'Multiculturalism is a miracle of stupidity': Jordan Peterson

 

The Difference between Bullshit and Lying

We have suffered both. Some never speak the truth because they don’t know or care about it. Others know the truth but lie anyway wrires Aditya Chakrabortty in The Guardian

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llustration: Ben Jennings/The Guardian 

 
Sometimes it falls to an old book to tell us what’s new, to a white-bearded philosopher based far from Westminster or Washington to clarify the shifts in our sharp-suited politics. So spare yourself the annual round-ups in the newspapers or the boy-scout enthusiasm of podcasters. To understand the great political shift of this year, the work you need is a piece of philosophy called ­– what else? – On Bullshit.

I offer it to you this Christmas because surely no reader of mine can resist an essay that begins: “One of the most salient features of our culture is that there is so much bullshit. Everyone knows this.” Statements like that made it a bestseller upon re-publication in 2005 and turned its then-75-year-old author, Harry Frankfurt, from a distinguished moral philosopher at Yale and Princeton into a chatshow guest.

But to open the book now is to get a blast of something quite different, in a climate that just didn’t exist two decades ago. Read today, On Bullshit taxonomises an entire style of government. It foretells the age of Donald Trump and Boris Johnson.

The task Frankfurt sets himself is to define bullshit. What it is not, he argues, is lying. Both misrepresent the truth, but with entirely different intentions. The liar is “someone who deliberately promulgates a falsehood”. He or she knows the truth or could lay hands on it – but they certainly aren’t giving it to you. The bullshitter, on the other hand, “does not care whether the things he says describe reality correctly. He just picks them out, or makes them up, to suit his purpose.” Bullshitters couldn’t give two hoots about the truth. They just want a story.

In that distinction lies an explanation for this era of politics. Commentators have struggled for years to coin the phrase for now. “Populist” doesn’t work. Too often, it merely denotes what the author and their friends dislike, throwing together clowns such as Beppe Grillo with social democrats such as Jeremy Corbyn. A similar problem bedevils “strongman”, a label stuck on Xi Jinping and Jair Bolsonaro alike. But “bullshitter” – that sums up just how different Trump and Johnson are from their predecessors.

‘Bullshit is where newspaper stories about Italians demanding smaller condoms meet plans for an airport on an island in the Thames.’ Photograph: Peter Byrne/PA

Some enterprising future editor of a dictionary of political terms will carry the word “bullshit” and cite as examples: writing two opposing columns on Brexit, claiming the NHS will be £350m a week better off and affecting a hurt expression when asked the whereabouts of your promised 40 new hospitals. Come on! Those little-doggy eyes beseech the hard-faced TV interviewer. Didn’t everyone know that was bullshit?

Socially, there is little to distinguish Johnson from David Cameron: both are Bullingdon boys manufactured at Eton. In policy, too, there is a fair carryover between George Osborne’s “northern powerhouse” and Johnson’s “levelling up”, or between Cameron’s vow to get net immigration down to the tens of thousands and the pledges made by Johnson’s home secretary, Priti Patel. The great divide is in rhetoric: how Johnson talked to voters and the promises he made us. They were never meant to be taken at face value.

Among the media class’s artisanal industries of the past few years has been trying to find a thread that runs through Johnson the journalist, the globalist mayor of London and the Brexit prime minister. Frankfurt furnishes that link: it is bullshit.

Bullshit is where newspaper stories about Italians demanding smaller condoms meet plans for an airport on an island in the Thames meet promises of an “oven-ready” Brexit deal. They are electioneering fables rather than manifesto commitments, grand gestures over small print, cheerful dishonesty in place of lawyered mendacity. In other words, they are all just careerist bullshit.

Much the same goes for Liz Truss, although she was clearly not as good at it. Looking back, this summer’s Tory leadership contest can be seen as a final hurrah for the “anything goes” era. And it certainly applies to Trump. “I will build a great, great wall on our southern border, and I will have Mexico pay for that wall.” Bullshit. “Sorry losers and haters, but my IQ is one of the highest.” Bullshit. A “sea of love” at his inauguration that broke all records. Bullshit, bullshit, bullshit. Frankfurt’s book offers a theory of a generation of politicians who now appear to be leaving the stage.

‘A ‘sea of love’ at Donald Trump’s inauguration that broke all records. Bullshit.’ Photograph: Saul Loeb/EPA

Lies can be shown up: Saddam Hussein had no weapons of mass destruction. But there is no point factchecking bullshit, as parts of the British media still do over Brexit or the New York Times did with Trump. For a bullshitter, facts are beside the point – the real aim is to produce a story that erases the line between truth and falsehood. It’s why the philosopher concludes: “Bullshit is a greater enemy of truth than lies are.”

We all lie sometimes, and around millions of tables there will be much bullshit spoken over the Christmas turkey. In British politics, the era of bullshit followed on naturally from a long spell of lies. Before Johnson, the most effective Tory of the post-Thatcher era was Osborne.

He blamed Labour and Gordon Brown for the banking crash, only later admitting that was untrue. He declared Labour’s 2008 package to bail out the banks would spark a run on the pound, before confessing: “Broadly speaking, the government did what was necessary.”

Most of all, he claimed that slashing benefits was essential to bring down borrowing and was being done fairly. Remember “we are all in this together”? Except a study at the end of the coalition by the late John Hills, of the LSE, alongside other leading academics, showed that the coalition’s tax and benefit changes had “a net fiscal cost” – which meant they increased the deficit. Not only that, but “the poorest 30% [of Britons] lost or broke even on average and the top half gained”. Heading the Treasury, Osborne was in charge of a machine that could calculate the effects of his policies. He would have or should have known the truth as he laid out each budget. And yet voters were fed something entirely different.

One might see these as common or garden political lies – falsehoods that could be checked and that aimed at nothing more than establishing a poll lead for Osborne’s team. They were not the alternative reality of Vote Leave. But if the currency of truth is sufficiently debased, voters may eventually choose the altogether more entertaining humbugger. In that lies a warning for both Rishi Sunak, the down-to-earth multimillionaire, and Keir Starmer, the man who said he was Corbyn before revealing himself to be Tony Blair meets Gordon Brittas, the TV sitcom manager whose words never match results or deeds.

One topic Frankfurt doesn’t address is the audience for bullshit. Why do people buy it? To which we might add another question. Why have swathes of the political establishment and the press spent the past few years claiming Brexit is a success or that levelling up is serious or that any alternative to the most venal dishonesty is just impossible? Answers would be welcome but were we to press for any, I suspect we’d be told to drop the bullshit.

Boris Johnson is Britain's most honest politician

Bagehot in The Economist

 

Boris Johnson lies often and easily. It is the hallmark of his career. He was fired from his first job, at the Times, for fabricating a quote. As a condition of becoming editor of the Spectator he promised not to stand as an mp, and then promptly did just that. As a shadow minister, he was fired by Michael Howard for lying about an affair. (He later divorced after a few more.) While mayor of London, he said numerous times that he would not stand in the 2015 election, only to turn up as a candidate in Uxbridge. 

