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McKinsey and the benefits of an elite network

Duff McDonald’s The Firm is an enjoyable history of consulting firm McKinsey. One of the things I found most interesting was the strength and durability of the firm’s network, in spite of the fairly brutal approach the company has had to hiring and retention historically.

‘Even for graduates of Harvard, landing a job at McKinsey is hardly a lifetime appointment. Most young consultants spend just a few years at the firm before being tossed back out into the workforce. Only one in six hires stays at the company for five years or more. This merciless system, [is] widely referred to as “up-or-out’…’

‘[McKinsey]…began formally employing the policy in 1954, inaugurating a relentless performance review cycle. At least once a year, the firm’s consultants are subjected to an exhaustive review, wherein dozens of their colleagues are asked to comment on their progress and performance. The pressure doesn’t subside over time, either. In 1963 the firm extended the policy to principals—junior partners—for whom “up-or-out” was rechristened “grow-or-go.” It was later applied to directors, who had to “lead or leave.” Once they hit age sixty, partners are pointed toward the door, through which they are strongly encouraged to exit at the age of sixty-five.’

‘The average age of McKinsey’s professionals was thirty-two about fifty years ago; it’s still thirty-two today. You don’t hold the line on a number like that without constant churn, especially at the top.’

At first glance, while you might expect the above to at least produce a good number of individual performers within the business, you’d also you’d expect the culture described above to be one that people would be glad to leave (and badmouth once they’d escaped).

Instead McKinsey seems to have developed a culture which lets people go while still letting them feel a part of an elite club. McKinsey refers to its network of former employees as ‘alumni’ and, crucially, ex-McKinsey employees (for the most part) seem to retain this connection as well.

McDonald describes this phenomenon like this:

‘Like almost no other firm in existence, McKinsey becomes a part of its people’s self-image. Years after leaving the firm, ex-consultants still use “we” when referring to McKinsey, even in the present tense.’

The impact of this is seen in a number of ways. Firstly, McKinsey’s alumni often end up in plum positions in other firms – the kinds of positions that are able to retain high fee consultants like McKinsey.

‘More than seventy past and present CEOs of Fortune 500 companies are McKinsey alumni. A 2008 study by USA “Today calculated that the odds of a McKinsey employee becoming a CEO at a public company were the best in the world, at 1 in 690. The closest rival was Deloitte & Touche, at 1 in 2,150. McKinsey is certainty the most efficient producer of CEOs the world has ever seen. In 2011 more than 150 McKinsey alumni were running companies with more than $1bn in annual sales.’

Secondly, the sheer number of alumni gives McKinsey access to an important network of contacts:

‘It wasn’t until the turn of the century that the number of alumni became truly meaningful. By 2000, there were more than 19,000 living professional alumni of the Firm [later in the book a figure of 23,000 alumni is quoted in 2011]…Perhaps the only alumni network with more reach and lifelong relevance to its members is that of Harvard University. And there’s no small amount of overlap between the two.’

All of which begs the question – how has McKinsey been so successful at making ex-employees feel like ‘alumni’?

A good answer to this is provided in the form of a quote from Bill Matassoni who worked for McKinsey and then rivals Boston Consulting Group for five years but ‘still considers himself a McKinsey man above all else. “BCG asked me how come their alumni aren’t as happy as McKinsey’s. I told them it was simple, that when a guy left BCG they shat all over him and considered him a failure. When people leave McKinsey, they are counseled out and are proud of their time there.”’

McDonald expands on this: ‘There is no McKinsey boneyard, in other words; you’re still McKinsey, even after you’ve left. Even those who lose turf battles and feel forced to leave eventually come around to warm and fuzzy feelings again’.

The rewards for McKinsey have been enormous. Aside from the financial performance and growth of the firm, it continues to advise companies at the very highest level. In this way, McKinsey’s elite network creates a virtuous circle: they hire some of the best people, who leave and go to other firms where they hire McKinsey, so McKinsey consistently does some of the most interesting strategic work, so it can continue to hire some of the best people around who want to do the best jobs (within – and eventually, outside of – McKinsey), etc.

Not every firm can create an elite network. But there may be smaller networks in specific niches that are open to other professional services firms. More broadly, treating departing people well can create future opportunities and can advance a company’s reputation. The McKinsey example shows that even former employees can play a part in the continued success of a firm, and that creating a culture where leaving isn’t a kind of failure can be a positive step.

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Ray Dalio and the ‘as-it-happened’ approach to history

I’ve finally got around to reading Ray Dalio’s Principles. One of the things I found most interesting was his approach to learning about the history of market events (in order to better understand the dynamics of these events so he could respond to them better as an investor). Dalio describes the genesis of this approach as follows:

‘On Sunday August 15 1971, President Nixon went on television to announce to announce that the U.S. would renege on its promise to allow dollars to be turned into gold, which led the dollar to plummet…

Monday morning I walked into the floor of the exchange expecting pandemonium. There was pandemonium all right, but not the sort I expected. Instead of falling, the stock market jumped about 4 percent, a significant daily gain.

To try and understand what was happening, I spent the rest of that summer studying past currency devaluations. I learned that everything that was going on – the currency breaking its link to gold and devaluing, the stock market soaring in response – had happened before, and that logical cause-effect relationships made those developments inevitable. My failure to anticipate this, I realized, was due to my being surprised at something that hadn’t happened in my lifetime, though it had happened many times before’.

