My track record as a prophet isn’t too bad: I thought the SNP would lose and that the Brexiteers would win and anticipated that Trump might just squeak it over Clinton in that epochal unpopularity contest all those (four) years ago. But I hadn’t examined those predictions closely; I just went with my gut.

Meanwhile, I’d been delighting my family with predictions of a pandemic for years. So when one finally arrived — and I was going to be spending a lot of time indoors — I figured it was as good a time as any to test the limits of my prescience.

At first I was quite successful. The initial mass freak out I saw coming a mile off, and was able to stay ahead of the various stages of the panic buying; and I was only 24 hours out when it came to predicting the day a stay-in-place order would be imposed in my part of Texas. I was confident that people wouldn’t be able to put up with much more than three months of lockdown — and I was correct — but I thought it would be anxiety about a looming economic disaster that would drive people to rebel. Instead it was the killing of a black man by a cop in Minnesota, captured on video for the whole world to see. What has happened since I didn’t foresee at all.

Anticipating what comes next is no idle game and sure enough, on social media, in the media, the prophecies and predictions flourish. But what are they based on? How do we know we are considering the right information? What information should we consider?

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This, of course, is a problem that has plagued humanity since the dawn of time. For our ancestors the anxiety was considerably more pronounced: they lived precariously in environments that were regularly ravaged by warfare, plague, famine and were subject to arbitrary (in)justice. In response, they developed a bewildering array of techniques for divining the future.

The I-ching, one of the oldest works in Chinese literature, is a divination text. The haruspices in Etruria saw the future in the entrail of dead animals; the Babylonians found it in the stars. The Greeks had their oracles, the Romans their augurs and both looked to the sky to see in which direction the birds were flying (though they disagreed on the significance of direction). In Rome you could even find gainful employment as a pullarius or keeper of the scared chickens and advise generals on whether to go to war or not.

And then, of course, there were dreams. In the 20th century Freud persuaded us that these nocturnal visions revealed what lay within us, but the ancients believed that they revealed what lay ahead of us. Greek literature is full of prophetic dreams, and the Bible has its celebrated oneirocritics also. In the Old Testament, when Joseph interpreted pharaoh’s dream he was rewarded with an appointment at the royal court; when Daniel interpreted the dream of Nebuchadnezzar, he produced a vision that apocalyptic Christians are still decoding to this fay.

Read in isolation a couple of millennia later, these stories read like eruptions of the supernatural or divine, rich in mystery. But dream interpretation was not only widespread in the ancient world; it was a job, and there were manuals that taught you how to do it. Today, however, only one of these survives: The Interpretation of Dreams by Artemidorus, recently published by Oxford World Classics in a new translation by Martin Hammond.

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Artemidorus, a professional dream interpreter based in Ephesus (now Efes in Turkey) wrote his dream manual in five books over a thirty-year period between AD 180 and 210. As the time required to finish the thing indicates, he wasn’t content to pass along some old wives’ tales he had heard at this mother’s knee; interpreting the future was a responsibility he took very seriously and he made a serious attempt to systematise dream interpretation, traveling widely throughout Asia minor, the Aegean islands, Greece and Italy to collect his material.

In the end he included more than 1,400 “dream elements” in the finished work, to which he applied a critical eye. He was careful to distinguish between dreams that simply reflected the mind turning over the day’s events and those which he identified as “prophetic”, which he divided into two categories — those which were directly predictive and required no interpretation and allegorical dreams, which did. He also stressed the importance of setting dreams in context and finding out as much as possible about the dreamer’s situation, then applied numerology, etymological analyses and other techniques to decoding dream imagery.

The results are fascinating, less for what they reveal about ancient futures long since passed but rather for what they reveal about our ancestor’s dreams and, by extension, the world they inhabited. Back then Greek gods were constant visitors to slumber, while people also dreamt a lot about sundials, stoning, whetstones, cloaks and gladiators considerably more than they do nowadays. Artemidorus’ job was to understand what, say, that gladiator represented (answer: a lawsuit would feature in your destiny).

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But there was also lots of dream material that Artemidorus collected which people still dream about today: family members, employers, journeys, animals, drunkenness, death and, of course, sex. Artemidorus’ writings on sex tickled Freud and Foucault no end, but don’t let that put you off. Indeed, this part of the book is so wild it is difficult to include too much of it on a respectable publication such as UnHerd.