Lying about attending a garden party at Downing Street in May 2020, at the height of lockdown, is just the latest in a very long list. When public anger grew, mps protested with all the sincerity of Captain Renault entering a gambling den in Casablanca. Douglas Ross, a Scottish mp who voted for the prime minister in the Conservative leadership election, labelled the prime minister’s position “untenable” and demanded he quit. Why did such defenders of truth once back a man they knew to be an enthusiastic liar? Because Mr Johnson is, in his own way, a man of his word.

When he was drumming up support for his bid for party leader, his pitch was simple: back me, keep your seat, defeat Jeremy Corbyn and do Brexit. And it all came true. Mr Corbyn was crushed and the biggest Conservative majority in three decades followed. In that election Mr Johnson promised two big things and did both. The nhs would be showered with cash, which it has been. And he would do a deal with the eu, which he did.

It was not a good deal, but it was quick and it was clear. Coming after a negotiation with the eu that lacked both speed and simplicity, it is little surprise that voters jumped at it. Mr Johnson’s predecessor, Theresa May, had obfuscated, attempting legalistic contortions to avoid Brexit’s brutal simplicities. Labour’s Brexit position was, in the words of one shadow cabinet minister, “bollocks”. Mr Johnson’s deal hobbles British business for little or no gain, beyond a point of principle. But it is, no more and no less, what he said he would do.

Political lying was not invented by Mr Johnson in the Brexit campaign, comforting though that idea might be. Indeed, the misleading claims of the Leave campaign sometimes revealed awkward truths. When it pointed out that Turkey was in the long process of joining the eu, for example, Remainers cried foul because other countries were likely to block its accession. Yet David Cameron could have promised to veto Turkish membership of the eu, and did not. Turkey joining the club was a long-standing British policy.

In politics, integrity is almost inevitably followed by hypocrisy. Politicians with firm moral centres can crack. Gordon Brown was feted as a son of the manse while hurling handsets at people’s heads. Tony Blair runs an institute dedicated to openness while accepting money from despots. Sir Keir Starmer stood for Labour leader by pitching himself as Mr Corbyn in a suit, and then ditched the leftiest proposals once he had won. Mr Johnson, by contrast, does not even pretend to be a family man, despite having a few of them. He has not pretended to be anything but a power-hungry cynic either. A lack of integrity becomes a form of integrity.

A competent administrator never lurked beneath that mop of thinning hair. Occasionally, a journalist has claimed otherwise in a breathless profile; Mr Johnson has not. Those who work closely with him cannot say they were fooled into thinking he was a loyal boss. His time as prime minister has been marked by the defenestration of aides. When trouble strikes Mr Johnson, deputy heads roll. Being a civil servant rather than a political appointee offers no protection. Those who help him out, for example by chipping in for new curtains in Number 10 to keep his new wife happy, end up enmeshed in scandal.

No one can claim they were not warned about Mr Johnson. He is in no sense a mystery. He is the subject of several biographies and for the past three decades has shared his views about the world in newspaper columns and articles. If he is ever silenced by ministerial responsibility, a high-profile relative can fill the gap with more Johnson trivia. Throughout his career he has left a trail of giggling journalistic colleagues with a cherished Boris story to be whipped out on special occasions, no matter how long ago or dull. The content of his character was known and yet people still saw fit to put him in power.

If voters are souring on Mr Johnson, they only have themselves to blame. The prime minister is not a monarch. In 2019 he won 43.6% of the vote, the biggest share since Margaret Thatcher. Mr Johnson is in Downing Street because just under half the country ticked a box next to a Conservative’s name. Voters are adults. They knew what they were voting for, and they voted for what they got.

It is common to blame the rise of Mr Johnson on “Have I Got News For You”, a bbc1 news quiz on which he was a frequent guest. Ian Hislop, one of the team captains, has a tart reply: “If we ask someone on and people like them, that is up to people.” Mr Johnson is not a boil that can be lanced, at which point Britain’s body politic will recover. British politics, its systems and culture, deteriorated to the point where an honest liar proved attractive. Mr Johnson benefited from chaos created by others.

Small lies, big truths

Those mps who helped put Mr Johnson in power must now decide whether to sack him for sins he has never hidden. Their choice will be made by calculating whether their voters still want him. Popularity was all that he promised, and he delivered it—until now. If his rise is depressing, his potential fall offers a glimmer of hope. British voters have, at last, begun to grow tired of Mr Johnson’s record of honest lies. A less cynical politics may prosper and populism become unpopular. But optimism should be tempered. mps would not hesitate to keep Mr Johnson if he, in turn, helped them keep their seats. If those who put the prime minister in power bring him down, they do so to absolve themselves.

Statistics, lies and the virus: Five lessons from a pandemic

In an age of disinformation, the value of rigorous data has never been more evident writes Tim Harford in The FT 

Will this year be 1954 all over again? Forgive me, I have become obsessed with 1954, not because it offers another example of a pandemic (that was 1957) or an economic disaster (there was a mild US downturn in 1953), but for more parochial reasons. 

Nineteen fifty-four saw the appearance of two contrasting visions for the world of statistics — visions that have shaped our politics, our media and our health. This year confronts us with a similar choice. 

The first of these visions was presented in How to Lie with Statistics, a book by a US journalist named Darrell Huff. Brisk, intelligent and witty, it is a little marvel of numerical communication. 

The book received rave reviews at the time, has been praised by many statisticians over the years and is said to be the best-selling work on the subject ever published. It is also an exercise in scorn: read it and you may be disinclined to believe a number-based claim ever again. 

There are good reasons for scepticism today. David Spiegelhalter, author of last year’s The Art of Statistics, laments some of the UK government’s coronavirus graphs and testing targets as “number theatre”, with “dreadful, awful” deployment of numbers as a political performance. 

“There is great damage done to the integrity and trustworthiness of statistics when they’re under the control of the spin doctors,” Spiegelhalter says. He is right. But we geeks must be careful — because the damage can come from our own side, too. 

For Huff and his followers, the reason to learn statistics is to catch the liars at their tricks. That sceptical mindset took Huff to a very unpleasant place, as we shall see. Once the cynicism sets in, it becomes hard to imagine that statistics could ever serve a useful purpose.  

But they can — and back in 1954, the alternative perspective was embodied in the publication of an academic paper by the British epidemiologists Richard Doll and Austin Bradford Hill. They marshalled some of the first compelling evidence that smoking cigarettes dramatically increases the risk of lung cancer. 

The data they assembled persuaded both men to quit smoking and helped save tens of millions of lives by prompting others to do likewise. This was no statistical trickery, but a contribution to public health that is almost impossible to exaggerate.  

You can appreciate, I hope, my obsession with these two contrasting accounts of statistics: one as a trick, one as a tool. Doll and Hill’s painstaking approach illuminates the world and saves lives into the bargain. 

Huff’s alternative seems clever but is the easy path: seductive, addictive and corrosive. Scepticism has its place, but easily curdles into cynicism and can be weaponized into something even more poisonous than that. 

The two worldviews soon began to collide. Huff’s How to Lie with Statistics seemed to be the perfect illustration of why ordinary, honest folk shouldn’t pay too much attention to the slippery experts and their dubious data. 

Such ideas were quickly picked up by the tobacco industry, with its darkly brilliant strategy of manufacturing doubt in the face of evidence such as that provided by Doll and Hill. 

