"I DON'T play GAMES": understanding what this really means

 

The ‘Yes sir!’ society

THERE are two lessons from the tragic PIA crash in Karachi, and if we don’t learn from them, the almost 100 victims would have died in vain writes Irfan Husain in The Dawn.

Firstly — and more easily fixable — is the business about pilots flying on fake licences. There is nothing to suggest that the captain of the ill-fated PIA flight was one of them, but his mishandling of the aircraft is an indicator of the culture of incompetence that rules our skies.

When an Airblue plane crashed a few miles from Islamabad a decade ago, killing all on board, the inquiry report shone a laser on the relationship between the captain and his first officer. Although the latter informed the captain that their approach was too low, and they should pull up, he was ignored because to have agreed would have indicated that his junior knew more than the captain did.

Then there was the near-tragedy at Islamabad airport some 30 years ago when the PIA Jumbo scraped home on its belly. Initially, the pilot was praised for executing a masterful belly landing, saving many lives. It then emerged that he had switched off the sensor that warns pilots they were too close to land without lowering the undercarriage.

I’m sure there are many other examples of why PIA is considered such a dangerous airline to fly on. The powerful pilots union (Palpa) prevents any meaningful punishment for blatantly dangerous manoeuvres.

But fake licences should not surprise us: remember the recent Axact scandal where millions of dollars were coined by the Karachi-based firm selling fake degrees around the world? After a flurry of arrests and court cases, the whole affair seems to have been forgotten.

Perhaps even a dysfunctional country like Pakistan can fix the problem of fake licences. But if this happens, it’ll be due more to foreign pressure and our image abroad than any concern for the lives of Pakistani passengers.

However, it is the second problem that is far more pervasive and deeply entrenched. As the Airblue report highlighted, the rigid hierarchy, even on a three-man flight deck, was such that the first officer could not do more to convince his captain of his dangerous approach than utter emollient words like ‘Sir, are too low’. The captain was apparently too full of his authority to agree, and insisted on maintaining his course: any change would have implied that his junior officer knew more than he did.

Now multiply this attitude across our entire society. When the boss is convinced he (seldom she) knows best, you will never get the optimum outcome. Take Kargil as an example of poor planning resulting from this rigid hierarchical approach.

When Musharraf cursorily ran the broad outline of his madcap adventure past Nawaz Sharif, there were few of the obvious questions that should have been asked. The kitchen cabinet reportedly saw the prime minister’s mild approval, and kept quiet. Musharraf’s team, for their part, only spoke out against the enterprise after they had retired. They, too, were prisoners of the ‘Yes, sir! No, sir!’ syndrome. To this day, the report of the debacle has not been released, even as an internal case study, as far as I know.

But it’s not just the military that operates on this principle. When I was president of a private university, I used to call weekly meetings of the teaching staff. At these sessions, I put forth my ideas for changes, and asked my colleagues to give counter-arguments. Although these were educated, intelligent people, they almost always stayed quiet, or agreed with me.

And when I monitored classes from the back of the room, I noticed that students hardly ever asked questions. Although I hated interfering, I would almost urge them to query or criticise. Again, silence. So clearly, the senior/junior hierarchy was at work. This is why we produce so few inquiring, curious minds.

Sucking up to the boss for promotions is a global malady, but mostly, it ends at the end of work. Here, we live with it each moment of our lives.

Our brainwashing begins earlier than the classroom. Boys are deemed too inexperienced to choose their careers, so their fathers decide. Girls aren’t practical enough to choose their husbands, so their parents use force, if necessary, to select a ‘suitable’ spouse. I know things are changing for the younger generation in a certain class. But for the majority, these major decisions are still made by parents.

Much of Asia is prisoner to this paternalistic approach, and is the poorer for it. Individuality is crushed, and bad decision-making is just one result. When people end up in the wrong career, or a disastrous, abusive marriage, relations between parents and their children can be ruined for life.

