion of a data-driven society has not been much considered.

But someone who has was host of the meeting: Alex Pentland, a computational social scientist at the Media Lab. He put his intellectual stake in the ground last year in a presentation posted on Edge.org, "Reinventing Society in the Wake of Big Data."

Mr. Pentland's starting point is that the most important data that is becoming available on a vast new scale is information about people's behavior. For example, he cites location data from cellphones and evermore consumption data as people increasingly use credit cards for even the smallest purchases. He distinguishes this behavioral data from less-telling data — about people's beliefs like Facebook communications or Google searches.

The fine-grained behavioral data, according to Mr. Pentland, opens the way to changing how we think about society and how a society is governed. Adam Smith and Karl Marx, he explains, thought about markets and classes, respectively. "But those are aggregates," he said. "They're averages."

Yet now, Mr. Pentland says, it becomes possible to track social phenomena down to the individual level and the social and economic connections among individuals. The ability to monitor these "micro-patterns," Mr. Pentland said, means "we're entering a new era of social physics."

What might that mean in practice? Reed Hundt, the chairman of the Federal Communications Commission in the Clinton administration, observed at the meeting that Big Data played a major role in the last election — a reference to the Obama campaign's deft use of data analysis to identify potential Obama voters and encourage them to cast their ballots.

"You get elected with Big Data, but you govern without it," Mr. Hundt said. "How much sense does that make?"

Mr. Hundt, chief executive for the Coalition for Green Capital, a nonprofit organization, pointed to the waste in a range of government incentive and benefit programs, from tax credits for solar panels to Social Security, that results from the across-the-board approach — or policy by averages, as Mr. Pentland might put it.

Instead, a data-driven approach to solar-energy incentives would concentrate government incentives to where the payoff is greatest in terms of efficiently generating alternative energy — larger buildings with a lot of roof space instead of small houses, Mr. Hundt said. A by-the-data model for benefits programs, he added, would suggest means-testing Social Security payments as well as adjusting payments locally for differences in costs of living.

"So all men are created equal, but are subsidized individually," Mr. Hundt quipped.

Intriguing, and perhaps wise policy, but it would also seem to be a redefinition of fairness as it applies to broad benefit programs, like Social Security and Medicare, which typically make standard payments and avoid means testing.

What are the chances such a data-driven course would be politically acceptable? If the data points the way to greater efficiency, why not, Mr. Hundt replied. After all, he said, a major role of government is to transfer income to people who would benefit most — better data, closely analyzed, means government can perform that role more effectively, Mr. Hundt said.

An underlying assumption of tilting toward a data-driven society is that, as one participant put it, "information over time wins out." That is, data will change attitudes and policy, combating bias and causing policy-making to be more of a science. To data optimists, then, the endless political squabbling and stalemate in Washington points to all the room there is for improvement.

In a Big Data world, the data-mining for patterns and insights to guide policy will be done automatically — by software algorithms. Of course, algorithms are created by people and they contain inferences and assumptions coded in. Those coded-in values shape the output — computer-generated predictions, recommendations and simulations.

That raises question of the human design and control of the computerized helpers in policy-making, as in other realms of decision-making. "At some point, you're in the hands of the algorithm," observed John Henry Clippinger, chief executive of the Institute for Data Driven Design, a nonprofit research and educational organization. "You're whistling in the dark if you don't think that day is coming."

CUSP in NYT: SimCity, for Real: Measuring an Untidy Metropolis

Steve Lohr, The New York Times, February 23, 2013

THE notion of a "science of cities" seems contradictory. Science is a realm of grand theory and precise measurement, while cities are messy agglomerations of people and human foible. But science is precisely the ambition of New York University's Center for Urban Science and Progress. Founded last year, the center has been getting under way in recent weeks, moving into new office space and firing off its first project proposal to the National Science Foundation.

The center's director is Steven E. Koonin, a Brooklyn native and graduate of Stuyvesant High School, who came to N.Y.U. after a stint in the Obama administration as the under secretary for science in the Department of Energy. He is both a theoretical physicist and science policy expert. The center shouldn't lack for intellectual rigor.

The initiative at N.Y.U. is part of a broader trend: the global drive to apply modern sensor, computing and data-sifting technologies to urban environments, in what has become known as "smart city" technology. The goals are big gains in efficiency and quality of life by using digital technology to better manage traffic and curb the consumption of water and electricity, for example. By some estimates, water and electricity use can be cut by 30 to 50 percent over the course of a decade.

