So, against all the odds, you have made it. You successfully got through a whole week of 2017 in the face of all those dire warnings that it will be a year of uncertainty.
Everyone – from central bankers, think tanks to politicians – has been wielding the U-word as if it is a stunning insight into the human condition. And perhaps it is for those people whose working lives are spent in the proverbial ivory towers.
But for the rest of us, the deluge of pronouncements about impeding “uncertainty” is almost insultingly vapid.
From getting a seat on the bus to work to still being in work by the end of the day, most of us have never had much certainty in our lives. We must take our chances, hoping for the best but expecting the worst.
Simply accepting the reality of uncertainty is a major advance, however, as it encourages us to seek guidance as we make our way through the fog.
In the search for such guidance, recent events such as the election of Republican Donald Trump to the US presidency suggest that we should be wary about relying on experts.
In a televised debate on Brexit last year, British politician Michael Gove achieved notoriety by declaring that people “have had enough of experts”.
Of course, that is a ludicrous statement: there is no evidence that people are, for example, declining to be treated by qualified surgeons or refusing to fly with licensed pilots.
But Mr Gove actually made a far more specific assertion. He claimed that people have had enough of experts from organisations who make prognostications on major issues – but get them wrong.
Although one can question whether people really are having such doubts, there is plenty of evidence that we should.
Systematic studies of the reliability of experts in political science and economics raise serious questions about the value of taking their prognostications seriously.
In the 1980s, Philip Tetlock, a psychologist at the University of California, interviewed hundreds of such experts and asked them to forecast what the future might hold.
Then he did something unprecedented: he compared their forecasts to reality.
The results, published in 2005, were – and are – sobering. The experts proved no more reliable guides to the future than informed non-experts.
Indeed, when their forecasts about events were classified into three categories – better, worse or unchanged – the experts scored less well than simple guesswork.
Analysing the findings, Prof Tetlock uncovered some telling insights about experts and why they do so badly.
First, they are often poor at telling when they are out of their depth – that is, they are “poorly calibrated”. Asked to put a reliability figure on their forecasts, they will be wildly overconfident.
And doom-mongers are especially prone to this: Prof Tetlock found that they gave 70 per cent probabilities to gloomy outcomes that came to pass just 12 per cent of the time.
But most unreliable of all are experts with a clear view of how the world works. As Prof Tetlock pointed out, these “gurus” are especially popular with the media, as they deliver clear, simple forecasts with compelling confidence. Ironically, the study revealed that their confidence in their own predictions is typically rivalled only by their unreliability.
The past six months have served to underscore Prof Tetlock’s insights. Political forecasters are still smarting from their back-to-back fails over the outcome of the Brexit referendum and the US presidential election.
It is too soon to tell whether the dire consequences of Brexit predicted for Britain will go the way of most such pessimistic forecasts. It is, however, pretty clear that the dozen or so economists who told Bloomberg last June that Britain would slide into recession by last year will end up confirming Prof Tetlock’s findings.
So if we cannot rely on experts to tell us what will happen during these times of uncertainty, what can we do?
Our best bet lies in taking some lessons from the centuries-old scientific study of uncertainty, better known as the theory of probability.
Originally developed more than 300 years ago to help gamblers to make better decisions, it is often thought of as dealing with ho-hum problems about, say, the chances of drawing a blue ball from a jar of 50 mixed colours.
But its basic principles have far wider applicability. And one of its most important lessons is that randomness can easily fool us into believing that we are witnessing something genuine. Chance events cluster together far more often than we think. For example, imagine a financial market that typically experiences a dozen corrections a year.
One might therefore expect to see an average of one correction a month.
But probability theory shows that so smooth a spread will typically occur once every 19,000 years. For the rest of the time, we will experience years with several corrections per month – all tempting us into making changes to our plans.
Probability theory also warns us about seeing significance in dramatic events – especially those that make headlines.
From coin tosses to the economic performance of nations, we live in a world affected by multiple chance effects.
Most of the time, these influences pull this way and that, leaving things close to average. But every so often, they conspire to propel events far from their normal range. The result seems to demand action, often at huge expense.
Pausing before acting will often cure the “problem” for free, as the chance effects revert back to normal in a probabilistic phenomenon known as regression to the mean.
Perhaps the single most powerful method probability offers for dealing with uncertainty is diversification.
Lotteries are hard to win because many random numbers have to come up simultaneously.
Flipping that logic around shows that diversifying across several, minimally related strategies cuts the risk of losing everything in one go.
And it works: there is now substantial evidence that spreading financial assets across as many different sectors and geographical areas as possible is the surest way of surviving market crashes.
There is no doubt that we live in a time of uncertainty – because that has always been the case. But the time has come to give up our belief in fortune tellers and seek guidance instead from the rational rules of chance.
Robert Matthews is visiting professor of science at Aston University in Britain. His latest book, Chancing It: The Laws of Chance and What They Mean For You, is published this month.