Artemidorus is not very interested in female sexuality it must be said, but he does include multiple analyses of men having sex with their mothers in various permutations of intercourse, and without any of Freud’s pretentious references to Greek tragedy. The practical, hard-headed Artemidorus rather insists that this flavor of oneiric coitus has a “multifaceted complexity” allowing it degree of analysis which has “escaped many of the dream interpreters”, which he then proceeds to expound upon in graphic yet clinical detail.

For instance, says Artemidorus, not only is it important to consider the sexual position when foretelling the future, but also whether or not your mother is alive in the dream. A face-to-face/alive combo is bad, indicating that you will fall out with father; however, if your father is in poor health then the dream indicates he will die, as it shows you “presiding” over your mother. Dreams of sex with your mother while she is dead are worse, needless to say: they indicate that you, the dreamer, will die.

As Artemidorus’ helpfully points out, the earth is your “mother” and the dream shows that you are returning to her. Oral sex was highly frowned upon in Artemidorus’ day and so Mother-son fellatio is still worse: it foretells death for your children, the loss of all your property, or a serious illness. Lest anyone doubts the seriousness of the threat, he supplies sceptical readers with the evidence: he knew of a man who was castrated after having this dream.

Far better to dream of sleeping on a dunghill: for the poor man this indicates a future in which he will acquire property, while the rich man can look forward to being appointed to high office (all members of the public make a contribution to the dunghill, you see). Being shat upon by a rich man, is a good thing as it means that, like in the dream, you will receive some of his… uh… stuff. Move over, Alain de Botton: that’s some heavy status anxiety right there.

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But what can Artemidorus possibly tell us today, in this era of pandemic and other panics? It’s unlikely many will look to him to foretell our futures, though you can learn a lot about life in second century Asia Minor: dreams being visual, he inadvertently opens a window on a colourful past filled with details found in no other ancient authors.

That’s pretty good; I think it definitely makes him worth reading. As for me, however, I am fascinated by the underlying structure of Artemidorus’ thinking, and his approach to dream interpretation. Like most people, he only went so far in investigating his assumptions. He accepted the belief that dreams contained messages about the future; after all he was carrying on a tradition that was more than half a millennium old and which carried the weight both of custom and authority. Custom may be less defensible to us today, but authority still draws a lot of water in twenty-first century town.

From that point, however, he gathered as much data as he could and tried to be as systematic as possible and so starts to seem quasi-modern. He studied the works of past masters and sought real life examples to “prove” his points, and and over the decades assembled a complex matrix for understanding the future.

This was predictive analytics, second century AD style. Yes, it was nonsense, but if someone back then wanted to assuage their anxiety about the future, what other options were there? Chickens? Entrails? The stars? You could try not believing in any of it (as Cicero had recommended two and a half centuries earlier) but humans are not great at that and it hardly solves the problem of the future: our minds, like nature, abhor a vacuum. Thus our ancestors cast around and assembled predictions from what they could lay their hands on: dreams, rich in symbols and plentiful as they are, were clearly one of the better options.

That anxiety is something deeply human. And I’m not sure that on a fundamental level we’ve changed much, if at all. Most of us, presented with the future, cast around for what we can find — a historical precedent there, a hunch here, something we read in a book there — and produce predictions at a rapid clip, only to forget them soon afterwards; pundits spit them out on a regular basis and are forgiven when none of it comes to pass. The occasional superforecaster aside, getting it right doesn’t even seem to matter that much;  our visions are fuelled by anxiety, or hope, more than cool reasoning. Even our complex models are obviously incomplete, full of holes, and frequently turn out to be wrong — yet still we act on them because, well, what is the alternative?

The future is coming for us, regardless: the pandemic, and upheaval, have a ways to go yet. As for me, I continue to test my skills as a prophet, though I grow less cocky by the day. My beard, at least, is coming along nicely. It’s not quite at prophet length yet, unless you count Karl Marx (he tried, anyway.) I do feel confident making one prediction, however: it will be at Elijah-on-an-Orthodox icon levels before this is all over.

This is what Twitter tells me every day. But as I pointed out the other week, while Johnson is routinely portrayed as a textbook case of failure, the key groups of voters that will decide his fate hold a very different view. Since becoming Prime Minister, Boris Johnson’s ‘net favourability’ score among all voters has increased by five points. Among Leavers it is up by 16-points. Among Conservatives it is up by 21-points.