I am informed by a friend that Japan Airlines trains its pilots to overcome their childhood conditioning, and stand their ground. But how do we transfer this to our entire society?

Why a leader’s past record is no guide to future success

Successful leadership depends on context, collaboration and character writes Andrew Hill in The FT

“There goes that queer-looking fish with the ginger beard again. Do you know who he is? I keep seeing him creep about this place like a lost soul with nothing better to do.”

That was the verdict of the then Bank of England governor on Montagu Norman, who, five years later, was to take over the top job. “Nothing in his background suggested that he would be well suited to the work of a central banker,” Liaquat Ahamed wrote in his prizewinning book Lords of Finance.

Plenty in Christine Lagarde’s background suggests she will be much better suited to run the European Central Bank: her political nous, her communication skills, her leadership of the International Monetary Fund through turbulent financial times.

Critics, though, have focused on the former corporate lawyer and finance minister’s lack of deep academic economic training, and her dearth of experience with the technicalities of monetary policy.

But how much should the past record of a candidate be a guide for how they will handle their next job? Not as much as we might think.

The truth is that successful leadership depends on context, collaboration and character as much as qualifications. For all the efforts to codify and computerise the specifications of important jobs, the optimal chemistry of experience, aptitude, potential, and mindset remains hard to define. Throw in the imponderable future in which such leaders are bound to operate and it is no wonder that sometimes the seemingly best-qualified stumble, while the qualification-free thrive.

For one thing, even if the challenge confronting a leader looks the same as one they handled in the past, it is very rarely identical — and nor is the leader. That is one reason big companies offer their most promising executives experience across countries, cultures and operations, from finance to the front line, and why some recruiters emphasise potential as much as the formal résumé of their candidates.

Curiosity is a big predictor of potential — and of success — according to Egon Zehnder, the executive search company. It asks referees what the candidate they have backed is really curious about. “It is a question that takes people aback, so they have to think anew about that person,” Jill Ader, chairwoman, told me recently.

I think there are strong reasons to back master generalists for senior roles. Polymathic leaders offer alternative perspectives and may even be better at fostering innovation, according to one study. In his new book Range, David Epstein offers this warning against over-specialisation: “Everyone is digging deeper into their own trench and rarely standing up to look in the next trench over.”

Take this to the other extreme of ignoring specialist qualifications, however, and you are suddenly in the world of blaggers, blowhards and blackguards, who bluff their way up the leadership ladder until the Peter Principle applies, and a further rise is prohibited by their own incompetence.

The financial crisis exposed the weaknesses of large banks, such as HBOS and Royal Bank of Scotland in the UK, chaired by non-bankers. Some of the same concerns about a dearth of deep financial qualifications now nag at the leaders of fintech companies, whose promise is based in part on their boast that they will be “different” from longer established incumbents.

In a flailing search for the reasons for its current political mess, the UK has blamed the self-confident dilettantism of some Oxford university graduates, while the US bemoans the superficial attractions of stars of television reality shows. These parallel weaknesses for pure bluster over proven expertise have brought us Boris Johnson and Donald Trump, respectively.

A plausible defence of both Mr Johnson and Mr Trump is that they should be able to play to their specific strengths, while surrounding themselves with experts who can handle the technical work.

Ms Lagarde, too, will want to draw on the team of experts around her. She is wise enough to know she cannot rely on silky political skills and neglect the plumbing of monetary policy.

At the same time, history suggests she should not assume her paper credentials or wide experience will be enough to guarantee success in Frankfurt. The Bank of England’s Norman was eccentric and neurotic, and his counterpart at the Banque de France, Émile Moreau, had a “quite rudimentary and at times confused” understanding of monetary economics, whereas Benjamin Strong at the New York Federal Reserve, was a born leader, and Hjalmar Schacht of Germany’s Reichsbank “came to the job with an array of qualifications”.