Despite this experience, Dalio goes on to describe how he made a series of further forecasting blunders, including a mistaken (and very public) call in the wake of the 1982 Mexican government debt default. In the wake of this, Dalio writes:

‘I again saw the value of studying history. What had happened, after all, was ‘another one of those’… I had failed to recognise the lessons of history’.

Later on, Dalio describes how:

‘That experience also drove me to learn a lot about debt crises and their effect of the markets…[Later, in 2007, as the financial crisis was drawing nearer] with the help of my teammates at Bridgewater, I took history books and old newspapers and went day by day through the Great Depression and the Weimar Republic, comparing what happened then with what was happening in the present. The exercise only confirmed my worst fears. It seemed inevitable to me that large numbers of individuals, companies, and banks were about to have serious debt problems’.

Dalio’s point is largely about repetitions in history, the long-run patterns that are particularly relevant to financial market dynamics. However, what interests me about this passage is the day-by-day approach to looking at history. It’s very easy when reading history casually to see events as forming an inevitable sequence, and to imagine that things could not have been any different. But this is not how those events seemed to the people living them. Like us at the present moment, those people had expectations, and hopes, and worries, but they didn’t know what the future held. Reading history ‘as it happened’ reminds us how uncertain things can be and how differently events can unfold compared to our expectations.

I recently read Orlando Figes’ Revolutionary Russia. In Figes’ description of the Bolshevik Revolution I was particularly struck by this section describing the events leading up to the seizure of the Winter Palace:

‘It is one of the ironies of the Bolshevik insurrection that hardly any of its leaders had wanted it to happen how and when it did. Until late in the evening of 24 October the majority of the Central Committee…had not envisaged the overthrow of the Provisional Government [that had forced the Tsar to abdicate earlier in the year]…

Lenin’s intervention was decisive. Disguised in a wig and cap with a bandage wrapped around his head, he left his hiding place in Petrograd and set off for the Bolsheviks’ headquarters…to force the start of the uprising. On his way across town…he was stopped by a government patrol, but they mistook him for a harmless drunk and let him pass. One can only ask how different history might have been had Lenin been arrested’.

The following day the Bolsheviks seized the Winter Palace. When the rival Menshevik and Socialist Revolutionary groups walked out of the Soviet Congress, the Bolsheviks took the initiative. As Figes writes:

‘Few people thought that the new regime could last. ‘Caliphs for an hour’ was the verdict of much of the press’.

You can read this a number of ways. It’s a story about near-misses: what if Lenin had been captured? It’s also a parable about the importance of decisive and visionary leadership. But there’s also a fascinating story about how a group of people in the thick of events completely missed what was about to happen. It may well be that they would eventually have been proven right, but I think there is an important reminder here about how unexpected events can be.

Even in our own recent experience, how many people would have scoffed at the idea that Donald Trump – or Emmanuel Macron – could be elected presidents of their respective countries? Or how many people – as oil prices marched upwards from a low of $27/barrel in late 2001 to a high of $160/barrel in mid-2008, with the background of a ‘peak oil’ narrative – would have predicted oil prices in the $50-$70/barrel range in 2018?*

Equally, though, as soon as these things do happen, we typically experience a brief period of surprise, followed by acceptance and rationalisation. In a relatively brief time, we come to view the events that have unfolded as – if not inevitable – then certainly not worthy of questioning too deeply. We take the backwards looking view of history.

We are living in a period of relative turbulence and uncertainty. By studying history on a day-by-day basis, we can remind ourselves of how far off our expectations can be if we miss the big picture, and how – time after time – many things that previously seemed unlikely came to pass.

*All prices WTI/NYMEX

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Reading is not learning

For a long time, I believed that just reading books was enough to help me learn the things I needed and wanted to learn. Despite all the evidence to the contrary, I thought that by reading a book I would remember the book. I would diligently read a book from beginning to end, then give myself a pat on the back, put the book on the shelf, and move onto the next one.

Over time, I realised that there was an advantage to reading books sequentially in order to try and give my (very fallible) memory a chance to turn the words flashing in front of my eyes a chance to settle in my brain as knowledge. So I’d read a group of books on similar or sequential topics one after the other.

This was a better method, but still didn’t respond to the bigger problem: that reading is not learning.

In a sense, this should be obvious. I knew at the time that I wasn’t remembering key details of books. And it’s not as though there is a school in the world that lets pupils just read. Learning is always about repetition and socialisation, as much as it is about information ‘input’.

But yet I assumed over time that simply reading, without a conscious attempt to retain the facts, would slowly aggregate into real learning. In this way I spent most of my twenties reading lots and learning far less than I could have with a bit more effort.

It wasn’t just reading where I made this mistake. I assumed that going to art exhibitions would teach me something about art. The experience was enjoyable, but I would have struggled to tell you anything substantial about the history of art, or the biographies of key artists. At work I failed to follow up on the names and terms being thrown around. I got used to the terminology, but it was pure luck most of the time that I didn’t get put on the spot about this detail or that fact.

In both reading and the rest of my life I assumed that merely by being around things that I would absorb information by some kind of osmosis.

Of course, that’s not how knowledge works. We know that we know something when we are able to explain it to others (and ourselves) without serious gaps. Knowledge is resistant to ‘what about…’ questions. If you can’t answer simple questions about a concept then you don’t understand it properly.

So reading is not learning because it only really helps information input, not information retention. Of course, if you are continually exposed to something then more of the concept will stick. But, equally, any concept will stick much faster and stronger if you make more proactive attempts to retain and recall the facts.