Cities from Stockholm to Singapore are deep into smart city projects. The market looms as big, lucrative business for technology companies. "The Smart City movement," according to a report this month from IDC, a technology research firm, "is emerging and growing as a significant force of innovation and investment at all levels of government." The N.Y.U. center's partners include technology companies like I.B.M., Cisco Systems and Xerox, as well as universities and the New York City government.

City governments, like other institutions, have collected data for years to try to become more efficient. There have been some notable achievements, like CompStat, the New York Police Department's system for identifying crime patterns, introduced in the mid-1990s and later widely adopted elsewhere.

What is different today, says Dr. Koonin, is that digital technologies — sensors, wireless communication, storage and clever software algorithms — are advancing so rapidly that it is becoming possible to see and measure activities in an urban environment as never before.

"We can build an observatory to be able to see the pulse of the city in detail and as a whole," Dr. Koonin explains.

Dr. Koonin's digital "observatory" of urban life raises questions about privacy. He is keenly aware of that issue, and vows that the center is engaged in science rather than surveillance. For example, individuals' names or tax identification numbers would be stripped from personal records.

The collected data, he says, will be the raw material for modeling outcomes — say, the steps required to reduce electricity consumption in a high-rise office building or in an individual apartment. Those modeled predictions, he adds, can guide policy or inform citizens.

"I'd like to create SimCity for real," Dr. Koonin says, referring to the classic computer simulation game.
To help, Dr. Koonin is forging partnerships with government laboratories to tap their expertise in building complex computer simulations, like climate models for weather prediction.

The path to SimCity will come step by step, through tackling specific projects. The first one is a program to monitor and analyze noise. The largest single cause of complaints to New York's 311 phone and online service is noise. It is a quality-of-life issue, Dr. Koonin says, and one related to health, especially when noise disrupts sleep.

The 10-member project team includes music professors, computer scientists and graduate students. The group will use the city's 311 data, but also plans to employ wireless sensors — tiny ones outside windows, noise meters on traffic lights and street corners, perhaps a smartphone app for crowdsourced data gathering. To inform policy choices, data on noise limits for vehicles and muffler costs might be added to the street-level noise readings. Then, computer simulations could predict the likely effect of enforcement steps, charges or incentives to buy properly working mufflers for vehicles without them.

The project, Dr. Koonin says, might also pull in data on traffic flows, garbage pickup times and building classifications. For example, he says, a 2 a.m. garbage pickup could be routed to a neighborhood with little residential housing.

The hope, he says, is that a problem many people view as an inevitable, if grating fact of urban life can be made less severe. "It's the beginning of what we want to do," he explains.

Another project on the drawing board is technology for capturing thermal images of buildings across much of the city, as a starting point for research on energy use.

The center will focus its research and resources on one city — New York, as "a living laboratory."

That may give the center a leg up, since New York, under Mayor Michael R. Bloomberg, is at the forefront of using data to guide operations. In 2010, the city even set up a team of data scientists for special projects in the mayor's office.

ONE problem the team tackled was illegal conversions, landlords packing far more people into an apartment building or house than its zoning permits. These locations are fire hazards. Data from 19 agencies — including late tax payments, repair permits, foreclosure records and ages of the buildings — were mined to predict where to send the city's 200 building inspectors, who field more than 20,000 complaints a year.
Inspectors responding to complaints usually find high-risk conditions 13 percent of the time. Guided by data predictions, inspectors greatly improved their odds when pursuing complaint reports, finding those risky conditions 70 percent of the time, says Michael P. Flowers, analytics director in the mayor's office.

The city government is committed to giving the N.Y.U. center access to all its public data. That is a rich asset not only for research, but also for its potential to change government operations and public behavior. In many "smart city" projects, "the single biggest impact is transparency — the effect of measurement and communicating the data," observes Jonathan R. Woetzel, a director of McKinsey & Company in Shanghai, who heads the firm's consulting work with cities.

Communicating effectively with data, experts say, requires skills beyond technology. Jurij R. Paraszczak, director of smarter cities research at I.B.M., pointed to a water-management pilot study in Dubuque, Iowa, in which 150 households were equipped with sensors to measure and analyze their water use. They had the data, but the households were also grouped into teams for an informal competition. Water use dropped by 7 percent in two months.

"People live in cities," Dr. Paraszczak says. "So much of the equation is not just the data but how you encourage people to change their behavior."