Twitter tells me @BorisJohnson has been a disaster

The data tells me that since entering Number 10 his ratings are:

-UP among all voters by 5 points

-UP among Leavers by 16 points

–UP among Conservatives by 21 points

Always. Challenge. Groupthink.https://t.co/glkO9RkCLb

— Matt Goodwin (@GoodwinMJ) September 17, 2019

Notwithstanding the general hysteria and catastrophism that has followed every twist and turn of the Johnson premiership, YouGov notes that “the ongoing Brexit chaos hasn’t adversely affected the PM’s favourability figures — which are, in fact, slightly higher now than they were when he first moved into Downing Street”.

In fact, despite the furore over Johnson’s suspension of parliament, the dismissal of special advisors, the expulsion of 21 moderate Conservative MPs and the PM’s allegedly dictatorial tendencies, support for the Conservative Party has increased.

The week before Johnson became Prime Minister, his party averaged 24% The week after he entered Downing Street it jumped to 29%. Today, it is 33%. Groupthink tells us that Johnson is making his party unelectable. The data tells us that Labour has only been ahead in one of the last 40 opinion polls.

There is, of course, a lot that could still go wrong for Johnson. With the SNP still dominant in Scotland, Britain’s electoral map is more constrained. Volatility is high and so it is much harder for either of the two main parties to win a majority.

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But if Johnson’s strategy is to mobilise Brexit Britain while exploiting a divided Remain camp, then, so far, it seems to be working. All he needs to do now is capture between one-third and a half of what remains of Nigel Farage’s vote. If he does that,  then he will almost certainly cruise to a comfortable majority and his stay in No 10 will be longer than many of his critics think.

But all of this speaks to a deeper point. It underlines why — with a fresh election on the horizon — we should be far more critical of herd thinking than we currently are.

Think for a moment about all of the shocks that have rocked British politics. The triumph of UKIP at the European elections in 2014. David Cameron’s surprise majority in 2015. The rise of the SNP in Scotland. Jeremy Corbyn’s election as Labour Party leader. The Leave victory in 2016. The Trump victory a few months later. The hung parliament in 2017. And the strong result for Corbyn’s Labour.

Most of the political Twitterati herd did not see these shocks coming. The experts didn’t help much either.

In 2015, hundreds of academics, journalists and pollsters were asked in a survey to predict the outcome of the general election. This was based on the popular idea that there is “wisdom in crowds”. These crowds predicted a hung parliament in which Ed Miliband and the Labour Party commanded the largest number of seats while the Liberal Democrats and SNP held the balance of power. The reality was the first Conservative majority since 1992.

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More complex election forecasts didn’t offer us much insight either. In fact, few, if any, successfully forecast the result. The most generous prediction gave the Conservatives 296 seats while the polling ‘guru’ Nate Silver was also way off, predicting 278. In the end, Cameron won 330 seats. Political scientists were left to reflect on what one described as their “substantial over-confidence in predictions of the election outcome”.

More recently, experts would tweet out Jeremy Corbyn’s dismal leadership ratings like clockwork every month: “Look how terrible he is!” But then Labour presided over the highest share of the vote since Tony Blair’s second landslide. The experts went quiet. As one academic rightly argued, the response to Corbyn reflected a wider problem: a tendency among experts to prioritise not objective analysis but their own normative positions and a desire to conform to the established ideological orthodoxy.

Much the same could be said about Brexit.

When the referendum arrived in 2016, another survey of nearly 600 experts delivered a remarkably clear view. Nearly 90% expected Remain to win. Just 5% felt that Brexit was the most likely outcome. Journalists were noticeably worse: 97% predicted Remain and just 3% Brexit. And most expected Remain to win by at least 10-points. There were simply too few people willing to question and challenge the herd thinking.

And then came the 2017 general election. This time around “[t]he prevailing view [of another expert survey]…was that the Conservatives will secure a large majority, with an implied probability of 65% that they will win a majority of more than one hundred seats”.

Once again this view was bolstered by even more technical election forecasts, all but one of which confidently predicted a solid Conservative majority (take a look for yourself). The largest majority of 124 seats was forecast by Ian Warren, an expert on the Labour vote.