As described in books such as Merchants of Doubt by Erik Conway and Naomi Oreskes, this industry perfected the tactics of spreading uncertainty: calling for more research, emphasising doubt and the need to avoid drastic steps, highlighting disagreements between experts and funding alternative lines of inquiry. The same tactics, and sometimes even the same personnel, were later deployed to cast doubt on climate science. 

These tactics are powerful in part because they echo the ideals of science. It is a short step from the Royal Society’s motto, “nullius in verba” (take nobody’s word for it), to the corrosive nihilism of “nobody knows anything”.  

So will 2020 be another 1954? From the point of view of statistics, we seem to be standing at another fork in the road. The disinformation is still out there, as the public understanding of Covid-19 has been muddied by conspiracy theorists, trolls and government spin doctors.  

Yet the information is out there too. The value of gathering and rigorously analysing data has rarely been more evident. Faced with a complete mystery at the start of the year, statisticians, scientists and epidemiologists have been working miracles. I hope that we choose the right fork, because the pandemic has lessons to teach us about statistics — and vice versa — if we are willing to learn. 

The numbers matter 

“One lesson this pandemic has driven home to me is the unbelievable importance of the statistics,” says Spiegelhalter. Without statistical information, we haven’t a hope of grasping what it means to face a new, mysterious, invisible and rapidly spreading virus. 

Once upon a time, we would have held posies to our noses and prayed to be spared; now, while we hope for advances from medical science, we can also coolly evaluate the risks. 

Without good data, for example, we would have no idea that this infection is 10,000 times deadlier for a 90-year-old than it is for a nine-year-old — even though we are far more likely to read about the deaths of young people than the elderly, simply because those deaths are surprising. It takes a statistical perspective to make it clear who is at risk and who is not. 

Good statistics, too, can tell us about the prevalence of the virus — and identify hotspots for further activity. Huff may have viewed statistics as a vector for the dark arts of persuasion, but when it comes to understanding an epidemic, they are one of the few tools we possess. 

Don’t take the numbers for granted 

But while we can use statistics to calculate risks and highlight dangers, it is all too easy to fail to ask the question “Where do these numbers come from?” By that, I don’t mean the now-standard request to cite sources, I mean the deeper origin of the data. For all his faults, Huff did not fail to ask the question. 

 

He retells a cautionary tale that has become known as “Stamp’s Law” after the economist Josiah Stamp — warning that no matter how much a government may enjoy amassing statistics, “raise them to the nth power, take the cube root and prepare wonderful diagrams”, it was all too easy to forget that the underlying numbers would always come from a local official, “who just puts down what he damn pleases”. 

The cynicism is palpable, but there is insight here too. Statistics are not simply downloaded from an internet database or pasted from a scientific report. Ultimately, they came from somewhere: somebody counted or measured something, ideally systematically and with care. These efforts at systematic counting and measurement require money and expertise — they are not to be taken for granted. 

In my new book, How to Make the World Add Up, I introduce the idea of “statistical bedrock” — data sources such as the census and the national income accounts that are the results of painstaking data collection and analysis, often by official statisticians who get little thanks for their pains and are all too frequently the target of threats, smears or persecution. 

 

In Argentina, for example, long-serving statistician Graciela Bevacqua was ordered to “round down” inflation figures, then demoted in 2007 for producing a number that was too high. She was later fined $250,000 for false advertising — her crime being to have helped produce an independent estimate of inflation. 

In 2011, Andreas Georgiou was brought in to head Greece’s statistical agency at a time when it was regarded as being about as trustworthy as the country’s giant wooden horses. When he started producing estimates of Greece’s deficit that international observers finally found credible, he was prosecuted for his “crimes” and threatened with life imprisonment. Honest statisticians are braver — and more invaluable — than we know.  

In the UK, we don’t habitually threaten our statisticians — but we do underrate them. “The Office for National Statistics is doing enormously valuable work that frankly nobody has ever taken notice of,” says Spiegelhalter, pointing to weekly death figures as an example. “Now we deeply appreciate it.”  

Quite so. This statistical bedrock is essential, and when it is missing, we find ourselves sinking into a quagmire of confusion. 

The foundations of our statistical understanding of the world are often gathered in response to a crisis. For example, nowadays we take it for granted that there is such a thing as an “unemployment rate”, but a hundred years ago nobody could have told you how many people were searching for work. Severe recessions made the question politically pertinent, so governments began to collect the data. 

More recently, the financial crisis hit. We discovered that our data about the banking system was patchy and slow, and regulators took steps to improve it. 

So it is with the Sars-Cov-2 virus. At first, we had little more than a few data points from Wuhan, showing an alarmingly high death rate of 15 per cent — six deaths in 41 cases. Quickly, epidemiologists started sorting through the data, trying to establish how exaggerated that case fatality rate was by the fact that the confirmed cases were mostly people in intensive care. Quirks of circumstance — such as the Diamond Princess cruise ship, in which almost everyone was tested — provided more insight. 

Johns Hopkins University in the US launched a dashboard of data resources, as did the Covid Tracking Project, an initiative from the Atlantic magazine. An elusive and mysterious threat became legible through the power of this data.  

That is not to say that all is well. Nature recently reported on “a coronavirus data crisis” in the US, in which “political meddling, disorganization and years of neglect of public-health data management mean the country is flying blind”.  

Nor is the US alone. Spain simply stopped reporting certain Covid deaths in early June, making its figures unusable. And while the UK now has an impressively large capacity for viral testing, it was fatally slow to accelerate this in the critical early weeks of the pandemic. 

Ministers repeatedly deceived the public about the number of tests being carried out by using misleading definitions of what was happening. For weeks during lockdown, the government was unable to say how many people were being tested each day. 

Huge improvements have been made since then. The UK’s Office for National Statistics has been impressively flexible during the crisis, for example in organising systematic weekly testing of a representative sample of the population. This allows us to estimate the true prevalence of the virus. Several countries, particularly in east Asia, provide accessible, usable data about recent infections to allow people to avoid hotspots. 

These things do not happen by accident: they require us to invest in the infrastructure to collect and analyse the data. On the evidence of this pandemic, such investment is overdue, in the US, the UK and many other places. 

Even the experts see what they expect to see 

Jonas Olofsson, a psychologist who studies our perceptions of smell, once told me of a classic experiment in the field. Researchers gave people a whiff of scent and asked them for their reactions to it. In some cases, the experimental subjects were told: “This is the aroma of a gourmet cheese.” Others were told: “This is the smell of armpits.” 

In truth, the scent was both: an aromatic molecule present both in runny cheese and in bodily crevices. But the reactions of delight or disgust were shaped dramatically by what people expected. 

Statistics should, one would hope, deliver a more objective view of the world than an ambiguous aroma. But while solid data offers us insights we cannot gain in any other way, the numbers never speak for themselves. They, too, are shaped by our emotions, our politics and, perhaps above all, our preconceptions. 

A striking example is the decision, on March 23 this year, to introduce a lockdown in the UK. In hindsight, that was too late. 

“Locking down a week earlier would have saved thousands of lives,” says Kit Yates, author of The Maths of Life and Death — a view now shared by influential epidemiologist Neil Ferguson and by David King, chair of the “Independent Sage” group of scientists. 

The logic is straightforward enough: at the time, cases were doubling every three to four days. If a lockdown had stopped that process in its tracks a week earlier, it would have prevented two doublings and saved three-quarters of the 65,000 people who died in the first wave of the epidemic, as measured by the excess death toll. 