Why does this matter? I’ve noticed a lot of articles recently that make unreasonable claims for the learning power of reading, and put too much emphasis on the volume of reading. Warren Buffett’s ‘500 pages a day’ quote (he actually said 500 pages a week) is a prime example of this kind of thinking.

Don’t get me wrong, reading is an important part of a well-rounded life and is vital for learning. But it is necessary rather than sufficient. It is just as important to have a robust set of habits around note-taking, revising information, and thinking / questioning. And reading is not the only way to learn: lectures, documentaries, podcasts, etc. are just as important.

It’s also important to remember that reading can be dangerous in the sense that it can give you a false sense of knowledge based on a superficial awareness rather than a deep and considered understanding. I’ve written about the illusion of knowledge previously on this blog.

All of which is not to say that you shouldn’t read, just that you shouldn’t fool yourself into believing that you are retaining everything you read without extra effort.

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Ballistics testing in the LAPD: the problem with flawed systems

Jill Loevy’s Ghettoside is a brilliant exposition of the problem of Black-on-Black murder in South Central LA. The central concern of the book is trying to understand why Black men, only 6% of the US population, make up 40% of the nation’s murder victims.

The book skilfully weaves between big picture thinking on why murder epidemics happen, and the social dynamics of Black-on-Black murder, with the story of the murder of an LAPD officer’s son. I think it might be even better than David Simon’s classic Homicide: A Year on the Killing Streets about Baltimore homicide detectives (which inspired the TV shows Homicide: Life on the Streets, and The Wire).

Mid-way through Ghettoside the action briefly focusses on the work of the firearms analysis team at the LAPD which provides a great case study in organisational dysfunction that’s worth focussing on.

Prior to the investigation that forms the main focus of the book, the LAPD had adopted the National Integrated Ballistics Information database (NIBIN):

‘The NIBIN catalogued digitised images of bullets and cartridge casings from crime scenes and seized guns. The database could be searched by an algorithm. This allowed fast, cheap searches, matching ammunition used in crimes to individual weapons…[but]…the computer system was not as discerning as trained humans. It relied on simplified digital renderings of microscopic images produced through standardised procedures – a process that eliminated many telling nuances and contours’.

Loevy compares this to the lower tech system which has prevailed before:

‘Before, skilled technicians had taped Polaroid photos of bullets and cartridge casings to the wall and examined every microscopic dent and groove with the naked eye to match them to ammunition test-fired from the individual firearms. This low-tech method was not efficient but it yielded excellent results’.

The real problem with the NIBIN was that it had a blind spot – revolvers:

‘Revolver matches are more difficult than other types of firearm analysis…bullets are cylindrical and the grooves and scratches they bear after being fired wrap around a curved surface. In contrast, breech face markings on the flat part of a cartridge case are relatively easy for a computer to read. So while the NIBIN system was adept at matching casings to semiautomatic pistols, it had proven useless at matching bullets to revolvers’.

The result:

‘Although the LAPD and other agencies had dutifully entered test-fired bullets from hundreds of revolvers into its database for years, by the summer of 2007, the system had never successfully matched a bullet used in an LA crime to a revolver’.

This is put into context by the following: ‘about one third of the LAPD’s seized firearms were revolvers’.

Loevy describes how the ballistics team, recognising the inadequacies of the software, create a shadow-system to get this part of their job done.

‘So Hudson [who ran the firearm analysis lab] made a decision. They would bypass NIBIN. Her workers would continue submitting images to the database as required. But they also would quietly assemble their own duplicate database of test-fire exemplars from seized revolvers. This secret trove would be analysed the old fashioned way with the human eye’.

The result of this painstaking work is ultimately a big break in the case at the heart of the book, leading to the identification of the murder weapon.

But this whole sequence begs the question: why are seasoned investigators having to secretly recreate an old system to bypass the failings of a newer system? (And, as this article makes clear, this is far from just an LAPD issue.)

Your knee-jerk response to this probably depends on your politics. Either this is an example of public sector mismanagement or an example of woeful underinvestment in essential services. Probably, it’s a little of both.

But this passage is more interesting as an example of what happens when software solves most, but not all, of a given problem. In particular, it shows how the existence of ‘edge cases’, instances where slightly or very different rules hold, can create big flaws in an otherwise operational system. In the case of NIBIN, a system worked for two-thirds of cases but was disastrous for the other third. It also shows how design ‘blind-spots’ – when a piece of software is designed without fully understanding the technical challenges involved – can have far-reaching consequences.

Clearly, though, this is not just a software problem: it’s also an organisational one. Big software purchases are often major decisions for organisations, creating a reluctance to admit when something has gone wrong. The decision may ultimately have been predicated on a cost-saving that could only be realised if all analyses were run through the NIBIN system. This created a dynamic where the two-thirds of the system that worked couldn’t be used in isolation from the third of the system that didn’t work. In an organisational context where such decisions can’t be challenged by the people using the system, the result is dual processes, informal ways of working and wasted time and effort observing the formal requirements.

This suggests a few important things about introducing new systems, all of which are both obvious and clearly very easy to overlook / get wrong:

(1) Talk to the people who use the system / do the job at the moment. Understand what the problems are and what they need.

(2) Make a real attempt to understand how your brilliant big picture idea could break when it encounters the messiness of reality. Understand how edge cases differ in crucial ways to the day-to-day of regular use.