Yet together this quartet of the under- and overqualified made a series of mistakes that pitched the world into the Great Depression.

A simple guide to statistics in the age of deception

Tim Harford in The Financial Times





“The best financial advice for most people would fit on an index card.” That’s the gist of an offhand comment in 2013 by Harold Pollack, a professor at the University of Chicago. Pollack’s bluff was duly called, and he quickly rushed off to find an index card and scribble some bullet points — with respectable results. 

When I heard about Pollack’s notion — he elaborated upon it in a 2016 book — I asked myself: would this work for statistics, too? There are some obvious parallels. In each case, common sense goes a surprisingly long way; in each case, dizzying numbers and impenetrable jargon loom; in each case, there are stubborn technical details that matter; and, in each case, there are people with a sharp incentive to lead us astray. 

The case for everyday practical numeracy has never been more urgent. Statistical claims fill our newspapers and social media feeds, unfiltered by expert judgment and often designed as a political weapon. We do not necessarily trust the experts — or more precisely, we may have our own distinctive view of who counts as an expert and who does not.  

Nor are we passive consumers of statistical propaganda; we are the medium through which the propaganda spreads. We are arbiters of what others will see: what we retweet, like or share online determines whether a claim goes viral or vanishes. If we fall for lies, we become unwittingly complicit in deceiving others. On the bright side, we have more tools than ever to help weigh up what we see before we share it — if we are able and willing to use them. 

In the hope that someone might use it, I set out to write my own postcard-sized citizens’ guide to statistics. Here’s what I learnt. 

Professor Pollack’s index card includes advice such as “Save 20 per cent of your money” and “Pay your credit card in full every month”. The author Michael Pollan offers dietary advice in even pithier form: “Eat Food. Not Too Much. Mostly Plants.” Quite so, but I still want a cheeseburger.  

However good the advice Pollack and Pollan offer, it’s not always easy to take. The problem is not necessarily ignorance. Few people think that Coca-Cola is a healthy drink, or believe that credit cards let you borrow cheaply. Yet many of us fall into some form of temptation or other. That is partly because slick marketers are focused on selling us high-fructose corn syrup and easy credit. And it is partly because we are human beings with human frailties. 

With this in mind, my statistical postcard begins with advice about emotion rather than logic. When you encounter a new statistical claim, observe your feelings. Yes, it sounds like a line from Star Wars, but we rarely believe anything because we’re compelled to do so by pure deduction or irrefutable evidence. We have feelings about many of the claims we might read — anything from “inequality is rising” to “chocolate prevents dementia”. If we don’t notice and pay attention to those feelings, we’re off to a shaky start. 

What sort of feelings? Defensiveness. Triumphalism. Righteous anger. Evangelical fervour. Or, when it comes to chocolate and dementia, relief. It’s fine to have an emotional response to a chart or shocking statistic — but we should not ignore that emotion, or be led astray by it. 

There are certain claims that we rush to tell the world, others that we use to rally like-minded people, still others we refuse to believe. Our belief or disbelief in these claims is part of who we feel we are. “We all process information consistent with our tribe,” says Dan Kahan, professor of law and psychology at Yale University. 

In 2005, Charles Taber and Milton Lodge, political scientists at Stony Brook University, New York, conducted experiments in which subjects were invited to study arguments around hot political issues. Subjects showed a clear confirmation bias: they sought out testimony from like-minded organisations. For example, subjects who opposed gun control would tend to start by reading the views of the National Rifle Association. Subjects also showed a disconfirmation bias: when the researchers presented them with certain arguments and invited comment, the subjects would quickly accept arguments with which they agreed, but devote considerable effort to disparage opposing arguments.  

Expertise is no defence against this emotional reaction; in fact, Taber and Lodge found that better-informed experimental subjects showed stronger biases. The more they knew, the more cognitive weapons they could aim at their opponents. “So convenient a thing it is to be a reasonable creature,” commented Benjamin Franklin, “since it enables one to find or make a reason for everything one has a mind to do.” 