The social ingredients of motivation, habit and incentives, according to Dr. Koonin, will be part of the research agenda at the N.Y.U. center. "The approach we're taking here is from sensors to sociologists. This has got to be science with a social dimension."

Quote: Recent advances in nanotechnology, microelectronics, optics, data compression and storage, cloud computing, information theory and synthetic biology could help make possible investigations that were unimaginable before. For instance, scientists might extend the value of traditional brain scans by implanting nanosensors, wireless fiber-optic probes or genetically engineered living cells to penetrate brain tissue and report which neurons are firing and when in response to various stimuli.

 

 

February 23, 2013

The Next Frontier Is Inside Your Brain

By PHILIP M. BOFFEY

http://www.nytimes.com/2013/02/24/opinion/sunday/the-next-frontier-is-in-your-brain.html?ref=opinion&pagewanted=print

The Obama administration is planning a multiyear research effort to produce an “activity map” that would show in unprecedented detail the workings of the human brain, the most complex organ in the body. It is a breathtaking goal at a time when Washington, hobbled by partisan gridlock and deficit worries, seems unable to launch any major new programs.

This effort — if sufficiently financed — could develop new tools and techniques that would lead to a much deeper understanding of how the brain works. The ultimate aim, probably not reachable for decades, is to answer such fundamental questions as how the brain generates thoughts, dreams, memories, perception and consciousness — and to find ways to intervene and influence such brain activities. It may also be possible to determine how the brain changes over time in response to learning.

We are a long way from that kind of understanding today. Scientists using electrodes and existing imaging technologies have been able to study how individual neurons and small networks of neurons respond to stimuli. But the human brain has some 100 billion neurons, each interacting with perhaps 10,000 other neurons through complex circuitry that no existing technology has the speed or resolution to track. All told, there could be 1,000 trillion connections between neurons in the brain.

Scientists have been able to infer the main functions of certain regions of the human brain by studying patients with head injuries, brain tumors and neurological diseases or by measuring oxygen levels and glucose consumption in the brains of healthy people, according to Dr. Francis Collins, director of the National Institutes of Health. But as Dr. Collins explains, this is like listening to the string section alone instead of the entire orchestra.

The sweeping scope of the new initiative, which has not yet been officially unveiled, was revealed by John Markoff in The Times on Monday. Fortunately, there is a strong base of knowledge to build on. Researchers have already made significant discoveries about brain functioning. They have identified how neurons behave at the point where anesthetized patients lose awareness, bringing us a step closer to understanding the nature of consciousness. They have linked certain areas of the brain to musical creativity and other areas to the formation of emotions and habits.

Scientists have even determined what animals are dreaming by first having them walk through certain locations in a fixed order and recording which neurons are activated. Then when the animal is sleeping, they can see if the same neurons are firing in the same order, an indication that the animal is probably dreaming about the walking it had just done. This rather simple experiment involves putting electrodes in the brain to record perhaps 100 neurons at a time. To really understand what is happening when an individual dreams, scientists will need to record what happens to many thousands or possibly millions of neurons as the dream is unfolding.

Recent advances in nanotechnology, microelectronics, optics, data compression and storage, cloud computing, information theory and synthetic biology could help make possible investigations that were unimaginable before. For instance, scientists might extend the value of traditional brain scans by implanting nanosensors, wireless fiber-optic probes or genetically engineered living cells to penetrate brain tissue and report which neurons are firing and when in response to various stimuli.

There should be clinical benefits as well. The knowledge developed could enable biomedical scientists to find more accurate ways to diagnose and treat depression, schizophrenia, dementia, autism, stroke, Parkinson’s and other illnesses or injuries of the brain.

President Obama hinted at broad ambitions for scientific advancement in his State of the Union address, saying, “Now is the time to reach a level of research and development not seen since the height of the space race.” He mentioned mapping the human brain, but it’s more likely that scientists will start with smaller brains and central nervous systems — like those of worms, fruit flies, zebra fish and small mammals — before they move on to primates. No firm budget exists yet, but some leading researchers say this initiative may require more than $300 million a year, or some $3 billion over the first decade, in federal support. Whether that is new money or drawn from existing well-financed programs, it is an investment worth making.

Of the big scientific programs in the past half-century, few if any were as daunting as the brain project. The race with Russia to land men on the Moon in the 1960s was comparatively straightforward because it was largely achieved with technologies that already existed. The Human Genome Project, completed a decade ago, had a clearly defined goal — to identify the complete sequence of genes on every chromosome in the body — and there was little doubt it was achievable; the only question was how fast and at what cost.