(3) Introduce the system as a work in progress, not a done deal. Encourage feedback. Take bad feedback well, not defensively. Use it to make the changes that lead to a system that works.

The alternative is unhappy users, under-utilisation of a new system / continuing to use old and inefficient processes, and – most importantly – a failure to improve your system to be what it needs to be.

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The Three Types of Time: Investment, Maintenance, Consumption

Determine never to be idle. No person will have occasion to complain of the want of time who never loses any. It is wonderful how much can be done if we are always doing.

Thomas Jefferson

 

It’s not enough to be busy, so are the ants. The question is, what are we busy about?

Henry David Thoreau

 

It is a cliché that time is the most precious thing that we have. There are umpteen articles, books and TED talks about how to use it better. What makes it difficult is knowing exactly how to do that.

A framework that I find helpful for its simplicity is thinking about the time I spend on different tasks as falling into one of three categories:(

(1) Investment: things you do which have a long-term benefit and help you meet your long-term goals. Learning a new skill is investment. Networking and meeting interesting people is investment. Spending time doing things with your loved ones is an investment.

(2) Maintenance: things you do primarily to avoid something bad happening. Paying your bills, sorting out your insurance, washing the car, doing the laundry, filing your documents in the right way, etc. We can usually put maintenance off for a while, but after a certain point not attending to the necessities of life tends to catch up on you.

(3) Consumption: Things you do which neither have a long-term benefit, or help you avoid something bad. This is usually something enjoyable, like watching TV or a sports event. But it can also be compulsive activities or wasted time – using social media, spending time with people you don’t like or doing things you don’t like.

Of course, nothing is quite that simple: reading a good novel, or watching a great TV show can be enriching. With the wrong mindset, many important tasks that are investments in our long-term health and happiness – like exercise, time for reflection, sleep etc. – can become maintenance tasks.

Not all investment opportunities are the same. Whether you choose to learn Mandarin or mathematics at a particular point in time should depend very much on what skills you need and what this knowledge will allow you to do.

And investment always comes with an opportunity cost. Time spent with your friends and family is time not spent learning new skills – and vice versa.

Nonetheless, these three categories help us think about how we use our time and whether we are building for the future. It suggests a few things are important to get the most out of your time:

  • Minimise consumption so you can maximise time spent on focussed ‘investment’ tasks. As a general rule you want to spend as much time building for the future as you can, and you want to focus this effort where the long-term rewards are greatest (hint: they will be where your efforts are compounded over time).

The easiest way to do this is to cut out wasted time. And the easiest way to identify time-wasting tasks is to ask yourself: what do I achieve in the long-term when I do X? For many activities the answer is ‘nothing’! These are things that you should cut out and turn into investment time as much as possible.

In doing this, it’s important to distinguish between genuine time wasting, and things which have a long-term payoff. Sometimes the incremental benefits of what we do are hard to perceive, but there is a long-term value. This is often true of learning, so it’s important to think about the long-term, rather than the short-term benefit.

  • ‘Outsource’ and ‘streamline’ as much maintenance as you can. Life has a way of piling up things to do. And generally the more diligent we are at doing things, the easier it is to find additional tasks. Maintenance tasks are often boring, but a bigger problem is that because they give us a sense of achievement we can prioritise them over investment that is more difficult or has an uncertain payoff.

When I go to wash the dishes I know: (1) I can do it, (2) what the outcome will be and (3) that I will have ticked a box. And it’s important. But if we only ever do maintenance tasks we simply get good at ticking boxes without achieving any of the things that we think are most importance.

So there are two things you need to do to reduce maintenance. The first is simply to find someone or something else to do the task. You might be able to find a way to reduce the time you take per task by investing in the right equipment (e.g. a dishwasher!). The second thing is to outsource maintenance. For example, can you afford to hire a cleaner? Can you delegate better to more junior colleagues?

Outsourcing isn’t always possible, so you also need to think about whether a particular task can be streamlined – i.e. re-designed so that there is less time spent on all or part of it. This might be through saving up a particular task and doing a lot in one go (this works better for some things than others). The other way to use time better is by doing something of greater value while you do your main task – a basic example is listening to a podcast while you do the dishes.

It’s also worth asking whether you need to do a particular task at all – as it’s very easy to get into the habit of busywork. If you’re able to, try not doing something for a while and see what happens.

Turn ‘maintenance’ into ‘investment’ as much as possible. Our perceptions play a big role in what category a task falls into. There are lots of maintenance tasks which can be a chore or a pleasure depending on how you choose to look at them: exercising, cooking healthy food, looking after the kids. All of these things have a long-term benefit which can be lost if we don’t approach them with a positive perspective.

  • Choose good ‘consumption’ over time-wasting. There is a difference between experiences consumption that is meaningful and important, and time-wasting, which often leaves us feeling worse than when we started. By all means, read that good novel, watch that well-reviewed film, and go to the show you’ve been meaning to see.

You can use this framework across your life as a whole or in different parts of it – e.g. at work. I’ve found this approach particularly helpful to think about what work tasks I choose to prioritise and how I manage my time.

The point of this framework is not that it gives all of the answers, but it provides a helpful way to think about the things you are doing, and decide which things are the most valuable. You won’t ever get rid of all maintenance tasks, but if you aren’t dedicating enough time to investment, it is very difficult to make meaningful positive change over time.

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Familiarity and the Illusion of Knowledge

The greatest enemy of knowledge is not ignorance; it is the illusion of knowledge.