That might be an overestimate of the effect, since people were already voluntarily pulling back from social interactions. Yet there is little doubt that if a lockdown was to happen at all, an earlier one would have been more effective. And, says Yates, since the infection rate took just days to double before lockdown but long weeks to halve once it started, “We would have got out of lockdown so much sooner . . . Every week before lockdown cost us five to eight weeks at the back end of the lockdown.” 

Why, then, was the lockdown so late? No doubt there were political dimensions to that decision, but senior scientific advisers to the government seemed to believe that the UK still had plenty of time. On March 12, prime minister Boris Johnson was flanked by Chris Whitty, the government’s chief medical adviser, and Patrick Vallance, chief scientific adviser, in the first big set-piece press conference. Italy had just suffered its 1,000th Covid death and Vallance noted that the UK was about four weeks behind Italy on the epidemic curve. 

With hindsight, this was wrong: now that late-registered deaths have been tallied, we know that the UK passed the same landmark on lockdown day, March 23, just 11 days later.  

It seems that in early March the government did not realise how little time it had. As late as March 16, Johnson declared that infections were doubling every five to six days. 

The trouble, says Yates, is that UK data on cases and deaths suggested that things were moving much faster than that, doubling every three or four days — a huge difference. What exactly went wrong is unclear — but my bet is that it was a cheese-or-armpit problem. 

Some influential epidemiologists had produced sophisticated models suggesting that a doubling time of five to six days seemed the best estimate, based on data from the early weeks of the epidemic in China. These models seemed persuasive to the government’s scientific advisers, says Yates: “If anything, they did too good a job.” 

Yates argues that the epidemiological models that influenced the government’s thinking about doubling times were sufficiently detailed and convincing that when the patchy, ambiguous, early UK data contradicted them, it was hard to readjust. We all see what we expect to see. 

The result, in this case, was a delay to lockdown: that led to a much longer lockdown, many thousands of preventable deaths and needless extra damage to people’s livelihoods. The data is invaluable but, unless we can overcome our own cognitive filters, the data is not enough. 

The best insights come from combining statistics with personal experience 

The expert who made the biggest impression on me during this crisis was not the one with the biggest name or the biggest ego. It was Nathalie MacDermott, an infectious-disease specialist at King’s College London, who in mid-February calmly debunked the more lurid public fears about how deadly the new coronavirus was. 

Then, with equal calm, she explained to me that the virus was very likely to become a pandemic, that barring extraordinary measures we could expect it to infect more than half the world’s population, and that the true fatality rate was uncertain but seemed to be something between 0.5 and 1 per cent. In hindsight, she was broadly right about everything that mattered. MacDermott’s educated guesses pierced through the fog of complex modelling and data-poor speculation. 

I was curious as to how she did it, so I asked her. “People who have spent a lot of their time really closely studying the data sometimes struggle to pull their head out and look at what’s happening around them,” she said. “I trust data as well, but sometimes when we don’t have the data, we need to look around and interpret what’s happening.” 

MacDermott worked in Liberia in 2014 on the front line of an Ebola outbreak that killed more than 11,000 people. At the time, international organisations were sanguine about the risks, while the local authorities were in crisis. When she arrived in Liberia, the treatment centres were overwhelmed, with patients lying on the floor, bleeding freely from multiple areas and dying by the hour. 

The horrendous experience has shaped her assessment of subsequent risks: on the one hand, Sars-Cov-2 is far less deadly than Ebola; on the other, she has seen the experts move too slowly while waiting for definitive proof of a risk. 

“From my background working with Ebola, I’d rather be overprepared than underprepared because I’m in a position of denial,” she said. 

There is a broader lesson here. We can try to understand the world through statistics, which at their best provide a broad and representative overview that encompasses far more than we could personally perceive. Or we can try to understand the world up close, through individual experience. Both perspectives have their advantages and disadvantages. 

Muhammad Yunus, a microfinance pioneer and Nobel laureate, has praised the “worm’s eye view” over the “bird’s eye view”, which is a clever sound bite. But birds see a lot too. Ideally, we want both the rich detail of personal experience and the broader, low-resolution view that comes from the spreadsheet. Insight comes when we can combine the two — which is what MacDermott did. 

Everything can be polarised 

Reporting on the numbers behind the Brexit referendum, the vote on Scottish independence, several general elections and the rise of Donald Trump, there was poison in the air: many claims were made in bad faith, indifferent to the truth or even embracing the most palpable lies in an effort to divert attention from the issues. Fact-checking in an environment where people didn’t care about the facts, only whether their side was winning, was a thankless experience. 

For a while, one of the consolations of doing data-driven journalism during the pandemic was that it felt blessedly free of such political tribalism. People were eager to hear the facts after all; the truth mattered; data and expertise were seen to be helpful. The virus, after all, could not be distracted by a lie on a bus.  

That did not last. America polarised quickly, with mask-wearing becoming a badge of political identity — and more generally the Democrats seeking to underline the threat posed by the virus, with Republicans following President Trump in dismissing it as overblown.  

The prominent infectious-disease expert Anthony Fauci does not strike me as a partisan figure — but the US electorate thinks otherwise. He is trusted by 32 per cent of Republicans and 78 per cent of Democrats. 

The strangest illustration comes from the Twitter account of the Republican politician Herman Cain, which late in August tweeted: “It looks like the virus is not as deadly as the mainstream media first made it out to be.” Cain, sadly, died of Covid-19 in July — but it seems that political polarisation is a force stronger than death. 

Not every issue is politically polarised, but when something is dragged into the political arena, partisans often prioritise tribal belonging over considerations of truth. One can see this clearly, for example, in the way that highly educated Republicans and Democrats are further apart on the risks of climate change than less-educated Republicans and Democrats. 

Rather than bringing some kind of consensus, more years of education simply seem to provide people with the cognitive tools they require to reach the politically convenient conclusion. From climate change to gun control to certain vaccines, there are questions for which the answer is not a matter of evidence but a matter of group identity. 

In this context, the strategy that the tobacco industry pioneered in the 1950s is especially powerful. Emphasise uncertainty, expert disagreement and doubt and you will find a willing audience. If nobody really knows the truth, then people can believe whatever they want. 

All of which brings us back to Darrell Huff, statistical sceptic and author of How to Lie with Statistics. While his incisive criticism of statistical trickery has made him a hero to many of my fellow nerds, his career took a darker turn, with scepticism providing the mask for disinformation. 

Huff worked on a tobacco-funded sequel, How to Lie with Smoking Statistics, casting doubt on the scientific evidence that cigarettes were dangerous. (Mercifully, it was not published.)  

Huff also appeared in front of a US Senate committee that was pondering mandating health warnings on cigarette packaging. He explained to the lawmakers that there was a statistical correlation between babies and storks (which, it turns out, there is) even though the true origin of babies is rather different. The connection between smoking and cancer, he argued, was similarly tenuous.  

Huff’s statistical scepticism turned him into the ancestor of today’s contrarian trolls, spouting bullshit while claiming to be the straight-talking voice of common sense. It should be a warning to us all. There is a place in anyone’s cognitive toolkit for healthy scepticism, but that scepticism can all too easily turn into a refusal to look at any evidence at all.