This is why it’s important to face up to our feelings before we even begin to process a statistical claim. If we don’t at least acknowledge that we may be bringing some emotional baggage along with us, we have little chance of discerning what’s true. As the physicist Richard Feynman once commented, “You must not fool yourself — and you are the easiest person to fool.” 

The second crucial piece of advice is to understand the claim. That seems obvious. But all too often we leap to disbelieve or believe (and repeat) a claim without pausing to ask whether we really understand what the claim is. To quote Douglas Adams’s philosophical supercomputer, Deep Thought, “Once you know what the question actually is, you’ll know what the answer means.” 

For example, take the widely accepted claim that “inequality is rising”. It seems uncontroversial, and urgent. But what does it mean? Racial inequality? Gender inequality? Inequality of opportunity, of consumption, of education attainment, of wealth? Within countries or across the globe? 

Even given a narrower claim, “inequality of income before taxes is rising” (and you should be asking yourself, since when?), there are several different ways to measure this. One approach is to compare the income of people at the 90th percentile and the 10th percentile, but that tells us nothing about the super-rich, nor the ordinary people in the middle. An alternative is to examine the income share of the top 1 per cent — but this approach has the opposite weakness, telling us nothing about how the poorest fare relative to the majority.  

There is no single right answer — nor should we assume that all the measures tell a similar story. In fact, there are many true statements that one can make about inequality. It may be worth figuring out which one is being made before retweeting it. 

Perhaps it is not surprising that a concept such as inequality turns out to have hidden depths. But the same holds true of more tangible subjects, such as “a nurse”. Are midwives nurses? Health visitors? Should two nurses working half-time count as one nurse? Claims over the staffing of the UK’s National Health Service have turned on such details. 

All this can seem like pedantry — or worse, a cynical attempt to muddy the waters and suggest that you can prove anything with statistics. But there is little point in trying to evaluate whether a claim is true if one is unclear what the claim even means. 

Imagine a study showing that kids who play violent video games are more likely to be violent in reality. Rebecca Goldin, a mathematician and director of the statistical literacy project STATS, points out that we should ask questions about concepts such as “play”, “violent video games” and “violent in reality”. Is Space Invaders a violent game? It involves shooting things, after all. And are we measuring a response to a questionnaire after 20 minutes’ play in a laboratory, or murderous tendencies in people who play 30 hours a week? “Many studies won’t measure violence,” says Goldin. “They’ll measure something else such as aggressive behaviour.” Just like “inequality” or “nurse”, these seemingly common sense words hide a lot of wiggle room. 

Two particular obstacles to our understanding are worth exploring in a little more detail. One is the question of causation. “Taller children have a higher reading age,” goes the headline. This may summarise the results of a careful study about nutrition and cognition. Or it may simply reflect the obvious point that eight-year-olds read better than four-year-olds — and are taller. Causation is philosophically and technically a knotty business but, for the casual consumer of statistics, the question is not so complicated: just ask whether a causal claim is being made, and whether it might be justified. 

Returning to this study about violence and video games, we should ask: is this a causal relationship, tested in experimental conditions? Or is this a broad correlation, perhaps because the kind of thing that leads kids to violence also leads kids to violent video games? Without clarity on this point, we don’t really have anything but an empty headline.  

We should never forget, either, that all statistics are a summary of a more complicated truth. For example, what’s happening to wages? With tens of millions of wage packets being paid every month, we can only ever summarise — but which summary? The average wage can be skewed by a small number of fat cats. The median wage tells us about the centre of the distribution but ignores everything else. 

Or we might look at the median increase in wages, which isn’t the same thing as the increase in the median wage — not at all. In a situation where the lowest and highest wages are increasing while the middle sags, it’s quite possible for the median pay rise to be healthy while median pay falls.  