It is easy to bash election predictions and take this argument too far, of course. We should remember that the forecasters had a better election in 2010 (although Nate Silver was still wide of the mark as he was, again, in 2016). And while, last week, Ipsos-MORI revealed that only 23% of people think that pollsters are trustworthy, the polls have actually got more right than people think.

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The problem is that some of the things that they have called correctly — some online studies showed a swing to Leave, there were some state-level polls in the US that pointed to a Trump victory, or the MRP model that predicted a hung parliament in 2017 — were simply never accepted by the herd. Too many people only saw what they wanted to see.

Like the much-derided economists who failed to see the financial crisis coming, it seems only fair to ask why so many political analysts have failed to diagnose a succession of shocks. Often, it has been those who are closest to the world of politics who have been consistently outflanked by political change.

I suspect that several factors are at play.

The sharp liberal Left slant that runs through the Twitter and university communities ensures that few people have strong links to the values and voters that run against the dominant ideological orthodoxy. Ideological cocoons prevent experts from seeing, and engaging with, the wider world.

Rather than challenge our priors, it is much easier to let motivated reasoning — a bias toward an outcome or decision that confirms with what we already know — win the day. As a result, anything or anybody that deviates from the consensus and the herd is challenged, undermined or simply ignored.

Furthermore, and as Philip Tetlock, author of Superforecasting argues, experts tend to suffer from a potent interplay of over-confidence, binary thinking, a herding instinct and, to be blunt, hubris. All of this undermines their ability to gauge the public mood in an increasingly volatile political environment. This seems especially likely when most of the movements that are challenging the status quo are anchored in communities that most highly-educated and privileged experts simply do not understand.

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To this we can add the dominance of the polls in shaping much of our media commentary, which is deeply problematic. Inaccurate polls feed inaccurate forecasts while many journalists, understandably, lack the time and skills to question the methodological choices being pursued. Equally, it’s easy to disregard those polls that simply don’t fit your requirements.

All of this underlines why — with a fresh election on the horizon — we all need to be far more sceptical of what we are told and more willing to challenge the consensus.

Tetlock suggested that the art and science of prediction is a skill that can be improved with practice. The jury is still out on that one. For the rest of us, one might suggest that another lesson can be taken from recent history: always challenge the herd.

Anyway, that’s the theory, about which I have my doubts – for reasons I’ve explained before.

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However, the general sense in which ‘singularity’ is used here is a useful one. Sometimes a technology reaches a tipping point not only in its own development, but in the development of everything around it – i.e. it doesn’t just ‘go mainstream’ it redefines the mainstream.

On LinkedIn, Michael Liebreich envisages what he calls the ‘solar singularity’ – a credible scenario in which solar photovoltaic technology doesn’t just find its place in the ‘energy mix’, but basically takes over – rewriting the rules not just of energy economics, but of all economic activity that depends on energy (i.e. ultimately everything):

“Most ‘sensible’ forecasts suggest solar meeting 10% (IEA NPS) to 25% (BNEF NEO) of current forecast global power demand by 2050. But it is entirely possible to imagine scenarios in which sustained growth delivers multiples of that, with cheap solar driving accelerated electrification.”

What makes him think that solar could so spectacularly exceed the sensible forecasts?

For a start, there’s solar’s performance to date – “since the year 2000, solar PV has delivered eight ‘doublings’ [in capacity]”. Granted, that’s from a low base, but each doubling has produced big advances in affordability. Solar’s ‘experience curve’ – i.e. the reduction in costs with every doubling of capacity – is a “fearsome 24-28%”.

Liebreich argues that we will see further cost reductions:

“Assuming the capital markets continue to deliver cheap money, my bet is that we will see record unsubsidised solar power prices – somewhere in the world – hit 1c/kWh by 2030… The current world record unsubsidised solar power price (from Mexico) is 1.97 c/kWh – so prices only have to halve once more, after falling 99.8% in the past 42 years to cut the record price to 1c/kWh. Of course the median price would remain higher, say 2-3 c/kWh, down from the current 4-6 c/kWh.”

The competitive economics of solar (and wind) are already disrupting the energy sector – further improvements in affordability will mean that the expansion of renewables will be driven by cost alone, irrespective of environmental policy. Indeed, it’s new coal and nuclear capacity that needs the subsidies now.

