The future — and predicting it — ‘ain’t what it used to be’

We want to know what to expect so we can prepare for it. It's more important than ever to get reliable information about what to expect, because the world is changing fast.

(Fotosmurf01/(<a href=''>Big Stock Photo</a>)

(Fotosmurf01/(Big Stock Photo)

Que sera, sera
Whatever will be will be
The future’s not ours to see
Que sera sera

Baseball sage Yogi Berra, would probably agree with the sentiment in that 1950s ditty, saying that the only thing he wouldn’t predict was the future, a future that he also said ‘ain’t what it used to be.’

Humans have always had a stubborn fascination with knowing the future. We want to know what to expect so we can prepare for it. It’s more important than ever to get reliable information about what to expect, because the world is changing faster and faster.

Whether it’s done by sifting through the tea leaves, peering into a crystal ball, or poring over mountains of data, prediction is a very inexact science. But the future must be ours to see. Climate change is beginning to wreak havoc. Just consider the extremes we’re seeing in weather. The loss of life and property caused by the recent flooding in Louisiana could have been at least ameliorated using more sophisticated prediction methods. And with today’s increasing global mobility, predicting disease has a greater importance viz. — the spread of Zika.

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One of the most famous prognosticators was Alvin Toffler, the author of “Future Shock,” who died at 87 in June. Toffler’s book, published in 1970, sold more than 6 million copies worldwide. It describes how, when change happens too fast, it results in social confusion and poor decision-making. As he put it: “Change is avalanching on our heads and most people are grotesquely unprepared to cope with it.”

Consider the brilliant economist John Maynard Keynes, who, like many of his famous contemporaries, failed to predict the great crash of 1929. In its Nov. 3, 1948, edition, the Chicago Tribune famously announced that Thomas E. Dewey had won the election that actually made Harry Truman president.

Toffler, though, was pretty much on the money, forecasting, in “The Third Wave” in 1980, the spread of the Internet and e-mail, interactive media, and cable television. AOL founder Steve Case cited Toffler as a formative influence on his thinking, calling him a real pioneer in helping people, companies and even countries lead into the future.

Among the features of post-industrial society that Toffler predicted were built-in obsolescence (such as the next generation of computers appearing before the end of the expected usability of the previous generation); whole branches of industry, such as manufacturing, dying off and new branches arising; people changing professions because the professions quickly become outdated; and people becoming nomadic to find work … something borne out with the current state of mass migration.

Today, some 46 years after Toffler’s original book, advanced generations of computers are making possible great progress with new methods of prediction. Using massive amounts of data, scientist today use a method called “predictive modeling” to study disease. Linking gene profiles to disease to help us better manage our health is a good example. Predicting disease patterns, and taking appropriate preventive measures, is another.

The Monte Carlo simulation, which has nothing to do with roulette or casinos, but rather is a technique using computer simulations unavailable in Toffler’s time, allows people to account for risk in such fields as finance, energy, manufacturing, research, and insurance. For example, using this method, one can calculate how long a nest egg will last in retirement over a range of market fluctuations.

Perhaps the most visible of predictors today are weather forecasters, who manage to get it right most but not all of the time. Big data analytics, this relatively new method only possible now with greater computing power, examines large data sets to reveal hidden patterns and unknown correlations. Weather-prediction researchers are using this to create both long-term models to help us understand climate change, and short-term models for day-to-day forecasts.

On the political front, Nate Silver, a statistician who analyses elections, correctly predicted the U.S. presidential election results in 49 states in 2008 and all 50 states in 2012. His prediction for the 2016 election is that Hillary will win.

Yes, we need to continue developing better ways of predicting the future, difficult as Yogi Berra claims that it is.

David Woods, Ph.D., is a Philadelphia-based medical writer and editor. A former editor in chief of the Canadian Medical Association Journal, he is the author of four books and more than 200 articles, editorials, and reviews in peer-reviewed health care publications.

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