Sir Andrew Dilnot, former chair of the UK Statistics Authority, warns that an average can never convey the whole of a complex story. “It’s like trying to see what’s in a room by peering through the keyhole,” he tells me.  

In short, “you need to ask yourself what’s being left out,” says Mona Chalabi, data editor for The Guardian US. That applies to the obvious tricks, such as a vertical axis that’s been truncated to make small changes look big. But it also applies to the less obvious stuff — for example, why does a graph comparing the wages of African-Americans with those of white people not also include data on Hispanic or Asian-Americans? There is no shame in leaving something out. No chart, table or tweet can contain everything. But what is missing can matter. 

Channel the spirit of film noir: get the backstory. Of all the statistical claims in the world, this particular stat fatale appeared in your newspaper or social media feed, dressed to impress. Why? Where did it come from? Why are you seeing it?  

Sometimes the answer is little short of a conspiracy: a PR company wanted to sell ice cream, so paid a penny-ante academic to put together the “equation for the perfect summer afternoon”, pushed out a press release on a quiet news day, and won attention in a media environment hungry for clicks. Or a political donor slung a couple of million dollars at an ideologically sympathetic think-tank in the hope of manufacturing some talking points. 

Just as often, the answer is innocent but unedifying: publication bias. A study confirming what we already knew — smoking causes cancer — is unlikely to make news. But a study with a surprising result — maybe smoking doesn’t cause cancer after all — is worth a headline. The new study may have been rigorously conducted but is probably wrong: one must weigh it up against decades of contrary evidence. 

Publication bias is a big problem in academia. The surprising results get published, the follow-up studies finding no effect tend to appear in lesser journals if they appear at all. It is an even bigger problem in the media — and perhaps bigger yet in social media. Increasingly, we see a statistical claim because people like us thought it was worth a Like on Facebook. 

David Spiegelhalter, president of the Royal Statistical Society, proposes what he calls the “Groucho principle”. Groucho Marx famously resigned from a club — if they’d accept him as a member, he reasoned, it couldn’t be much of a club. Spiegelhalter feels the same about many statistical claims that reach the headlines or the social media feed. He explains, “If it’s surprising or counter-intuitive enough to have been drawn to my attention, it is probably wrong.”  

OK. You’ve noted your own emotions, checked the backstory and understood the claim being made. Now you need to put things in perspective. A few months ago, a horrified citizen asked me on Twitter whether it could be true that in the UK, seven million disposable coffee cups were thrown away every day.  

I didn’t have an answer. (A quick internet search reveals countless repetitions of the claim, but no obvious source.) But I did have an alternative question: is that a big number? The population of the UK is 65 million. If one person in 10 used a disposable cup each day, that would do the job.  

Many numbers mean little until we can compare them with a more familiar quantity. It is much more informative to know how many coffee cups a typical person discards than to know how many are thrown away by an entire country. And more useful still to know whether the cups are recycled (usually not, alas) or what proportion of the country’s waste stream is disposable coffee cups (not much, is my guess, but I may be wrong).  

So we should ask: how big is the number compared with other things I might intuitively understand? How big is it compared with last year, or five years ago, or 30? It’s worth a look at the historical trend, if the data are available.  

Finally, beware “statistical significance”. There are various technical objections to the term, some of which are important. But the simplest point to appreciate is that a number can be “statistically significant” while being of no practical importance. Particularly in the age of big data, it’s possible for an effect to clear this technical hurdle of statistical significance while being tiny. 

One study was able to demonstrate that unborn children exposed to a heatwave while in the womb went on to earn less as adults. The finding was statistically significant. But the impact was trivial: $30 in lost income per year. Just because a finding is statistically robust does not mean it matters; the word “significance” obscures that. 

In an age of computer-generated images of data clouds, some of the most charming data visualisations are hand-drawn doodles by the likes of Mona Chalabi and the cartoonist Randall Munroe. But there is more to these pictures than charm: Chalabi uses the wobble of her pen to remind us that most statistics have a margin of error. A computer plot can confer the illusion of precision on what may be a highly uncertain situation. 