This crisis has reminded us of the lure of partisanship, cynicism and manufactured doubt. But surely it has also demonstrated the power of honest statistics. Statisticians, epidemiologists and other scientists have been producing inspiring work in the footsteps of Doll and Hill. I suggest we set aside How to Lie with Statistics and pay attention. 

Carefully gathering the data we need, analysing it openly and truthfully, sharing knowledge and unlocking the puzzles that nature throws at us — this is the only chance we have to defeat the virus and, more broadly, an essential tool for understanding a complex and fascinating world.

Facts v feelings: how to stop our emotions misleading us

The pandemic has shown how a lack of solid statistics can be dangerous. But even with the firmest of evidence, we often end up ignoring the facts we don’t like. By Tim Harford in The Guardian
 

By the spring of 2020, the high stakes involved in rigorous, timely and honest statistics had suddenly become all too clear. A new coronavirus was sweeping the world. Politicians had to make their most consequential decisions in decades, and fast. Many of those decisions depended on data detective work that epidemiologists, medical statisticians and economists were scrambling to conduct. Tens of millions of lives were potentially at risk. So were billions of people’s livelihoods.

In early April, countries around the world were a couple of weeks into lockdown, global deaths passed 60,000, and it was far from clear how the story would unfold. Perhaps the deepest economic depression since the 1930s was on its way, on the back of a mushrooming death toll. Perhaps, thanks to human ingenuity or good fortune, such apocalyptic fears would fade from memory. Many scenarios seemed plausible. And that’s the problem.

An epidemiologist, John Ioannidis, wrote in mid-March that Covid-19 “might be a once-in-a-century evidence fiasco”. The data detectives are doing their best – but they’re having to work with data that’s patchy, inconsistent and woefully inadequate for making life-and-death decisions with the confidence we would like.

Details of this fiasco will, no doubt, be studied for years to come. But some things already seem clear. At the beginning of the crisis, politics seem to have impeded the free flow of honest statistics. Although the claim is contested, Taiwan complained that in late December 2019 it had given important clues about human-to-human transmission to the World Health Organization – but as late as mid-January, the WHO was reassuringly tweeting that China had found no evidence of human-to-human transmission. (Taiwan is not a member of the WHO, because China claims sovereignty over the territory and demands that it should not be treated as an independent state. It’s possible that this geopolitical obstacle led to the alleged delay.)

Did this matter? Almost certainly; with cases doubling every two or three days, we will never know what might have been different with an extra couple of weeks of warning. It’s clear that many leaders took a while to appreciate the potential gravity of the threat. President Trump, for instance, announced in late February: “It’s going to disappear. One day it’s like a miracle, it will disappear.” Four weeks later, with 1,300 Americans dead and more confirmed cases in the US than any other country, Trump was still talking hopefully about getting everybody to church at Easter.

As I write, debates are raging. Can rapid testing, isolation and contact tracing contain outbreaks indefinitely, or merely delay their spread? Should we worry more about small indoor gatherings or large outdoor ones? Does closing schools help to prevent the spread of the virus, or do more harm as children go to stay with vulnerable grandparents? How much does wearing masks help? These and many other questions can be answered only by good data about who has been infected, and when.

But in the early months of the pandemic, a vast number of infections were not being registered in official statistics, owing to a lack of tests. And the tests that were being conducted were giving a skewed picture, being focused on medical staff, critically ill patients, and – let’s face it – rich, famous people. It took several months to build a picture of how many mild or asymptomatic cases there are, and hence how deadly the virus really is. As the death toll rose exponentially in March, doubling every two days in the UK, there was no time to wait and see. Leaders put economies into an induced coma – more than 3 million Americans filed jobless claims in a single week in late March, five times the previous record. The following week was even worse: more than 6.5m claims were filed. Were the potential health consequences really catastrophic enough to justify sweeping away so many people’s incomes? It seemed so – but epidemiologists could only make their best guesses with very limited information.

It’s hard to imagine a more extraordinary illustration of how much we usually take accurate, systematically gathered numbers for granted. The statistics for a huge range of important issues that predate the coronavirus have been painstakingly assembled over the years by diligent statisticians, and often made available to download, free of charge, anywhere in the world. Yet we are spoiled by such luxury, casually dismissing “lies, damned lies and statistics”. The case of Covid-19 reminds us how desperate the situation can become when the statistics simply aren’t there.

When it comes to interpreting the world around us, we need to realise that our feelings can trump our expertise. This explains why we buy things we don’t need, fall for the wrong kind of romantic partner, or vote for politicians who betray our trust. In particular, it explains why we so often buy into statistical claims that even a moment’s thought would tell us cannot be true. Sometimes, we want to be fooled.

Psychologist Ziva Kunda found this effect in the lab, when she showed experimental subjects an article laying out the evidence that coffee or other sources of caffeine could increase the risk to women of developing breast cysts. Most people found the article pretty convincing. Women who drank a lot of coffee did not.

We often find ways to dismiss evidence that we don’t like. And the opposite is true, too: when evidence seems to support our preconceptions, we are less likely to look too closely for flaws. It is not easy to master our emotions while assessing information that matters to us, not least because our emotions can lead us astray in different directions.

We don’t need to become emotionless processors of numerical information – just noticing our emotions and taking them into account may often be enough to improve our judgment. Rather than requiring superhuman control of our emotions, we need simply to develop good habits. Ask yourself: how does this information make me feel? Do I feel vindicated or smug? Anxious, angry or afraid? Am I in denial, scrambling to find a reason to dismiss the claim?

In the early days of the coronavirus epidemic, helpful-seeming misinformation spread even faster than the virus itself. One viral post – circulating on Facebook and email newsgroups – all-too-confidently explained how to distinguish between Covid-19 and a cold, reassured people that the virus was destroyed by warm weather, and incorrectly advised that iced water was to be avoided, while warm water kills any virus. The post, sometimes attributed to “my friend’s uncle”, sometimes to “Stanford hospital board” or some blameless and uninvolved paediatrician, was occasionally accurate but generally speculative and misleading. But still people – normally sensible people – shared it again and again and again. Why? Because they wanted to help others. They felt confused, they saw apparently useful advice, and they felt impelled to share. That impulse was only human, and it was well-meaning – but it was not wise.

Protestors in Edinburgh demonstrating against Covid-19 prevention measures. Photograph: Jeff J Mitchell/Getty Images

Before I repeat any statistical claim, I first try to take note of how it makes me feel. It’s not a foolproof method against tricking myself, but it’s a habit that does little harm, and is sometimes a great deal of help. Our emotions are powerful. We can’t make them vanish, and nor should we want to. But we can, and should, try to notice when they are clouding our judgment.

In 1997, the economists Linda Babcock and George Loewenstein ran an experiment in which participants were given evidence from a real court case about a motorbike accident. They were then randomly assigned to play the role of plaintiff’s attorney (arguing that the injured motorcyclist should receive $100,000 in damages) or defence attorney (arguing that the case should be dismissed or the damages should be low).

The experimental subjects were given a financial incentive to argue their side of the case persuasively, and to reach an advantageous settlement with the other side. They were also given a separate financial incentive to accurately guess what the damages the judge in the real case had actually awarded. Their predictions should have been unrelated to their role-playing, but their judgment was strongly influenced by what they hoped would be true.