By contrast, the brain project will have to create new tools to explore an organ that is the seat of human cognition and behavior. A task of that magnitude can truly capture the imagination. 

 

CFR: Big Data, Better Global Health

Big Data, Better Global Health

Author: Thomas Bollyky, Senior Fellow for Global Health, Economics, and Development
February 21, 2013

http://www.cfr.org/global-health/big-data-better-global-health/p30042

Bill Gates, Margaret Chan, the Director General of the World Health Organization (WHO), and other experts and leaders gathered this month in Geneva for a very important meeting on a very unimportant-sounding subject: global disease estimates.

The impetus was the release of the Global Burden Disease (GBD) Study, the most comprehensive and ambitious effort to date to quantify the world's health status. Led by Chris Murray and his colleagues at the Institute for Health Metrics and Evaluation (IHME) and funded by the Bill & Melinda Gates Foundation, the GBD study involved 486 collaborators from 302 institutions in 50 countries. In a field in which donors and policymakers have lacked basic health data, such as birth and death registries, for many countries, the GBD study assesses 291 diseases and injuries and 67 risk factors in 187 countries over a 20-year period (1990-2010).

The British medical journal, The Lancet, devoted an entire triple-length issue to the study and compared it to the Human Genome Project, the international initiative that mapped man's genetic code. Other health experts criticized the GBD study as nontransparent in its methodology and lacking the accountability of WHO-generated data.

The recent meeting in Geneva produced consensus on principles for generating future global health estimates, but left the more fundamental question—the use of these estimates–unaddressed. Evidence-based global health policymaking is impossible without better data, but it is far from clear that such policymaking will now occur with better data either.

Indeed, the GBD study is unlikely to dramatically alter donor policies or mobilize significant new foreign aid. The more likely and better use of the study and its future iterations are as evidence and sources of accountability for national governments in addressing the needs of their citizens. As such, the legacy of the study may be helping to usher in the emerging era of global health, in which donor aid matters less and improved governance, efficiency, and better collaboration among trade, regulatory, and technical agencies matter more.

The Global Burden of Disease

The findings of the study are important, but in many respects, not new. WHO published two previous GBD studies, also led by Murray, in 1996 and 2004. Like the most recent Study, those reports showed that cancers, diabetes, cardiovascular disease, and respiratory illnesses – noncommunicable diseases (NCDs)--would far surpass the burden of infectious diseases and maternal and child mortality in developing countries and pose severe challenges to their health-care systems. Other studies have also confirmed those conclusions.

The chief contributions of the latest study are its analytical rigor, scope, and demonstration of the shocking speed with which epidemiological and demographic transitions are occurring globally. Life expectancies are rising in most developing countries. The dominant health risks globally are now behavioral--such as tobacco use, high blood pressure, and household air pollution. These increases in longevity and exposure to behavioral risks are outpacing the improvement of developing country governments' health and regulatory systems. As a result, more people are falling susceptible to NCDs faster and suffering more chronic disability than expected. NCDs are now responsible for more than 70 percent of the death and disability that occurs in many parts of Latin America, the Middle East, and Asia.

Percent of Disability-Adjusted Life Years Lost to NCDs in 2010

^{Source: IHME}

Global Health Funding and Policies

Previous GBD studies helped catalyze important policy initiatives to address the changing health needs of developing countries. In 2002, WHO concluded an international treaty on tobacco control, to which 175 countries are now parties. In 2011, the United Nations General Assembly devoted a high-level meeting to NCDs, the first such meeting on a health topic other than HIV/AIDS.

Yet previous GBD studies have not dramatically altered the distribution of donor resources. The U.S. government is the largest provider of global health aid, but little of that funding goes to NCDs. Past GBD studies have shown tobacco use annually kills more people worldwide than HIV/AIDS, tuberculosis, and malaria combined, but the U.S. government spent only $7 million of its $8.4 billion global health budget in 2010 on international tobacco control. WHO spending on NCDs and their risk factors increased since the 1996 GBD study, but still represents only 6 percent of the WHO budget and may be decreasing. NCD-related aid from the World Bank and regional development banks has declined since 1996.