“It is better to be vaguely right than exactly wrong,” wrote Carveth Read in Logic (1898), and excessive precision can lead people astray. On the eve of the US presidential election in 2016, the political forecasting website FiveThirtyEight gave Donald Trump a 28.6 per cent chance of winning. In some ways that is impressive, because other forecasting models gave Trump barely any chance at all. But how could anyone justify the decimal point on such a forecast? No wonder many people missed the basic message, which was that Trump had a decent shot. “One in four” would have been a much more intuitive guide to the vagaries of forecasting.

Exaggerated precision has another cost: it makes numbers needlessly cumbersome to remember and to handle. So, embrace imprecision. The budget of the NHS in the UK is about £10bn a month. The national income of the United States is about $20tn a year. One can be much more precise about these things, but carrying the approximate numbers around in my head lets me judge pretty quickly when — say — a £50m spending boost or a $20bn tax cut is noteworthy, or a rounding error. 

My favourite rule of thumb is that since there are 65 million people in the UK and people tend to live a bit longer than 65, the size of a typical cohort — everyone retiring or leaving school in a given year — will be nearly a million people. Yes, it’s a rough estimate — but vaguely right is often good enough. 

Be curious. Curiosity is bad for cats, but good for stats. Curiosity is a cardinal virtue because it encourages us to work a little harder to understand what we are being told, and to enjoy the surprises along the way.  

This is partly because almost any statistical statement raises questions: who claims this? Why? What does this number mean? What’s missing? We have to be willing — in the words of UK statistical regulator Ed Humpherson — to “go another click”. If a statistic is worth sharing, isn’t it worth understanding first? The digital age is full of informational snares — but it also makes it easier to look a little deeper before our minds snap shut on an answer.  

While curiosity gives us the motivation to ask another question or go another click, it gives us something else, too: a willingness to change our minds. For many of the statistical claims that matter, we have already reached a conclusion. We already know what our tribe of right-thinking people believe about Brexit, gun control, vaccinations, climate change, inequality or nationalisation — and so it is natural to interpret any statistical claim as either a banner to wave, or a threat to avoid.  

Curiosity can put us into a better frame of mind to engage with statistical surprises. If we treat them as mysteries to be resolved, we are more likely to spot statistical foul play, but we are also more open-minded when faced with rigorous new evidence. 

In research with Asheley Landrum, Katie Carpenter, Laura Helft and Kathleen Hall Jamieson, Dan Kahan has discovered that people who are intrinsically curious about science — they exist across the political spectrum — tend to be less polarised in their response to questions about politically sensitive topics. We need to treat surprises as a mystery rather than a threat.  

Isaac Asimov is thought to have said, “The most exciting phrase in science isn’t ‘Eureka!’, but ‘That’s funny…’” The quip points to an important truth: if we treat the open question as more interesting than the neat answer, we’re on the road to becoming wiser.  

In the end, my postcard has 50-ish words and six commandments. Simple enough, I hope, for someone who is willing to make an honest effort to evaluate — even briefly — the statistical claims that appear in front of them. That willingness, I fear, is what is most in question.  

“Hey, Bill, Bill, am I gonna check every statistic?” said Donald Trump, then presidential candidate, when challenged by Bill O’Reilly about a grotesque lie that he had retweeted about African-Americans and homicides. And Trump had a point — sort of. He should, of course, have got someone to check a statistic before lending his megaphone to a false and racist claim. We all know by now that he simply does not care. 

But Trump’s excuse will have struck a chord with many, even those who are aghast at his contempt for accuracy (and much else). He recognised that we are all human. We don’t check everything; we can’t. Even if we had all the technical expertise in the world, there is no way that we would have the time. 