Psychologists call this “motivated reasoning”. Motivated reasoning is thinking through a topic with the aim, conscious or unconscious, of reaching a particular kind of conclusion. In a football game, we see the fouls committed by the other team but overlook the sins of our own side. We are more likely to notice what we want to notice. Experts are not immune to motivated reasoning. Under some circumstances their expertise can even become a disadvantage. The French satirist Molière once wrote: “A learned fool is more foolish than an ignorant one.” Benjamin Franklin commented: “So convenient a thing is it to be a reasonable creature, since it enables us to find or make a reason for everything one has a mind to.”

Modern social science agrees with Molière and Franklin: people with deeper expertise are better equipped to spot deception, but if they fall into the trap of motivated reasoning, they are able to muster more reasons to believe whatever they really wish to believe.

One recent review of the evidence concluded that this tendency to evaluate evidence and test arguments in a way that is biased towards our own preconceptions is not only common, but just as common among intelligent people. Being smart or educated is no defence. In some circumstances, it may even be a weakness.

One illustration of this is a study published in 2006 by two political scientists, Charles Taber and Milton Lodge. They wanted to examine the way Americans reasoned about controversial political issues. The two they chose were gun control and affirmative action.

Taber and Lodge asked their experimental participants to read a number of arguments on either side, and to evaluate the strength and weakness of each argument. One might hope that being asked to review these pros and cons might give people more of a shared appreciation of opposing viewpoints; instead, the new information pulled people further apart.

This was because people mined the information they were given for ways to support their existing beliefs. When invited to search for more information, people would seek out data that backed their preconceived ideas. When invited to assess the strength of an opposing argument, they would spend considerable time thinking up ways to shoot it down.

This isn’t the only study to reach this sort of conclusion, but what’s particularly intriguing about Taber and Lodge’s experiment is that expertise made matters worse. More sophisticated participants in the experiment found more material to back up their preconceptions. More surprisingly, they found less material that contradicted them – as though they were using their expertise actively to avoid uncomfortable information. They produced more arguments in favour of their own views, and picked up more flaws in the other side’s arguments. They were vastly better equipped to reach the conclusion they had wanted to reach all along.

Of all the emotional responses we might have, the most politically relevant are motivated by partisanship. People with a strong political affiliation want to be on the right side of things. We see a claim, and our response is immediately shaped by whether we believe “that’s what people like me think”.

Consider this claim about climate change: “Human activity is causing the Earth’s climate to warm up, posing serious risks to our way of life.” Many of us have an emotional reaction to a claim like that; it’s not like a claim about the distance to Mars. Believing it or denying it is part of our identity; it says something about who we are, who our friends are, and the sort of world we want to live in. If I put a claim about climate change in a news headline, or in a graph designed to be shared on social media, it will attract attention and engagement not because it is true or false, but because of the way people feel about it.

If you doubt this, ponder the findings of a Gallup poll conducted in 2015. It found a huge gap between how much Democrats and Republicans in the US worried about climate change. What rational reason could there be for that?

Scientific evidence is scientific evidence. Our beliefs around climate change shouldn’t skew left and right. But they do. This gap became wider the more education people had. Among those with no college education, 45% of Democrats and 23% of Republicans worried “a great deal” about climate change. Yet among those with a college education, the figures were 50% of Democrats and 8% of Republicans. A similar pattern holds if you measure scientific literacy: more scientifically literate Republicans and Democrats are further apart than those who know very little about science.

If emotion didn’t come into it, surely more education and more information would help people to come to an agreement about what the truth is – or at least, the current best theory? But giving people more information seems actively to polarise them on the question of climate change. This fact alone tells us how important our emotions are. People are straining to reach the conclusion that fits with their other beliefs and values – and the more they know, the more ammunition they have to reach the conclusion they hope to reach.

Anti-carbon tax protesters in Australia in 2011. Photograph: Torsten Blackwood/AFP/Getty Images

In the case of climate change, there is an objective truth, even if we are unable to discern it with perfect certainty. But as you are one individual among nearly 8 billion on the planet, the environmental consequences of what you happen to think are irrelevant. With a handful of exceptions – say, if you’re the president of China – climate change is going to take its course regardless of what you say or do. From a self-centred point of view, the practical cost of being wrong is close to zero. The social consequences of your beliefs, however, are real and immediate.

Imagine that you’re a barley farmer in Montana, and hot, dry summers are ruining your crop with increasing frequency. Climate change matters to you. And yet rural Montana is a conservative place, and the words “climate change” are politically charged. Anyway, what can you personally do about it?

Here’s how one farmer, Erik Somerfeld, threads that needle, as described by the journalist Ari LeVaux: “In the field, looking at his withering crop, Somerfeld was unequivocal about the cause of his damaged crop – ‘climate change’. But back at the bar, with his friends, his language changed. He dropped those taboo words in favour of ‘erratic weather’ and ‘drier, hotter summers’ – a not-uncommon conversational tactic in farm country these days.”

If Somerfeld lived in Portland, Oregon, or Brighton, East Sussex, he wouldn’t need to be so circumspect at his local tavern – he’d be likely to have friends who took climate change very seriously indeed. But then those friends would quickly ostracise someone else in the social group who went around loudly claiming that climate change is a Chinese hoax.

So perhaps it is not so surprising after all to find educated Americans poles apart on the topic of climate change. Hundreds of thousands of years of human evolution have wired us to care deeply about fitting in with those around us. This helps to explain the findings of Taber and Lodge that better-informed people are actually more at risk of motivated reasoning on politically partisan topics: the more persuasively we can make the case for what our friends already believe, the more our friends will respect us.

It’s far easier to lead ourselves astray when the practical consequences of being wrong are small or non-existent, while the social consequences of being “wrong” are severe. It’s no coincidence that this describes many controversies that divide along partisan lines.

It’s tempting to assume that motivated reasoning is just something that happens to other people. I have political principles; you’re politically biased; he’s a fringe conspiracy theorist. But we would be wiser to acknowledge that we all think with our hearts rather than our heads sometimes.

Kris De Meyer, a neuroscientist at King’s College, London, shows his students a message describing an environmental activist’s problem with climate change denialism:


To summarise the climate deniers’ activities, I think we can say that:

(1) Their efforts have been aggressive while ours have been defensive.

(2) The deniers’ activities are rather orderly – almost as if they had a plan working for them.

I think the denialist forces can be characterised as dedicated opportunists. They are quick to act and seem to be totally unprincipled in the type of information they use to attack the scientific community. There is no question, though, that we have been inept in getting our side of the story, good though it may be, across to the news media and the public.

The students, all committed believers in climate change, outraged at the smokescreen laid down by the cynical and anti-scientific deniers, nod in recognition. Then De Meyer reveals the source of the text. It’s not a recent email. It’s taken, sometimes word for word, from an infamous internal memo written by a cigarette marketing executive in 1968. The memo is complaining not about “climate deniers” but about “anti-cigarette forces”, but otherwise, few changes were required.

You can use the same language, the same arguments, and perhaps even have the same conviction that you’re right, whether you’re arguing (rightly) that climate change is real or (wrongly) that the cigarette-cancer link is not.