As for the intermittency of the wind and sunshine, remember that all forms of power generation need backing up. Furthermore, the increasingly competitive construction costs (and zero fuel costs) of renewables are driving investment not only in generation but also storage. (And mass-manufactured, modular storage technologies like batteries are subject to much the same downward pressures on cost as are solar panels.)

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With the roll-out of any game-changing technology there are uncertainties. But compared to the huge, one-off, time-consuming investments involved in the building of a new nuclear power station or coal plant, the deployment of new renewable capacity is modular, flexible and has a short turnaround time – meaning that the risks, from a commercial point of view, are manageable.

Therefore, expect to see the disciplines of the market-place drive investment into the ongoing expansion and improvement of clean energy, not just the promises of policy-makers.

For all of these reasons and others, I think that Michael Liebriech is right to explore the possibility of a solar singularity in which the global economy reorganises around the availability of the least polluting and most affordable sources of energy:

“You would… need to see energy-intensive industries shift around the world to take advantage of cheap solar power. Imagine the demand for solar power if most heavy manufacturing were to move to the sunbelt.”

For instance, Spain, with its vast, sunbaked and largely uninhabited interior (and geographical proximity to Western European markets) would be well-placed to benefit from a solar-driven manufacturing revolution. A shift in Europe’s industrial heartland from Germany to the Mediterranean would upset a lot of apfel carts – but when the economics of energy undergo a fundamental change, many other things are bound to change with it.

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Predictions are hard, especially about the future, as Niels Bohr may have said. But that doesn’t stop people making them. May’s deal will get through, say some. She doesn’t have the votes, say others. Of course, if we do crash out, then the crystal ball gets even murkier. We might have to stockpile medicines. Energy prices might skyrocket. We might run out of Mars Bars.

Niels Bohr was right. Predictions are hard, and people are spectacularly bad at making them. But there are, also, some simple things you can do to get better at making them, in order to mitigate the consequences of whatever the future holds. I went on a workshop last week, with a bunch of senior and important people from various major banks and intergovernmental agencies, to learn how to do it better.

It was run by an organisation called the Good Judgment Project. The GJP was set up by Philip Tetlock who, in 1984, had noticed something. The young psychologist had joined an expert committee set up by the National Academy of Sciences to help prevent nuclear war. The experts were divided. Conservatives wanted to maintain a tough line against the Soviet Union; liberals thought that line was strengthening Kremlin hardliners.

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When, a few months later, Mikhail Gorbachev took office and started opening the USSR up, Tetlock was surprised to see that everyone thought this showed they’d been right all along. The conservatives thought the tough line had pushed the Kremlin into action; the liberals thought it would have happened anyway, and that the hard line had just slowed things down. People all thought they were right, whatever had actually happened.

So Tetlock set up an experiment to see how good experts were at predicting the future. First, he had to tie them to falsifiable predictions. Pundits had, and have, a tendency to vague answers that don’t really pin them to anything: “Food shortages could be likely”, and so on. Tetlock gave them specific questions with clear dates: “Will the yen be higher than it is now against the deutschmark in one month’s time?” He then asked the pundits to give percentage values for how likely that was: there is a 75% chance that this will happen, a 60% chance, etc.

He had nearly 300 pundits make an average of 100 predictions each, and then saw how well they did over the coming months. If your 60% predictions came in 60% of the time, and your 35% predictions came in 35% of the time, you were “well calibrated”. You were also given points for being confident and right – 90% guesses got a higher score than 50% ones – but punished for being confident and wrong.

What he found was that pundits’ calibration, on average, was no better than random guessing – or, as Tetlock described it, “than a dart-throwing chimpanzee”. But some were significantly better. What predicted who was better was not whether they were conservative or liberal, or even, particularly, their expertise in the field, but how they approached the problem. People who assumed the world was simple and had simple solutions did badly; people who thought it was complex, who realised they could be wrong, and who learnt from mistakes did better. It also helped if you were good with numbers, and good at spotting patterns, as in IQ tests.

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Tetlock would later set up the GJP, which runs tests to find the best forecasters and enters them in forecasting competitions against teams from intelligence agencies and universities. Teams of these “superforecasters” outperformed the best of the rest by a huge margin, even CIA analysts and academic experts.

The workshop that I attended couldn’t make everyone superforecasters. There is some inherent talent involved in being the best. But the one of the supers who ran it pointed out that not everyone can ever play at Carnegie Hall, but almost everyone can learn to play the violin. Practising the right skills can make anyone better.