^{Source: IHME, Financing Global Health 2012: The End of the Golden Age? (2013)}

According to a new IHME report, only $185 million of the $28.2 billion spent globally on development assistance for health in 2010 was dedicated to NCDs. Donors spent $300 for each year lost to disability from HIV/AIDS, $200 for malaria, and $100 for TB, but less than $1 for NCDs. Nearly half of the funding for NCDs derives from a single source: the Bloomberg Family Foundation.

Donors have been reluctant to devote resources to diseases increasing fastest in China, India, and other middle-income countries, even though those countries are home to 70 percent of the world's people living on less than $2 per day. Policymakers dismiss NCDs because their dominant risk factors are behavioral, despite study after study demonstrating that the NCD epidemic in developing countries is increasing fastest among the poor who lack access to adequate nutrition, education, and health care. Finally, donors have noted that substantial R&D occurs on NCDs, even though little goes to developing medicines and diagnostics usable in low-resource, low-infrastructure settings.

What Better Data Can Do for Global Health

The funding priorities of donors and intergovernmental institutions reflect humanitarian and political-economic considerations in which the global burden of disease plays only a modest part. Particularly amid tightening donor budgets, there is little reason to expect new aid to address the health trends that the GBD study identifies. The Bill & Melinda Gates Foundation, for example, hailed the study as confirmation of the effectiveness of the foundation's infectious-disease focused strategy, but not as a reason for the foundation to devote additional resources to NCDs.

Yet the world's health needs are changing. In the past, global health initiatives were dedicated to delivering food and health technologies to the world's poor. These initiatives required tremendous donor resources, but could achieve progress even in countries with dysfunctional governments. But donors and international technical agencies cannot enforce the regulations now needed on smoke-free public places or maintain primary health care programs to manage diabetes. This is the domain of developing countries, where health spending is increasing and, on average, outstrips aid by a ratio of 18 to 1. The fundamental challenge in this emerging era of global health will be better governance and accountability for that health spending and its efficiency, not newer medicines.

The GBD study has a critical role to play in addressing that challenge. On March 5, IHME will release its country-level GBD assessments and will announce that those estimates will be continuously updated. These data would empower national governments to identify the NCDs and risk factors especially prevalent among their poor and increasing fastest.

International technical agencies and more experienced developed country health, trade, and regulatory officials can help poorer national governments design and implement practical programs for addressing priority needs even in low-infrastructure settings. Modest donor aid would allow pilots of such programs. The ongoing updates of the GBD study will help identify which pilots are working, assess their cost-efficiency, and improve the accountability of national governments for implementing them.

Fulfilling that transformative potential will depend on the sustained openness of the GBD study. Disputes are inevitable among scientists in any ambitious enterprise, but policymakers and funders need confidence before acting. IHME allows public access to its raw data and methodology eighteen months after publication, but will need to do so sooner and with greater guarantees that its many collaborators will do likewise to ensure that the study remains relevant for the governments that can put its findings to greatest use.

Nassim Taleb: Beware the Big Errors of ‘Big Data’

Beware the Big Errors of 'Big Data'

• By Nassim N. Taleb
• 02.08.13

http://www.wired.com/opinion/2013/02/big-data-means-big-errors-people/

We're more fooled by noise than ever before, and it's because of a nasty phenomenon called "big data." With big data, researchers have brought cherry-picking to an industrial level.

Modernity provides too many variables, but too little data per variable. So the spurious relationships grow much, much faster than real information.

In other words: Big data may mean more information, but it also means more false information.

Nassim Taleb

Nassim N. Taleb is the author of Antifragile, which this piece is  adapted from. He is a former derivatives trader who became a scholar and philosophical essayist. Taleb is currently a distinguished Professor of risk engineering at New York University's Polytechnic Institute. His works focus on decision making under uncertainty, in other words "what to do in a world we don't understand." His other book is The Black Swan: The Impact of the Highly Improbable.

Just like bankers who own a free option — where they make the profits and transfer losses to others – researchers have the ability to pick whatever statistics confirm their beliefs (or show good results) … and then ditch the rest.

Big-data researchers have the option to stop doing their research once they have the right result. In options language: The researcher gets the "upside" and truth gets the "downside." It makes him antifragile, that is, capable of benefiting from complexity and uncertainty — and at the expense of others.

But beyond that, big data means anyone can find fake statistical relationships, since the spurious rises to the surface. This is because in large data sets, large deviations are vastly more attributable to variance (or noise) than to information (or signal). It's a property of sampling: In real life there is no cherry-picking, but on the researcher's computer, there is. Large deviations are likely to be bogus.