(Here’s an example of this tendency that, for personal reasons, I can’t help but be sensitive about. My left-leaning, environmentally conscious friends are justifiably critical of ad hominem attacks on climate scientists. You know the kind of thing: claims that scientists are inventing data because of their political biases, or because they’re scrambling for funding from big government. In short, smearing the person rather than engaging with the evidence.

Yet the same friends are happy to embrace and amplify the same kind of tactics when they are used to attack my fellow economists: that we are inventing data because of our political biases, or scrambling for funding from big business. I tried to point out the parallel to one thoughtful person, and got nowhere. She was completely unable to comprehend what I was talking about. I’d call this a double standard, but that would be unfair – it would suggest that it was deliberate. It’s not. It’s an unconscious bias that’s easy to see in others and very hard to see in ourselves.)

Our emotional reaction to a statistical or scientific claim isn’t a side issue. Our emotions can, and often do, shape our beliefs more than any logic. We are capable of persuading ourselves to believe strange things, and to doubt solid evidence, in service of our political partisanship, our desire to keep drinking coffee, our unwillingness to face up to the reality of our HIV diagnosis, or any other cause that invokes an emotional response.

But we shouldn’t despair. We can learn to control our emotions – that is part of the process of growing up. The first simple step is to notice those emotions. When you see a statistical claim, pay attention to your own reaction. If you feel outrage, triumph, denial, pause for a moment. Then reflect. You don’t need to be an emotionless robot, but you could and should think as well as feel.

Most of us do not actively wish to delude ourselves, even when that might be socially advantageous. We have motives to reach certain conclusions, but facts matter, too. Lots of people would like to be movie stars, billionaires or immune to hangovers, but very few people believe that they actually are. Wishful thinking has limits. The more we get into the habit of counting to three and noticing our knee-jerk reactions, the closer to the truth we are likely to get.

For example, one survey, conducted by a team of academics, found that most people were perfectly able to distinguish serious journalism from fake news, and also agreed that it was important to amplify the truth, not lies. Yet the same people would happily share headlines such as “Over 500 ‘Migrant Caravaners’ Arrested With Suicide Vests”, because at the moment at which they clicked “share”, they weren’t stopping to think. They weren’t thinking, “Is this true?”, and they weren’t thinking, “Do I think the truth is important?” 

Instead, as they skimmed the internet in that state of constant distraction that we all recognise, they were carried away with their emotions and their partisanship. The good news is that simply pausing for a moment to reflect was all it took to filter out a lot of the misinformation. It doesn’t take much; we can all do it. All we need to do is acquire the habit of stopping to think.

Inflammatory memes or tub-thumping speeches invite us to leap to the wrong conclusion without thinking. That’s why we need to be calm. And that is also why so much persuasion is designed to arouse us – our lust, our desire, our sympathy or our anger. When was the last time Donald Trump, or for that matter Greenpeace, tweeted something designed to make you pause in calm reflection? Today’s persuaders don’t want you to stop and think. They want you to hurry up and feel. Don’t be rushed.

Why the Modi government gets away with lies, and how the opposition could change that

As with Putin’s Russia and Trump’s America, India faces a ‘fire-hosing of falsehood’. Mere fact-checking won’t defeat it writes SHIVAM VIJ in The Print

Illustration by Soham Sen | ThePrint Team


The Narendra Modi government announces a grand stimulus ‘package’ that it claims is worth Rs 20 lakh crore or ‘10 per cent’ of India’s GDP. But barely a fraction of it is new money being pumped into the economy. What is made to look like a stimulus is mostly a grand loan mela.

The Modi government is making hungry migrant labourers pay train fare. When this became a political hot potato, it said it was paying 85 per cent per cent of the fare and the state governments were paying the rest 15 per cent. Truth was that that 85 per cent was notional subsidy — in effect, the migrants were being charged the usual fare, and in some places, even more.

If no one else, at least the endless sea of migrant labourers would be able to see through the ‘85 per cent’ lie. It is curious that the Modi government openly lies — lies that are obvious and blatant. Just a few examples:

Narendra Modi said on the top of his voice that there had been no talk of a National Register of Citizens (NRC) in his government, when in fact both the President of India and the Home Minister had said it in Parliament.

Narendra Modi said the purpose of demonetisation was to destroy black money but when that didn’t work, his government kept changing goal-posts. Many lies to hide one truth: that demonetisation had failed.

Electoral bonds make political donations opaque, but the Modi government says they bring transparency. The full list of the Modi government’s lies could fill a library.

DOUBLETHINK

The Modi government has made lying an art form. This non-stop obvious lying was described by George Orwell as doublethink: “Every message from the extremely repressive leadership reverses the truth. Officials repeat ‘war is peace’ and ‘freedom is slavery,’ for example. The Ministry of Truth spreads lies. The Ministry of Love tortures lovers.”

People are thus expected to believe as true what is clearly false, and also take at face value mutually contradictory statements. The Modi government talked about NRC, but it also did not talk about it. The Modi government is making migrants pay for train fares, but at the same time, it is not charging them. Doublethink also applies other Orwellian principles — Newspeak, Doublespeak, Thoughtcrime, etc.

But why do people accept it all so willingly? Why do the people who are lied to every day go and vote for the same BJP?

There are many obvious answers to this question: weak opposition, mouthpiece media, social media manipulation, and Modi’s personality cult that makes his voters repose great faith in him.

But the lies are so obvious, you wonder why anyone would lie so obviously. Surely, when someone is caught lying they can’t be considered credible anymore?

What’s happening here is the plain assertion of power. Our politics has become a contest of who gets to lie and get away with it and who will have to go on a back-foot when their lies are caught.

When the Modi government lies so blatantly, it is basically saying: ‘Yes we will lie to make a mockery of your questions. Do what you can.’

Fire-hosing of falsehood

In 2016, Christopher Paul and Miriam Matthews wrote a paper for RAND Corporation, an American think-tank, in which they analysed propaganda techniques used by the Vladimir Putin government in Russia. They called it the “Firehose of Falsehood” (read it here). The Russian model is not to simply make you believe a lie — the lie is often so obviously a lie, you’d be a fool to believe it. The idea is to “entertain, confuse and overwhelm” the audience.

They identified four distinct features of the Putin propaganda model, all of which are true for the Modi propaganda machinery as well, as they are for Donald Trump’s.

1) High volume and multi-channel: The Modi propaganda machine will bombard people with a message through multiple channels. By “multiple” we really mean multiple — you will even see Twitter handles claiming to be Indian Muslims saying the same things as the far-Right Hindutva handles. Of course, some of the Muslim handles are fake. But when you see everyone from Akshay Kumar to Tabassum Begum support an idea, you’re inclined to doubt yourself. If everyone from Rubika Liyaquat to your WhatsApp-fed uncle is saying the same thing, it must be right. If so many people are saying the Citizenship (Amendment) Act will grant citizenship and not take it away, they must be right.

2) Rapid, continuous and repetitive: The hashtags, memes and emotionally charged videos will be ready before any announcement is made. The moment the announcement is made, both social and mainstream media will start bombarding you with messages in support of it. The volume and speed of the propaganda will barely leave you with the mind space to judge for yourself.

While the government will be careful to avoid saying it is not charging migrants, its deniable propaganda proxies will go around suggesting exactly that until the voice of the doubters has been drowned out. (A liberal journalist I know actually thought the migrants were not having to pay train fares anymore.)