Daniel J. Boorstin

 

Estimates of how much information the average person is exposed to on a given day vary, but it’s always a big number, and there’s little doubt that the amount of information the average person ‘consumes’ daily has risen over the past few decades as technology has led to more platforms and channels for consuming it.

One of the biggest problems being exposed to lots of information – either through the media, social media, or just through reading a lot – is that we get used to hearing names and labels: of people, places, policies, etc. To put it another way, we develop familiarity with these names and labels.

Quite a lot of the time this familiarity is skin-deep. We can recite names and labels easily, and we might know whether something is ‘broadly good’ or ‘broadly bad’, but we would struggle to explain what a thing is in any detail, why it’s important (or unimportant) in objective terms, and how the current situation has come to be.

Of course, nobody can know everything. It’s unrealistic to expect everybody to have a detailed understanding of politics, economics, business trends, the physical sciences, arts and culture, etc.

However, the problem with familiarity is that we so often confuse it with actual knowledge. This happens simply and naturally when we are not alert and questioning. We read or hear something, make a guess about what it means based on the context or on a superficial explanation, and then feel as though we know enough about it to make sense of the rest of the information we are presented with. Later on, we hear the same term again, and our illusion of knowledge is reinforced. And so on.

Part of the wisdom of Boorstin’s quote above is that, in a sense, ignorance – without an accompanying sense of knowledge – can be ‘fixed’ through good teaching. But the illusion of knowledge – familiarity – can be much more pernicious. The impacts of this play out both at a societal level, and individually.

This article by Elizabeth Kolbert in the New Yorker (which cites, amongst others: The Knowledge Illusion: Why We Never Think Alone, by Steven Sloman and Philip Fernbach) puts it well:

In a study conducted at Yale, graduate students were asked to rate their understanding of everyday devices, including toilets, zips, and cylinder locks. They were then asked to write detailed, step-by-step explanations of how the devices work, and to rate their understanding again. Apparently, the effort revealed to the students their own ignorance, because their self-assessments dropped. (Toilets, it turns out, are more complicated than they appear.)

… It’s one thing for me to flush a toilet without knowing how it operates, and another for me to favour (or oppose) an immigration ban without knowing what I’m talking about. Sloman and Fernbach cite a survey conducted in 2014, not long after Russia annexed the Ukrainian territory of Crimea. Respondents were asked how they thought the U.S. should react, and also whether they could identify Ukraine on a map. The farther off base they were about the geography, the more likely they were to favour military intervention. (Respondents were so unsure of Ukraine’s location that the median guess was wrong by eighteen hundred miles, roughly the distance from Kiev to Madrid.)

How do we address this? First, it’s important to be prepared to really re-evaluate what you actually know. Humility – at the very least in the privacy of our own minds – is essential to learning.

Secondly, we have to learn to spot things that we don’t really know. Ask yourself: ‘what does this mean?’ If you can’t answer the question without using the term itself then you almost certainly don’t know enough.

Once you’ve learned ‘what?’ you need to start thinking about ‘why?’ – as in ‘why is this important?’ or ‘why did this happen?’. ‘Why’ questions are tougher, but even knowing how much you don’t understand is instructive and will help prevent you from making poor judgement calls based on familiarity but not understanding of a concept or situation.

In the example given by Kolbert, simply attempting to write down how a toilet or a zip works was enough to help people re-evaluate how much they knew. Kolbert also gives an example of how this works on more far reaching questions:

In a study conducted in 2012, they asked people for their stance on questions like: Should there be a single-payer health-care system? Or merit-based pay for teachers? Participants were asked to rate their positions depending on how strongly they agreed or disagreed with the proposals. Next, they were instructed to explain, in as much detail as they could, the impacts of implementing each one. Most people at this point ran into trouble. Asked once again to rate their views, they ratcheted down the intensity, so that they either agreed or disagreed less vehemently.

So we must be prepared to question the labels we are using and ask ourselves how much we really understand them. Doing so will make us more aware of how little we actually know and understand, encourage us to fill in the most important gaps, and make it less likely that we will make important mistakes due to false perceptions about our own knowledge.

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Master of reinvention: Hokusai

‘From the age of six, I had a passion for copying the form of things and since the age of fifty I have published many drawings, yet of all, I drew by my seventieth year there is nothing worth taking into account. At seventy-three years I partly understood the structure of animals, birds, insects and fishes, and the life of grasses and plants. And so, at eighty-six I shall progress further; at ninety I shall even further penetrate their secret meaning, and by one hundred I shall perhaps truly have reached the level of the marvellous and divine. When I am one hundred and ten, each dot, each line will possess a life of its own.’

Hokusai

 

If you’re stuck in a rut and worried your best days are over you could do a lot worse than consider the life and works of Hokusai, the Japanese artist famed for his iconic 1836 print The Great Wave of Kanagawa.

Hokusai was an artist during the Edo period, producing print-designs in the ukiyo-e style, a style which typically celebrated the transience of life and Epicurean pleasures. In terms of societal status, Hokusai was more of a craftsman than an artist as we would think about the term today.

Hokusai had a relatively successful career. But a series of financial disasters caused by spendthrift members of his family forced him to come out of retirement in his late 60s. Where most people would have been broken by this series of events, Hokusai instead went from strength to strength as an artist.