We used to have protections in place for this kind of thing, but big data makes spurious claims even more tempting. And fewer and fewer papers today have results that replicate: Not only is it hard to get funding for repeat studies, but this kind of research doesn't make anyone a hero. Despite claims to advance knowledge, you can hardly trust statistically oriented sciences or empirical studies these days.

This is not all bad news though: If such studies cannot be used to confirm, they can be effectively used to debunk — to tell us what's wrong with a theory, not whether a theory is right.

Another issue with big data is the distinction between real life and libraries. Because of excess data as compared to real signals, someone looking at history from the vantage point of a library will necessarily find many more spurious relationships than one who sees matters in the making; he will be duped by more epiphenomena. Even experiments can be marred with bias, especially when researchers hide failed attempts or formulate a hypothesis after the results — thus fitting the hypothesis to the experiment (though the bias is smaller there).

This is the tragedy of big data: The more variables, the more correlations that can show significance. Falsity also grows faster than information; it is nonlinear (convex) with respect to data (this convexity in fact resembles that of a financial option payoff). Noise is antifragile. Source: N.N. Taleb

The problem with big data, in fact, is not unlike the problem with observational studies in medical research. In observational studies, statistical relationships are examined on the researcher's computer. In double-blind cohort experiments, however, information is extracted in a way that mimics real life. The former produces all manner of results that tend to be spurious (as last computed by John Ioannidis) more than eight times out of 10.

Yet these observational studies get reported in the media and in some scientific journals. (Thankfully, they're not accepted by the Food and Drug Administration). Stan Young, an activist against spurious statistics, and I found a genetics-based study claiming significance from statistical data even in the reputable New England Journal of Medicine — where the results, according to us, were no better than random.

Big data can tell us what's wrong, not what's right.

And speaking of genetics, why haven't we found much of significance in the dozen or so years since we've decoded the human genome?

Well, if I generate (by simulation) a set of 200 variables — completely random and totally unrelated to each other — with about 1,000 data points for each, then it would be near impossible not to find in it a certain number of "significant" correlations of sorts. But these correlations would be entirely spurious. And while there are techniques to control the cherry-picking (such as the Bonferroni adjustment), they don't catch the culprits — much as regulation didn't stop insiders from gaming the system. You can't really police researchers, particularly when they are free agents toying with the large data available on the web.

I am not saying here that there is no information in big data. There is plenty of information. The problem — the central issue — is that the needle comes in an increasingly larger haystack.

Survey: Feds believe analytics can save lives, money

Read the Report

Big Data and the Public Sector

 

Survey: Feds believe analytics can save lives, money

• By Frank Konkel
• Feb 20, 2013

A breakdown of some of the findings of the SAP/TechAmerica big-data study. (Graphic: TechAmerica)

A majority of federal IT officials believe big data and other analytics tools have the potential to make the government more efficient and improve public health and safety, according to a new study released by the TechAmerica Foundation on Feb. 20.

The survey, commissioned by SAP and conducted by pollsters Penn, Schoen and Berland, gleaned insight from 200 public IT officials at state and federal levels, of whom 75 percent or more said big data could help government cut the federal budget, save lives, enhance citizens' quality of lives and reduce crime.

TechAmerica President Jennifer Kerber said the survey results backed a big-data report the foundation presented to the Congressional High-Tech Caucus in October 2012, which attempted to define "big data" and presented several use cases detailing how agencies were making use of it.

"The findings from this study underscore the infinite potential of big data and reaffirm the findings of our big data commission," Kerber said in a statement. "That governments can save money and improve their service to citizens is clear from this study but it's also clear that we must find ways to overcome adoption barriers -- quickly."

Highlights from the survey include:

•  Substantial budget cuts: Federal IT officials say real-time analytics of big data can help the government cut at least 10 percent annually from the federal budget by discovering wasteful spending. For example, such analytics can detect improper healthcare payments before they occur.
•  Lifesaving potential: According to 87 percent of federal IT officials and 75 percent of state IT officials, the use of real-time big data solutions will save a significant number of lives each year. For example, medical researchers can aggregate information about healthcare outcomes to reveal patterns that lead to more effective treatments and detection of outbreaks.
•  Crime reduction: 75 percent of state IT officials see the practical benefits of big data in medicine and public safety as extremely beneficial. Police departments are currently using big data technology to develop predictive models about when and where crimes are likely to occur, helping dramatically reduce the overall crime rate in specific locations.
•  Enhanced quality of life: Real-time big data is helping the government improve the quality of citizens' lives, according to 75 percent of federal IT officials. For example, by gaining insight into huge volumes of data across agencies, the government can provide improved, personalized services to citizens.

