Investigative reporter for the Times James Glanz brings some hard facts and dissenting opinions to bear on the current big claims about Big Data as “the new oil” for our economy. Glanz cites Northwestern economics professor Robert Gordon, who says that comparing Big Data to the impact of oil in the late nineteenth and early twentieth century in terms of economic impact is simply a silly form of exaggeration: “Gasoline made from oil made possible a transportation revolution as cars replaced horses and as  commercial air transportation replaced railroads. If anybody thinks that personal data are comparable to real oil and real vehicles, they don’t appreciate the realities of the last century.”

Nor does the parallel to the rise of the electricity grid hold much credence with some. In terms of numbers, the comparison is certainly tempting:  From 2005 to 2012 the volume of data on the internet increased 1696%. But the revolutions that the unleashing of electricity produced in manufacturing processes, ways of daily living, and transportation have no match in the rise of “Big Data” to date. In fact, during the time that has seen the increase in Big Data, we have experienced a lackluster economy where productivity, which had risen largely due to automation from the 1970s through the start of the 2000s, has actually shrunk. Productivity growth decreased 1.8% annually from 2005 to 2012.

In part making such outlandish claims is one of the hazards of predicting the future, always a difficult if not impossible task to get right. Yet it may also borrow something from the spirit of the times: We have grown so
accustomed to enormous, “revolutionary” sorts of changes in the last two decades that some believe the sheer size of the growth rate in Big Data must signal something equally unprecedented and huge on the horizon. Yet in the end some economists have observed that the new analytics, which companies use to mine Big Data, just allows those companies to cannibalize the customer base of their competitors, or, make the case for digital advertising over print and other traditional media even stronger. It creates incremental or sideways changes, not revolutions in the economy. And still others muse that the current framework for the use of Big Data may be just plain wrong. In the end, they posit, the context in which our futurists have placed Big Data, like cloud computing, may end up simply being  “a mirage.” Big Data and cloud computing may be incorporated into our economic and business practices in ways we have yet to even envision. I think we’ll just have to wait and see on this one . . .

There’s a semi-apocryphal story about Norbert Wiener, the brilliant, visionary MIT mathematician. It is said that he used to walk around the halls of the campus with his eyes closed and a finger on the wall to ensure that he did not lose his way. One day traveling what is fondly known as the “Infinite Corridor,” which stretches 825 feet from the main lobby of MIT’s central building west to east through 5 major buildings in all housing classrooms and offices. On one particular day, one of the classrooms in session  happened to have its door open and Norbert Wiener simply entered the classroom  and walked completely  around the perimeter and out the door again as he made his way toward his destination—to the silent amazement (and amusement) of the professor as well as
his students. 

Recently the New York Times published an excerpt from a long-lost article that Norbert Wiener wrote in 1949. Originally solicited by the oddball Sunday Times editor, Lester Markel,it was mysteriously either lost by Markel or abandoned by Wiener, or both. In any event, a researcher recently found the  among Wiener’s papers at the MIT archives. In the piece Wiener  about “what the ultimate machine age is likely to be.” He expounded  future automated systems well beyond what then existed and about smart computers and smart gauges that would integrate one machine with another machine various manufacturing processes.

Although he did not foresee the economic shift in the value of information versus manufacturing, the revolution he did envision was profound and his predictions dire: “These new machines have a great capacity for upsetting the
present basis of industry, and for reducing the economic value of the routine factory employee to a point at which he is not worth hiring at any price. If we combine our machine-potentials of a factory with the valuation of human beings on which our present factory system is based, we are in for an industrial revolution of unmitigated cruelty. . . Moreover if we move in the direction of  making machines which learn and whose behavior is modified by experience, we must face the fact that every degree of independence we give the machine is a degree of possible defiance of our wishes. The genie in the bottle will not willingly go back in the bottle, nor have we any reason to expect them to be well disposed to us. In short, it is only a humanity which is capable of awe, which will also be capable of controlling the new potentials which we are  opening for ourselves. We can be humble and live a good life with the aid of the machines, or we can be arrogant and die.”

Would that our writers and our thinkers and our leaders of corporations today, instead of blithely hailing the onslaught of robots and marveling at increased productively and the brilliance of our technology, had some of the compassion and wisdom that Wiener possessed in 1949.


  

If there is a dominant metaphor in the books and articles about the recent financial crisis, it is that of a machine, an invisible, incomprehensible engine that increased in complexity, scale, and speed until it grew out of control. Michael Lewis entitles his book The Big Short: Inside the Doomsday Machine (2010), making a forbidding allusion to the fated mechanisms that led to the meltdown on Wall Street. In Thirteen Bankers (2010), economist Simon Johnson refers to the derivatives process as a “securitization machine.” That machine automatically and efficiently created derivatives like credit default swaps. One leading investor who  figures prominently in The Big Short , Steve Eisman, called the derivative process the “engine of doom” and spoke of the “madness of the machine.” “It was like an unthinking machine,” he told Michael Lewis, “that could not stop itself.”