In the early 1830s – i.e. when Hokusai would have been in his early 70s – he created his Thirty-Six Views of Mount Fuji series (actually consisting of 46 prints) which contains the famous Great Wave print. The postscript to the series is the quote above. Despite his mastery of his craft, Hokusai insists that nearly all his work up to that point is not worth consideration, and that he is only just beginning to be able to understand the animals, plants and birds he is drawing. At the same time, he shows extraordinary optimism that he can go much further by the time he is ‘one hundred and ten’.

In part, Hokusai was able to do this through reinvention. Whilst it was common for Japanese artists of the time to use different names, Hokusai took this to an extreme, using over 30 names during his lifetime, more than any other major artist. His art as well, incorporated some important innovations, such as the use of Prussian Blue to create bold effects, use of western elements of perspective, and adopting a broader range of themes – particularly landscapes.

Hokusai kept creating right up until the end. In 1839 a fire destroyed his studio and home. Again, rather than let this setback end his career he kept on working and trying to improve at his craft until his death at the age of 88. His incredible work ethic meant that he created approximately 30,000 pictures and drawings over his lifetime.

For me, Hokusai is an inspiration and an example that it really is never too late. It can be easy to think that if we haven’t achieved something by the time we’re a particular age that we’ve failed. But this kind of thinking is reductive and damaging. Hokusai shows us that we can adapt to adversity and go further than we have gone before.

Hokusai also shows us the true spirit of learning. Real learning comes from being humble, and viewing our achievements as merely a stepping stone to greater things. This doesn’t mean damaging perfectionism – where we are never happy with the things we’ve achieved – but rather having a healthy sense of perspective and not resting on our laurels.

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Living a ‘lucid life’

Lucid dreaming is dreaming in which a person is aware that they are in a dream. With this awareness, a person can choose to do whatever they want to do in the dream, unconstrained by the rules that govern normal life.

In Richard Linklater’s 2001 film about lucid dreaming, Waking Life, one character offers a fairly incisive comment about why more people don’t lucid dream given how enjoyable it can be:

The trick is, you’ve got to realise that you’re dreaming in the first place. You’ve got to be able to recognise it. You’ve got to be able to ask yourself, “Hey man, is this a dream?” See, most people never ask themselves that when they’re awake, or especially when they’re asleep. Seems like everyone’s sleep-walking through their waking state, or wake-walking through their dreams. Either way, they’re not going to get much out of it.

In lucid dreaming, a key technique to attain lucidity is the ‘reality-check’. As described above, you need to get used to asking yourself: ‘Am I awake? Is this real?’. You can then check this by seeing if you can levitate themselves or objects. If you can, that’s usually a pretty good indication that you’re dreaming! Another good question that you can ask yourself is ‘how did I get here?’ If you can’t answer that question or it doesn’t make sense that’s usually an indication that you’re dreaming.

The point of the reality check is that when we’re dreaming it often feels real. That’s why nightmares are frightening – because we think they’re real and we get caught up in what’s happening and don’t stop to ask ourselves basic questions.

In the same way, when we’re distracted – by social media, or emails that aren’t really that important, or any of the myriad distractions of modern life – we can often feel that the thing we’re doing is necessary or important when it really isn’t. We can lose sight of what we are trying to achieve and get swept along by what’s happening around us. In this way it’s very easy to sleepwalk into automatic behaviours that waste time

So I think there’s a strong case for pursuing a lucid life – one in which we aim to be aware of what we are doing at any given point in time, rather than just letting life happen to us. Just as in lucid dreaming, the reality check is a pretty useful tool for thinking about things – questions like:

– What am I trying to achieve?

– What am I doing right now? (useful when you’re aimlessly flicking through a twitter timeline)

– Will doing this help me achieve what I want to achieve?

– Will I be glad I did it a year from now? (useful for sorting the wheat from the chaff in terms of tasks)*

Of course, reflection is essential for the long-term as well – setting life goals and working out what you really believe is important. But cultivating good day-to-day habits and not letting yourself sleepwalk through the day is essential too.

 

Bonus mini-post! On Lucid Dreaming

The above isn’t really about lucid dreaming but references it a lot. There’s quite a lot ‘out there’ about lucid dreaming but it’s still a relatively little heard of phenomenon, probably because no-one has quite worked out how to make money off of it (yet).

Chances are that you’ve probably experienced a lucid dream by accident. It tends to happen when you’ve woken up, been awake for a while then fall back to sleep.

If you want to consciously try and have more lucid dreams then two basic beginner tips:

– Start doing reality checks. Set an alarm if needed to remind yourself. If you get into the habit when you’re awake it will prompt you to do so when you’re dreaming.

-Keep a dream journal – i.e. a note of your dreams. Try and make notes as soon as you wake up. This is the most time-consuming bit, but also fascinating because you will realise exactly how much you dream (that you forget when you’re not actively motivated to try and remember it). Keeping a journal is essential to helping you become more aware of your dreams in the first place.

Some people take this further by setting alarms to wake themselves up (e.g. at 4am) and then falling back to sleep (to take advantage of the natural tendency for this to happen after a sleep interruption), but the above two points will take you a long way. Happy dreaming!

 

*See also this post on setting life goals / now vs tomorrow

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Who Governs Britain? Brexit and the role of ‘partisans’

Anthony King’s ‘Who Governs Britain?’ is a great guide to the UK political system, its component parts, and how each part of the system exerts influence on the governance of the country as a whole. It’s a short and very readable book rather than a textbook treatment of the subject.