This is a useful thing to do. Being a better forecaster, if you’re a major financial institution, lets you make better bets with your money, and so turn it into more money; if you’re a national security agency or an intergovernmental organisation, it lets you direct resources towards things that are likely to happen, and keep the world safer.

Some of the things you could do were obvious: invoking the “wisdom of the crowd”, for instance. The “ask the audience” option on Who Wants to Be a Millionaire comfortably outperforms the Phone A Friend option; more generally, if you ask 1,000 people to guess something, any one of them will probably be wrong, but on average they’ll all be wrong in different directions, so the average guess is usually close. And if you’re in a big group, you should take the median average, and then readjust your predictions, and take the average again. Doing so gives you a real, measurable improvement in forecasting.

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Another is to use the sort of precise, numerical terms we talked about above. The superforecasters were highly critical of things like Sadiq Khan’s claim that no-deal Brexit is “more likely than ever” or Sir Vince Cable’s that the same was “highly improbable”. These don’t really tie you to anything: more likely than ever? How likely was it before? How likely is “highly improbable”? Is it a 5% chance? 10? Instead, write down a real percentage, and then revisit it afterwards, and see how it did. Otherwise you’ll find a way to convince yourself you were right all along, and you’ll never improve.

A third is to use the “outside view” as well as the “inside view”. For instance, say you’re at a wedding, and someone asks you how likely it is that the couple’s marriage will last until one of them dies. You look at them, they seem very happy, and you know them to be well-matched, so you say “90%”. That’s the “inside view”: looking at the specifics of the situation in front of you.

But taking the “outside view” involves looking at the whole class of similar events. So you might first think “About 40% of British marriages end in divorce,” and use the inside view to adjust your estimate from there, by taking into account other details that might change it from the base rate – social class, educational level, how long they stared into each other’s eyes when making their vows.

Fourth, you might break down the problem into smaller parts. How likely is it that we’ll have a Labour-led government before June next year? Well, the Government feels like it’s teetering on the brink at the moment, so you might think it’s pretty likely, so you might say 60%.

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But if you break it down, Parliament would have to vote for a new election, which would probably mean at least some Tories voting for an election they could well lose, and then it would mean Labour forming the largest party. If you think how likely each of those things are individually – I’d say an election before June next year is at best 50% likely, and that Labour’s odds of winning aren’t much better than 50% either – and then multiply them out, you get 25%. That’s called a “Fermi estimate”, and again, using this has been shown to improve accuracy.

There are a few other things that help – I won’t list them all, but one is, if you have a group, to try to make sure that it’s got diverse viewpoints, so it’s not just teachers or financial analysts (or just liberals or just conservatives), but a mix of many types. You also ought to make sure that the loudest or highest-status person in the group doesn’t dominate, so that the “wisdom of the crowd” doesn’t get drowned out.

We weren’t a bunch of superforecasters. We were just ordinary people, albeit some of us (not me) from some quite important financial, academic and governmental organisations. But we had a stab at it, and put best-guess estimates on some things, and then shared them and readjusted them as we were supposed to. And, after doing all that, in answer to the question “Will a Brexit withdrawal agreement obtain the consent of the European Parliament by 1 April 2019?” – a specific and falsifiable version of the question “Will Britain agree a Brexit deal?” – we figured that there was a 60% chance.

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That seems scary enough to me – that there’s a 40% chance of either a no-deal collapse or another referendum. And when we did it, our guess roughly matched that of the superforecasters. They wouldn’t tell us exactly what their numbers were, because it’s all privileged information, but I’m told we weren’t far off their guess.

That was over a week ago, though. Since then, the deal has been unveiled, Cabinet ministers have resigned, the ERG has unsuccessfully launched a leadership challenge, and the Democratic Unionist Party have all but ended the deal with the Tories that gives May her majority. Again, I don’t know exactly what the superforecasters’ numbers are, but that 60%-ish figure that we came up with now looks deeply optimistic.

The three main options – leaving with a deal, leaving without a deal, and not leaving at all, by 1 April 2019 – are all, I understand, roughly equal in probability. The Good Judgment Project is, verifiably, the best forecasting outfit in the world, but even for them it is, pretty much, a complete tossup.

So, yes. Predictions are hard, especially about the future. You can find ways of making it easier. But it looks like when it comes to Brexit, we really are all in the dark.