While the study focuses heavily on views of big data in the future tense, many challenges remain for agencies if big data is to be the widely used tool it is sometimes touted to be.

Almost half of federal IT officials surveyed ranked privacy concerns as the "biggest barrier" toward big data adoption, citing the perceived relationship between big data and "Big Brother."

About 40 percent of federal and state It officials surveyed said high costs were another barrier cash-strapped agencies will have a tough time overcoming; and that database queries take "too long using traditional database technology.' Another 42 percent on the federal side said returns on investment lacked sufficient clarity.

Big data is not yet part of the government's "holistic" IT approach, according to Dante Ricci, senior director of SAP's global public sector, but the biggest challenges are on the human side of the equation, not the technological side.

"What we've seen so far by this survey is the education of customers and the knowledge of what big data can deliver has grown exponentially over the last 18-24 months," Ricci said. "Is this something done holistically in the government right now? No. But we're still learning how to best understand how people, policy, operational processes and user experience can fit together with big data technology enablers across enterprise."

Ricci added that certain technologies, like memory, necessary for big data platforms are getting cheaper, and touted that big data can lower the total cost of an IT landscape if implemented properly.

While it can be expensive to implement a big data solution, Ricci said agencies can get the most bang for their bucks by outlining expected outcomes, "starting small and targeting a highest-value use case," before executing a project.

About the Author

Frank Konkel is a reporter for FCW. Connect with him on Twitter at @Frank_Konkel.

 

Tim Wu in the New Yorker: Does a Company Like Apple Need a Genius Like Steve Jobs?

• Idea of the Week: Open Vs. Closed »

February 19, 2013

Does a Company Like Apple Need a Genius Like Steve Jobs?

Posted by Tim Wu

The old tech adage is that "open beats closed." In other words, open technological systems, or those that allow interoperability, always beat their closed competitors. This is an article of faith for certain engineers. It's also the lesson from Windows' defeat of the Apple Macintosh in the nineteen-nineties, Google's triumph in the early aughts, and, more broadly, the success of the Internet over its closed rivals (remember AOL?). But is it still true?

The adage has been seriously questioned over the last few years, primarily because of one firm. Apple, ignoring the ideals of engineers and the preaching of tech pundits, steadfastly stuck to a semi-closed strategy—or an "integrated" one, as it likes to say—and defied the rule. Compared to its rivals, Apple is far more structurally integrated. It owns its hardware, software, and retail operations. It also curates and blocks competitors a lot more. Oh, yes, and by doing this, the company became the most valuable one on earth. Last quarter, Apple made more profit than Amazon has made over its entire lifespan.

But now, over the last six months, in ways little and large, Apple has begun to stumble. Accuse me of overreading, but I propose a revision of the old adage: closed can beat open, but you have to be genius. Under normal conditions, in an unpredictable industry, and given regular levels of human error, open still beats closed. Stated a different way, a firm gets to be closed in exact proportion to its vision and design talent.

To explain, I need to first be careful about what I mean by "open" and "closed," words that are widely used in the tech industry, but with various meanings. The truth is that no company is completely open or completely closed; they exist on a spectrum, somewhat like the one that Alfred Kinsey used to describe human sexuality. Here, I mean it as the combination of three things.

First, "open" and "closed" can refer to how permissive a tech firm is, with respect to who can partner with or interconnect with its products to reach consumers. We say an operating system like Linux is "open" because anyone can design a device that runs Linux. In contrast, Apple is very selective: it would never license iOS to run on a Samsung phone, or sell the Kindle in an Apple store. Second, openness can describe how impartially a tech company treats other firms in comparison to how it treats itself. Firefox, the browser, treats most Web sites about the same. Apple, in contrast, always treats itself better. (Try removing iTunes from your iPhone.) Third, and finally, it describes how open, or transparent, the company is about how its products work, and how to work with them. Open-source products, or those that rely on open standards, make their source code available widely. Meanwhile, a firm like Google might be open in many respects, but it guards things like its search-engine code very carefully. In tech, the standard metaphor to describe this last difference is that of a cathedral versus a bazaar.