King takes a chapter on each bit of the system but presents this in an unconventional way: the book starts with a chapter on ‘foreigners’ (i.e. the extent to which the UK’s governance is affected by international treaty obligations) and only gets around to talking about prime ministers in Chapter 11.

Explaining this choice, King writes:

The order in which the chapters are set out will probably strike some readers as idiosyncratic. They will be right. It is. The deliberate aim of the exercise…is to disturb the order in which these topics are customarily approached, an order [i.e. the traditional order] that in a curious way encourages readers to think along conventional lines when new times perhaps require a less conventional approach.

It’s an approach that works and the book is highly accessible and highly recommended. But one thing that struck me is that the book – published in 2015 – takes as its first two chapters the subjects of ‘foreigners’ and ‘partisans’. In many ways, the whole saga of Brexit reflects a conflict between these two influences on British politics – namely the EU, and the Eurosceptics rank-and-file party members in the Conservative Party.

On the EU, King has the following to say:

The most intimate and intense involvement of foreigners in the making of governmental decisions affecting British citizens dates from 1 January 1973, the day on which the UK joined what is now the European Union…

The Maastricht Treaty, finally ratified in 1993…[saw] the remit of the EU’s various institutions… extended to include influence upon, although not total control over, transport, education, consumer protection, public-health policy, policing, and immigration and asylum…

The impact of all this on the way in which Britain is governed – and by whom – has been enormous. For many purposes, though by no means all, heads of government and other ministers from other EU countries, and members of the European Commission, are now fully integrated into Britain’s governing institutions.

In summing up the impact of this – and other UK treaty obligations, the impact of international financial markets and the power of multinational firms to pick and choose their tax jurisdictions – King concludes:

The United Kingdom long ago lost most of its capacity to act independently*. Moreover, there is no way it can regain it.

One group of people that would agree with most of the first sentence above, and disagree vigorously with the second sentence are the ‘partisans’ / members of the Conservative Party. King notes that membership of the Conservative Party peaked at 2.8m during the early 1950s, and has since declined to (a self-reported number of) 150,000 as of the mid-2010s. King states that as of the mid-2010s ‘the Conservative Party was no longer a mass party: it had become a boutique party for the most ardent of the faithful’ (he also notes that Labour has not fared much better).

No one knows exactly what proportion of party members are also party activists, but the proportion is probably less than one in four and is unlikely to be as high as one in three… [this] would mean that during the mid-2010s Conservative activists across the country numbered, at most, in the order of 50,000.

The relatively small number of hardcore party members creates a dynamic where:

Party leaders, MPs, and candidates desperately need their party supporters to be a visible and audible presence in their constituencies… Party leaders also badly need their activists’ moral support. It is debilitating for an MP or candidate to have to deal for months on end with vocal and disgruntled members of their local party; and party leaders, even those at or near the top, go out of their way not to alienate the people they meet at party gatherings and address at party conferences… Sometimes they even feel the need to sacrifice their own judgement to [those of their members].

King continues:

The balance of power that exists in that relationship between leaders and followers has, if anything, shifted in party activists favour just as the latter’s total numbers on the ground have diminished. The main parties have become more internally democratic. As they have done so, the power of party members and activists within each of them has increased… It is party members who choose parliamentary candidates, and nowadays it is they who have the power to elect their party’s leader… and they are also able to influence their party’s rhetorical tone, its broad policy direction and sometimes even its stance on specific issues.

…What is clear is that on some significant issues large numbers of active Conservative party members hold more radical views that most of their party’s leaders and at the same time feel more strongly about them.

…In more recent years… [such issues have included]…drastically reducing net immigration and, of course, not only resisting further European integration but calling for Britain to withdraw from the EU altogether.

Clearly, the views of Tory members are not the only factor in the Brexit vote, but it is striking the extent to which their views have steered the course of British policy on the EU. Only 43% of Conservative MPs backed Brexit, compared to 58% of Conservative voters. The best data I can find on Conservative members is this – which doesn’t track referendum voting patterns but suggests that (as of early 2018) fewer than 10% of Conservative members surveyed back remaining in either the single market or customs union, which seems to suggest that Conservative members were significantly more pro-Leave than even typical Conservative voters.

The role of ‘partisans’ in the Brexit saga also plays out on the Labour side as well as the Conservative side. The role of Labour members in electing the relatively left-wing Jeremy Corbyn has played an important part in the fairly tame response to the weakness of the current Conservative position (with a wafer-thin majority in parliament thanks to an informal partnership with the DUP). The desire of Labour members to choose a more socialist leader has clearly limited the party’s mainstream electoral appeal, though it’s possible Corbyn could still end up as PM given the inherent instability / uncertainty in the political system at the moment.

Stepping back from Brexit for a second, all of this illustrates the outsized influence that small groups can have on large complex systems. In a recent article I talked about the role of the Cluniac monasteries – i.e. relatively small groups of people – in promoting Church reform in medieval Europe. When small groups do something we perceive as wrong, we bemoan ‘special interests’ and ‘extremists’, and when this works in our favour or a way we approve of we talk about ‘visionaries’.

Either way, an important dynamic in complex societies, systems, and organisations is the way that control over key bottlenecks (in this case, the role in making and unmaking Tory politicians), can create very significant change. Indeed, the EU itself is largely an elite project (albeit with substantial popular support).

King’s book talks about other points of pressure on the British political system, including the media, special interest groups and the judiciary, but if anything each of these chapters reinforces the point that small groups – often in conflict with each other – play a key role in British politics.

