The Future of Everything: The Science of Prediction

The Future of Everything: The Science of Prediction

David Orrell

Language: English

Pages: 464

ISBN: 1568583699

Format: PDF / Kindle (mobi) / ePub


For centuries, scientists have strived to predict the future. But to what extent have they succeeded? Can past events-Hurricane Katrina, the Internet stock bubble, the SARS outbreak-help us understand what will happen next? Will scientists ever really be able to forecast catastrophes, or will we always be at the mercy of Mother Nature, waiting for the next storm, epidemic, or economic crash to thunder through our lives? In The Future of Everything, David Orrell looks back at the history of forecasting, from the time of the oracle at Delphi to the rise of astrology to the advent of the TV weather report, showing us how scientists (and some charlatans) predicted the future. How can today’s scientists claim to anticipate future weather events when even thee-day forecasts prove a serious challenge? How can we predict and control epidemics? Can we accurately foresee our financial future? Or will we only find out about tomorrow when tomorrow arrives?

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the future or have an accurate model. THE BUTTERFLY EFFECT This is not to say that it is impossible to make any kind of sense out of the global climate system, or to make any predictions. To take an example from a different context, suppose that a dietician monitors a child who has taken to eating a number of candy bars a day. It would be impossible to compute or predict the exact effect: in some children the candy will speed their metabolism so they burn off the energy (negative feedback),

we prove whether avian flu is the revenge of chickens on the human race? As Garrett says, “The bottom line for policymakers: Science does not know the answer.”91 It is hard to get the balance right, and even our own immune system overreacts sometimes. All we can do is watch for coming storms. WE DON’T KNOW It might seem in this chapter that we have fallen into the trap of assuming that the future will resemble the past: just because we cannot predict atmospheric, biological, or economic systems

information. . . . Therefore we put into World3 the kinds of information one uses to understand the generic behaviour modes of thrown balls, not the kinds of information one would need to describe the exact trajectory of one particular throw of one specific ball.”45 But the world is not an inert ball, and there is no generic response. Perhaps that is why, like Cassandra, such models so often fail to convince or to move. Lack of predictability is a deep property of life. Any organism that is too

a dynamic balance between these forces: subjective greed or panic versus “objective” calculations of value. The behaviour gets interesting when the market participants are allowed to influence one another: if a majority thinks that the market is going to tank, then this mood eventually deflates the most optimistic chart-follower. Predictions therefore affect the future in a self-reinforcing, positive feedback loop. Tastes and preferences are treated as dynamic rather than fixed. The result is an

them sensitive to even small errors in parameterization. As a result, the models are highly flexible and can be made to match past data, but accurate predictions of the future remain elusive. The models are often most useful as tools for understanding the present function of the underlying systems. The three areas of scientific forecasting—weather, health, and wealth—are like siblings. They have the same origins, grew up together, and hung out with some of the same people. Each has its own

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