THIS YEAR, NEXT YEAR, SOMETIME, NEVER? Geoff Royston
1720, is quoted as saying that “I can calculate the motions of the heavenly bodies, but not the madness of people”) feature important non-linearities, e.g. from feedback effects, where initially imperceptible fluctuations can quickly amplify over time and introduce ever-growing errors into forecasts; the so-called butterfly effect. However precise our current knowledge of such complex dynamic systems, these compounding errors will eventually swamp our ability to predict their future.
THE TRUMPET OF UNCERTAINTY
The kwik brown foks jumpd ovr the lazee dog. That could be correct spelling - if the forecasts made at the dawn of the twentieth century by the engineer John Elfreth Watkins had been right. In 1900 he made a number of predictions, including that by the 2000s, English language will be condensed, with “no C, X, or Q in our everyday alphabet.” We will come back later to foxes and dogs (or rather, hedgehogs), and indeed to John Watkins’ forecasts. Meantime, let’s look at problems of prediction.
This problem can be viewed as the trumpet of uncertainty, see Figure 1, where uncertainty is visualised as proportional to the product of time and complexity. The trumpet is narrow for the highly predictable (e.g. clocks, planetary motions, tomorrow’s weather), wide for the highly unpredictable (e.g. the weather in a month’s time, stock market fluctuations, revolutions) and in between for things in the middle (e.g. UK birth rate ten years from now, the rise and fall of an epidemic, the extent of climate change).
There is no shortage of forecasts that have been spectacularly wrong. Take for example, the world of information technology. In 1943 the president of IBM predicted a world market "for maybe five computers"; in 1977 the head of the DEC (Digital Equipment Corporation) said “there is no reason why anyone would want a computer in their home”; and in 2007 the CEO of Microsoft said “there’s no chance that the iPhone is going to get any significant market share”. So, is forecasting just a load of crystal balls? Before looking into some empirical evidence, let’s consider first why prediction is difficult. Apart from the obvious factor of ignorance – lack of knowledge or understanding of (or disregard for) crucial elements of a situation - two key factors are time and complexity. Our ability to see into the future is generally less the further ahead we are trying to look and the greater the complexity of the system we are considering. Some real-world systems obey simple laws and are highly predictable – for example Edmund Halley in 1705 accurately foretold the return 53 years later of the comet that accordingly now bears his name. Many others however (not least those where human behaviour is involved – Newton, who lost a fortune in the South Sea Bubble of
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IMPACT © THE OR SOCIETY
© Geoff Royston
CRYSTAL BALLS?
FIGURE 1 THE TRUMPET OF UNCERTAINTY
Can we learn to blow the trumpet of uncertainty? That could be the leitmotif for a widely praised book; Superforecasting: The Art and Science of Prediction, written by Philip Tetlock with co-author Dan Gardner.
FOXES AND HEDGEHOGS
Tetlock wanted to test the accuracy of experts’ predictions. So he asked several hundred of them to make forecasts in areas like elections, the economy and so on and then looked (over a period of 20 years!) at the results. The experts on average scored about as well as would a dart throwing chimpanzee!