No private firm is entirely open, though some non-profits, like Mozilla, come close. Similarly, no firm today can afford to be entirely closed. A platform owner gains from having good apps available (consider, um, the iPhone without Google Maps), and so too much blocking will destroy what makes the product valuable. Even Apple needs to be open enough not to annoy consumers too much. You can't run Adobe's Flash on an iPad, but you can plug nearly any kind of earphones into it.

The idea that "open beats closed" is a new one. For most of the twentieth century, integration was widely believed to be the superior form of business organization. Closed or integrated designs come with advantages long recognized and even trumpeted by economists. Coördination is a key advantage: in theory, with one firm coördinating every aspect of a given product's features, the result can work better than its uncoördinated rival. The economist Joseph Farrell called this the "internalization of complementary efficiencies." If that doesn't stick in your ear, consider it the Disney World effect. Disney exercises iron (or near-iron) control, and the amusement park operates more smoothly and impressively than, say, a typical country fair.

Andrew Carnegie relied on logic similar to Apple's when he integrated ore mining with steel production in U.S. Steel. The old Hollywood studios of the nineteen-thirties and forties integrated acting, writing, production, and theatres into one firm, and successfully chased everyone else out of the industry. I.B.M. was closed, and the old A. T. & T. monopoly was the ultimate closed system: you weren't allowed to own your own telephone, let alone use one made by someone else.

The conventional wisdom began to change in the nineteen-seventies. In technology markets, from the eighties through the mid-aughts, open systems repeatedly defeated their closed competitors. Microsoft Windows defeated its rivals by being more open: unlike Apple's operating system, which was technically superior, Windows ran on any hardware, and ran nearly any software. At the same time, Microsoft also defeated a vertically integrated I.B.M. (Remember Warp O.S.?) Google was boldly open in its original design, and sailed past the selective pay-for-placement design of Yahoo. Most of the winner firms in the eighties to the aughts, like Microsoft, Dell, Palm, Google, and Netscape, were open. And the Internet itself, a government-funded project, was both incredibly open and incredibly successful. A movement was born, and with it the rule that "open beats closed."

The triumph of open systems revealed a major defect in closed designs. As a matter of economic theory, in a state of perfect information, a central designer should be able to produce a better product. But that only follows if the future is predictable, and if you ignore the tendency of humans to make boneheaded mistakes. In a closed system, with one decision-maker, errors are very costly. Stupid decisions, or compromising the product for short-term profit, will make it not just a bit worse but much worse than the open competitor. For example, AOL's "walled garden" of the nineties tried to guess what users wanted, but AOL made lots of mistakes, and it was ultimately no match for the open Web.

An open product, in contrast, is better buffered against human error, because no one entity makes a decision that can destroy the product. The economists Tim Bresnahan and Shane Greenstein, writing in the nineteen-nineties, described this as "divided technical leadership," and they meant that as a good thing. The product is the collective result of many, and sometimes thousands, of decision-makers. An open product can also take advantage of collective, voluntary contributions of the masses, a point emphasized by Yochai Benkler. Consequently, an individual Wikipedia entry might be lousy and contain errors, but the entire corpus will remain impressive. In the mid-nineteen nineties, Windows wasn't as intuitive as Macintosh, but all the accessories and all the applications collectively made it a superior product.

Which brings us to the aughts, and Apple's great run. For about twelve years, Apple successfully beat the rule. But that's because it had the best of all possible systems; namely, a dictator with absolute control who was also a genius. Steve Jobs was the corporate version of Plato's ideal: the philosopher-king more effective than any democracy. The firm was dependent on one centralized mind, but he made very few mistakes. In a world without errors, closed beats open. Consequently, for a while, Apple bested its rivals.

So what's a technology firm to do? Each faces the open/closed question, and here is how to answer it. First and foremost, there will always be a complex tradeoff between closed and open designs, so there is no use being too religious in either direction. It is easy to underestimate open designs (no one thought Wikipedia would work), but even open systems need some points of control. In the end, the better your vision and design skills, the more closed you can try to be. If you think your product designers can duplicate the nearly error-free performance of Jobs over the past twelve years, go for it. But if mere mortals run your firm, or if you're facing an extremely unpredictable future, the economics of error suggest an open system is safer. Maybe rely on this test: wake up, look in the mirror, and ask yourself, Am I Steve Jobs?

Infographic: Are open compan

Read more: http://www.newyorker.com/online/blogs/newsdesk/2013/02/does-a-company-like-apple-need-a-genius-like-steve-jobs.html?printable=true&currentPage=all#ixzz2LOpo4Maa