3) Lacks commitment to objective reality: In other words, fake news. We know why fake news works: confirmation bias, information overload, emotional manipulation, the willingness to believe a message when it is shared by a trusted friend, and so on. There’s no dearth of this in the Modi propaganda ecosystem. There are countless fake news factories like OpIndia and Postcard News. Moreover, the mainstream media itself has been co-opted to manufacture fake news at scale, as the absolutely fictional charges of JNU students wanting India to be split into pieces (“Tukde tukde gang”) shows.

PM Modi himself is happy to lie for political posturing: from attributing a fake quote to Omar Abdullah, to saying there are no detention centres in the country, to exaggerating all kinds of data.

4) Lacks commitment to consistency: This is the bit where the fake news and claims are exposed, and yet they don’t hurt the leader. One day the Modi government says demonetisation is for destroying black money and next day it says it was to push cashless transactions, and third day it says the idea was to widen the tax base.

Ordinarily, such contradictions should hurt the credibility of Modi and his government. But, coupled with the three points above, the RAND researchers suggest, “fire hosing” manages to sell the changed narrative as new information, a change of opinion, or just new, advanced or supplementary facts presented by different actors.

How to fight the fire-hosing of falsehood

The RAND corporation researchers also suggest five ways for the United States to counter the Russian “fire-hosing of falsehood”. These are applicable to any actor who undertakes this propaganda model, including Modi and Trump.

1. First Information Report: Try to be the first in presenting information on a particular issue. In shaping public opinion, the first impression can be the last impression. (With our lazy opposition, this ain’t happening, but the Congress party’s announcement of paying train fares for migrant labourers was one example of creating the first impression of an issue.)

2. Highlight the lying, not just the lies: The world needs fact-checkers, but they’re not going to be able to stop the fire-hosing of falsehood. That’s like taking paracetamol for Covid-19. You may need it for the fever, but it won’t kill the virus.What might treat the virus of fire-hosing, however, according to the RAND researchers, is to chip away at the credibility of the liar by simply pointing out that he’s a serial liar. M.K. Gandhi’s assertion of truth as the core of his politics, for example, served the purpose of painting the British colonial rule as being based on falsehoods.

3. Identify and attack the goal of the propaganda: Instead of simply fact-checking the propaganda, the political opponents need to understand the objective of the lies and attack those. So, if the objective of lying about migrants having to pay for train fares is to not let them travel for free, the opposition should spend great time and energy addressing migrant labourers about how the government is being insensitive to their plight. This will take a lot more work on the ground, and simply tweeting facts won’t be enough.

4. Compete: Across the world, fire-hosing of falsehood is becoming a powerful propaganda tool. Those who want to defeat such propaganda may have to do their own fire-hosing of falsehood. As the Hindi saying goes, iron cuts iron. When public opinion is being manipulated with fake news and lies, the opposition cannot win the game with mere fact-checking. It may have to do its own rapid and continuous misinformation with little regard for the truth. The RAND researchers suggest this is what the US should do against Russia.

5. Turn off the tap: Lastly, attack the opponent’s supply chain of lies. If opposition-ruled states are not cracking down on fake news and communal hate-mongers in their states, for example, they’re making a huge mistake.

On writing a column - Credibility of political pundits is low but voters’ need for punditry high

By Vinod Mehta in The Times of India

Soon, the NDA government led by Prime Minister Narendra Modi will chalk up 100 days in office. For some mysterious reason this magic figure is considered an appropriate moment by the media to take stock. It is a rite of passage.

One expects that the verdict on his performance will be sharply divided. One take on the report card will show BJP scoring a century in as many days. The other take will give the party half a ton, and another will award the government less than pass marks. In a robust democracy with a lively media, all three perspectives must be seriously examined before final evaluation is made. The difficulty for citizens is they lack the tools and instruments to make an informed judgment.

So, what options does the voter have? He can speak with friends. He can go online. He can tap a person who has a reputation for being knowledgeable in such matters. But most, i suspect, will rely on the media pundit in the shape of the opinion page writer. I would go so far as to say that political commentary is the main resource available to most people to help them make up their mind.

So far so good. Unfortunately, at this precise moment a problem arises. Recently, i was talking to an old colleague, and i told him i had read an article by Mr X which i liked. “Oh, he is not to be believed,” he replied. “He gets all his information from xxx” And he then mentioned the name of a minister in the present government. My interlocutor added that the gentleman we were talking about had an axe to grind, an `agenda`. Accordingly, what he wrote needed to be taken with a shovel of salt.

Frankly, we live in such ‘interesting’ times that it is virtually impossible to locate a commentator without an agenda. An agenda-less commentator is an endangered species. Which brings us back to the luckless citizen looking for views and positions he can put his faith in. Who does he turn to if all public affairs gurus are openly partial?

I will not be revealing any secrets when i say the credibility of the pundit is at an all-time low, if you exclude the Emergency. The prevailing atmosphere of suspicion and conspiracy theories is so toxic we should not be surprised by the strong inclination towards negativity in the people. As a result, even while he is perusing a 900-word column, the reader is wondering, “Why is this lying bugger lying to me?”

These days anyone who has spent a couple of years in the profession feels qualified to become a pundit. Nothing wrong with that, but the question is, what preparation did the said journalist make before he walked into the hallowed editorial space? When i became an editor in 1974, for over a decade i never dared to write an opinion piece. I was terrified because i felt too raw and too naive. Instead, i embarked on a course of self-education.

Sadly, there were, and are, no textbooks on column writing, no mass communication institutes which can teach you the craft. The sole guide: read pundits you admire — those with a standing for honesty and objectivity.

By objectivity i am not suggesting you abandon your prejudices and preferences, but keep them in check. And, sometimes, restrain them if the message on the wall is too clear. Pseudo-secularists and assorted Modi-detesters could not ignore the hawa blowing in his favour across the country in 2013. Whatever your predilections, you had to take note of the wind whose intensity was growing by the week.

If i can identify one quality the reader is looking for in an opinion column it is ‘trust’. The reader is aware from where the columnist is coming from, what his leanings are. Despite that, he needs to ‘trust’ the writer. He must feel confident the column, at the least, will acknowledge reality, not deny reality. In my 40-odd years of editorship the highest compliment paid to me, among zillions of abuses, went, “I don’t like your opinions but i don’t think you will deliberately mislead me.”

At a time when the entire media is increasingly perceived with suspicion, why should the column-writer remain uncontaminated by partisanship? After all, the pundit is a creature of the environment we all inhabit. He does not live on Mars.

The challenge for those privileged to contribute to the ‘heart of a newspaper’, then, becomes even more daunting. In a society where columnists and editors play favourites, the victim is the reader. Who looks after his interest? Media people day in and day out affirm their commitment to the reader, and the reader alone. Alas, the commitment doesn’t stand up to scrutiny.

In short, truth and readability are essential for a column. Remember you don’t want to tell the truth in a way which puts your reader to sleep.

Is there any solution for the present depressing situation? I cannot easily think of one. However, if a solution exists it lies in the hands of the reader. He must reject those columnists (and the papers they write for) that flagrantly violate the basic canons of trust. The reader will be doing the media a favour and also the pundit, who must know he has been caught out.