 

* It’s worth noting that King also recognises that most states are in a similar position, and even the US and China are far from constrained in their ability to act independently.

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Tom Gash on the weaknesses of large scale econometric studies

Criminal by Tom Gash is a great book about why people commit crime. But it also has one of the most accessible and straightforward analyses of the weaknesses of econometric modelling that I’ve seen.

Picking on two famous analysis of the causes of crime – Steven Levitt’s (of Freakonomics fame) thesis that increases in abortion in the 1970s led to reduced crime in the 1990s, and John Lott’s claim (set out in More Guns, Less Crime) that part of the crime decline was due to new laws in certain US states allowing for people to carry firearms – Gash states:

Despite vast differences in their conclusions, these studies have at least two things in common: their ability to capture headlines and their method of analysis. This method – generally known as econometrics – sounds intimidating. It results in papers filled with equations, data tables and the results of various ‘tests’ run through powerful computer programs which can make even the mathematically minded feel insecure.

Of course, it’s tempting to defer to the wisdom of those clever enough to wield this startling array of tools…but the basics of econometrics are reasonably easy to grasp. Essentially, econometric studies look at a number of different trends or events and try to understand the relationships between them. To create models explaining shifts in crime, our dispassionate scientists must therefore first identify the main factors that might influence crime. Then they must find the data that measures these factors. Then they must discover how all these factors interrelate… Results will tell researchers whether there is a relationship between crime and the factors identified, how ‘confident’ researchers can be of the relationship, and the likely magnitude of the relationship’.

While Gash is talking about ‘crime’ in the above, you can subject pretty much any phenomenon that has been subjected to econometric research – from other social trends, to the economy, to political dynamics, etc. Gash further explains:

This is the process that allows authors like Levitt…to say ‘legalised abortion appears to account for as much as 50 percent of the recent drop in crime’. Such methods and the arguments that rest on them appear superficially plausible. But a deeper examination soon reveals that what looks like dispassionate science is in fact messy art…econometric models are typically underpinned by myriad assumptions.

The choices of which variables to include in a model – and, by implication, which to exclude – and how these variables are measured play a crucial role in determining the findings. On top of this, findings can be skewed by other data selection issues like the geography (some countries / cities and not others) or the timescales over which data is examined. Gash explains that:

…[the variables used]…vary vastly between different models. Levitt’s include abortion laws; most don’t. John Lott’s include gun laws; most don’t. Levitt, Lott and Marvell [Thomas Marvell, who has argued that crime in the US would be many times higher without the historic increase in the prison population] do not regularly look at factors such as drug consumption, social values, or marriage – which other studies do examine.

Almost all studies include variables such as police numbers – but virtually none bother to examine the number of people working in private security companies, even though the US, for example, has significantly more security guards than police officers…There are choices as to whether to use police force figures for recorded crime (which are more plentiful) or data from victim surveys (which are more meaningful)…As importantly, I have never seen an econometric model that includes ‘the number of well-positioned street lights’ or ‘good transport out of urban centres at night’ as variables – even though we know from small-scale studies that these can have an impact on crime rates.

The process of deciding which data should be used also requires a high degree of precision…Many people think that inequality matters in assessing crime rates but… which types of inequality… differences between incomes, differences in wealth…or how unequal people feel their society is [?]…

Choices must be made – and they are choices that are constrained by the limits of our ability to measure complex phenomena in our complex world.

Even after data has been collected, there are many judgements to be made…One major problem is that having too many variables in a model creates mathematical complexity and confusion – so modellers go through a process to narrow down to the variables that appear to ‘matter’. As factors that show an apparently ‘weak’ relationship… are excluded, the mathematical relationships between… those factors that remain grow stronger.

In other words, the myriad small choices that econometricians make – due to their own research interests, the availability (or lack thereof) of suitable data, and how they decide to ‘tidy up’ the model towards the end of the research project (i.e. removing ‘messy’ variables that create unnecessary complexity) – all have a big influence on the findings of the work. Tom Gash puts it like this:

In the process of creating a model, it’s often surprisingly easy to persuade yourself that this vast array of omissions and assumptions aren’t that important and that the conclusions you come to are still highly meaningful. As an old economics joke puts it: ‘econometricians, like artists, tend to fall in love with their models’.

These are not trivial issues. Some of the most significant and highly publicised works of the last few years have been subsequently taken to task over their use of figures and modelling – including Thomas Piketty’s Capital in the Twenty-First Century, and Kate Pickett and Richard Wilkinson’s The Spirit Level. Equally, the complexity of these models means it can take some time to recognise and correct errors (Gash notes that Levitt had to downgrade his estimate of the impact of abortion on crime when a coding error was discovered).

All of this leaves the casual reader in a bit of a pickle. Without enough time or expertise to properly vet research, what should we make of studies that use these methods?

Gash’s view is:

The results of such analysis remain opinions and not pure facts…we simply cannot rely on these methods alone to draw robust conclusions, and we must certainly not accept their conclusions at face value.

In fairness to Gash, he provides a nuanced take on the above, stating that in smaller scale studies these methods are more suitable and produce better results. He also notes that his own conjectures about the causes of crime ‘are no more provable than the theories proposed by our bold econometricians’.

At a time when these kinds of studies are increasingly supporting policy conclusions, recognising their (serious) flaws is important, as is having the humility to recognise the weakness in one’s own ideas.