This automated electronic monster seemed to have a life of its own. And Wall Street, who had long reveled in the wonders of technology, marveled at it. Since the eighties, Wall Street companies had been hiring Ph.D. scientists who understood digital technology. Those scientists, called “the quants,” built the software machines that fueled tremendous growth in the financial industry. They applied mathematical models of uncertainty to financial data and increasingly complex products. They readily wrote new algorithms, built mathematical models to quantify risks, and devised procedures and operations to handle the new complexities. As a result, the markets worked faster, more efficiently. But as the years rolled on the financial instruments became more byzantine and opaque. Finally those products, which were designed to manage the risk, were actually creating new risks out of thin air through high-tech obfuscation.

Much of this complexity was done in the name of “innovation.” Financial innovation, like technological innovation, had become a good in and of itself. Alan Greenspan has long been a big proponent of innovation in financial markets.  In his autobiography, The Age of Turbulence (2007), Greenspan praised “the development of technologies that have enabled financial markets to revolutionize the spreading of risk. Three or four decades ago, markets could only deal with plain vanilla stocks and bonds. Financial derivatives were simple and few. But with the advent of the ability to do around-the-clock business real-time in today’s linked worldwide markets, derivatives, collateralized debt obligations, and other complex products have arisen that can distribute risk across financial products, geography, and time.” (488) According to Greenspan these financial innovations were all for the good. After all, they contributed to growth, productivity, and increases in market efficiency.

The quants also designed another type of machine, a manufacturing machine, if you will, for creating “innovative” derivatives. And they built a third type of machine: computer models that used scenarios to “demonstrate” how derivatives would perform under certain conditions. In effect, the software models, while complex in themselves, gave a powerful set of easy-to-use tools to Wall Street traders and salespeople. Thus they could start conversations with their customers about very complex derivative products. It didn’t seem to matter that most people on Wall Street didn’t understand them.

In Lecturing Birds on Flying: Can Mathematical Models Destroy Financial Markets? (2010),  Pablo Triana, himself a seasoned trader, says these models made it possible to demonstrate with mathematical precision how derivatives would produce returns under given conditions. And many people—both traders and investors—believed in the models. They trusted the numbers that were displayed in all their high-tech glory on the screens. Unfortunately they did not understand the underlying securities, the assumptions built into the models, or the methods by which the models were built.

In fact, the slick and sophisticated models created widespread overconfidence in the forecasts. The traders, the salespeople, and the investors looked at the numerical certainty of the models and were convinced by what they said, ignoring the fact that financial markets are by their nature unpredictable and vulnerable to crises. In some cases, the models, it seemed, just gave bankers justification for taking on more and more risk while at the same time appearing highly sophisticated to the outside world.

This belief in the truth of technology is not uncommon.  Alan Greenspan himself expressed a similar kind of blind faith in financial innovation and high-tech complexity when he compared the financial markets to a U.S. Air Force B-2 airplane. Our twenty-first century markets are too big, too complex, and too fast to be governed by twentieth century regulation and supervision, he argues toward the end of his autobiography. The movement of funds is too huge, the entire market system far too complex, the daily transactions far too numerous, to be understood and regulated. And this is OK. After all, a U.S. Air Force pilot does not need to know all the computer-aided adjustments that keep his B-2 in the air. Why should we expect to know how the markets behave?

But this analogy begs the question: After all, there’s a lot of solid scientific knowledge in the B-2. A team of top scientists and engineers worked for years to design, build, and test it. Other crews of highly skilled maintenance workers ensure that its systems are all working correctly before each flight. But the markets are at bottom a social system that does not operate according to such predictable laws.

 The nineteenth century didn’t see it that way. At that time, economists adopted certain scientific terms—equilibrium, pressure, momentum— to explain how the economy and financial markets operated, with the underlying assumption that these systems did follow laws similar to the laws of nature upon which sciences like physics and chemistry were based. But we are a long way from that kind of certainty in financial affairs. In the twentieth century, after World War I and the Depression, deep uncertainty started to color our understanding of markets as economists considered the role of human nature and its irrationality in markets.

The last thirty to forty years have seen the rise of two other major changes in the market: Volatility has become a major factor in modern markets. At the same time, and somewhat by coincidence at the beginning, computers came to play a dominant role in those markets. Those two major changes have developed in tandem and now are recombining to create new uncertainties on top of the old ones based on human nature. That combination of computer systems—with all their fallibilities, unintended consequences, and illusion of truth—and highly volatile markets is what we face today. And no one knows how the two will play off one another in the years to come.