My aim is more modest. I want to encourage us all to make the effort a little more often: to be open-minded rather than defensive; to ask simple questions about what things mean, where they come from and whether they would matter if they were true. And, above all, to show enough curiosity about the world to want to know the answers to some of these questions — not to win arguments, but because the world is a fascinating place. 

Creativity and curiosity: Do we make stuff up or find it out?

By Prof. Colin Lawson in The Independent

The world of music has much to contribute to debate around the nexus between discovery and invention. Igor Stravinsky memorably once wrote of his ballet The Rite of Spring; ‘I heard and I wrote what I heard. I am the vessel through which the Rite passed’. He felt that he had in effect ‘discovered’ rather than invented it. These days we’re all too eager to accept such an explanation. The Rite’s achievement seems indeed to be that it just exists, a gargantuan presence, arousing the same feelings of wonder as the most remarkable works of nature. However much one seeks to explain it, the Rite seems inexplicable. Yet it’s important to note that Stravinsky’s rationale for the Rite’s composition appeared in print almost half a century after its riotous première in May 1913. At the time of its gestation Stravinsky had described composing the Rite as ‘a long and difficult task’, a claim supported by the surviving sketchbooks. It’s not altogether unexpected that the Rite has also been remade by successive generations of performers. It wasn’t composed as a cornerstone of twentieth century music comprising a series of tableaux, but as a piece of theatre. Innovation and revolution go hand in hand with techniques in which Stravinsky was brought up and trained.

Our own desire to seek explanation, even of subject matter that is fundamentally ‘beyond text’, has become inflected by a cult of celebrity that was unknown in earlier times. Our vocabulary carries a new set of overtones, with words such as classical, serious, musical, genius and masterpiece that would have meant little at a time when music was more closely woven into the fabric of society. When we encounter exceptional achievement we rapidly reach for that vocabulary.

Important evidence for the relationship of creativity and curiosity is provided by the life and posthumous reception history of Mozart.  These days an over-exploited and over-exposed Mozart has almost come to represent western classical music itself. The great man is invoked to sell confectionery, cheese, spirits and tobacco. You can have a Mozart ski holiday or attend a ‘meet Amadeus’ event. Mozart’s credentials as a timeless genius were established immediately after his death. He was soon transformed from mere composer to inspired artist to meet the needs of the age that followed him. In the first biography just six years after his death Mozart was made to observe from his deathbed: ‘Now I must leave my Art just as I had freed myself from the slavery of fashion, had broken the bonds of speculators, and won the privilege of following my own feelings and composing freely and independently whatever my heart prompted.’ During Mozart’s recent 250^th anniversary, Nicholas Kenyon remarked that this apocryphal statement sums up everything the Romantics wanted a composer to be and Mozart was not. Whether or not Mozart would have understood the concept of ‘composing freely’, he wanted to be needed and appreciated and to make the most of performing opportunities; whilst he was conscious of the musical value of his compositions, there’s no evidence that he ever wrote for some far-distant future. Further recent research into Mozart’s compositional method has conclusively exposed as a myth the notion that Mozart carried all his music in his head, awaiting only space in his schedule to scribble it all down.

The usage of words such as ‘creative’ in connection with the production of musical works of art illustrates our tendency to mythologize. The idea of composers as creators or musical artists in a categorical sense is really a feature of the modern era; as Kenyon observes, Mozart doesn’t indicate anywhere that he regards himself as a genius or creator, whilst recognizing that he has genius, a superior talent for making music. In reality, Mozart’s pragmatism is evident in many facets of his professional life, since he worked within the conventions of his time, stretching them to their limits. It’s clear that Mozart’s principal focus was to address specific situations, such as commissions, concerts and dedications. At the same time he contrived to produce a stream of sublime music. But the situations and people directly influenced both his completed compositions and the many fragments that somehow never came to fruition. Perhaps in the case of both Stravinsky and Mozart, it’s the distinction between making stuff up and finding it out that is problematic.