Movies such as Good Will Hunting tell beautiful stories about young people able to instantly master difficult topics, without any effort on their part.

That performance is unrelated to effort is an appealing belief. Whether you perform well or poorly is not your fault. Some go further and conclude that success and skill levels are primarily about genetics. That is an even more convenient observation: the quality of your parenting or education becomes irrelevant. If kids raised in the ghetto do poorly, it is because they inherited the genes of their parents! I personally believe that poor kids tend to do poorly in school primarily because they work less at it (e.g., kids from the ghetto will tend to pass on their homework assignments for various reasons).

A recent study by Macnamara et al. suggests that practice explained less than 1% of the variance in performance within professions, and generally less than 25% of the variance in other activities.

It is one of several similar studies attempting to debunk the claim popularized by Gladwell that expert performance requires 10,000 hours of deliberate training.

Let us get one source of objection out of the way: merely practicing is insufficient to reach world-expert levels of performance. You have to practice the right way, you have to put in the mental effort, and you have to have the basic dispositions. (I can never be a star basketball player.) You also need to live in the right context. Meeting the right people at the right time can have a determining effect on your performance.

But it is easy to underestimate the value of hard work and motivation. We all know that Kenyan and Ethiopian make superb long-distance runners. Right? This is all about genetics, right? Actually, though their body type predispose them to good performance, factors like high motivation and much training in the right conditions are likely much more important than any one specific gene.

Time and time again, I have heard people claim that mathematics and abstract thinking was just beyond them. I also believe these people when they point out that they have put many hours of effort… However, in my experience, most students do not know how to study properly. You should never, ever, cram the night before an exam. You should not do your homework in one pass: you should do it once, set it aside, and then revise it. You absolutely need to work hard at learning the material, forget it for a time, and then work at it again. That is how you retain the material on the long run. You also need to have multiple references, repeatedly train on many problems and so on.

I believe that poor study habits probably explain much of the cultural differences in school results. Some cultures seem to do a lot more to show their kids how to be intellectually efficient.

I also believe that most people overestimate the amount of time and effort they put on skills they do not yet master. For example, whenever I face someone who failed to master the basics of programming, they are typically at a loss to describe the work they did before giving up. Have they been practicing programming problems every few days for months? Or did they just try for a few weeks before giving up? The latter appears much more likely as they are not able to document how they spent hundreds of hours. Where is all the software that they wrote?

Luck is certainly required to reach the highest spheres, but without practice and hard work, top level performance is unlikely. Some simple observations should convince you:

  • There are few people who make world-class contributions at once… there are few polymaths. It is virtually impossible for someone to become a world expert several distinct activities. This indicates that much effort is required for world-class performance in any one activity. This is in contrast with a movie like Good Will Hunting where the main character appears to have effortlessly acquired top-level skills in history, economics, mathematics.

    A superb scientist like von Neumann was able to make lasting contributions in several fields, but this tells us more about his strategies than the breadth of his knowledge:

    Von Neumann was not satisfied with seeing things quickly and clearly; he also worked very hard. His wife said “he had always done his writing at home during the night or at dawn. His capacity for work was practically unlimited.” In addition to his work at home, he worked hard at his office. He arrived early, he stayed late, and he never wasted any time. (…) He wasn’t afraid of anything. He knew a lot of mathematics, but there were also gaps in his knowledge, most notably number theory and algebraic toplogy. Once when he saw some of us at a blackboard staring at a rectangle that had arrows marked on each of its sides, he wanted to know that what was. “Oh just the torus, you know – the usual identification convention.” No, he didn’t know. The subject is elementary, but some of it just never crossed his path, and even though most graduate students knew about it, he didn’t. (Halmos, 1973)

  • In the arts and sciences, world experts are consistently in their 30s and 40s, or older. This suggests that about 10 years of hard work are needed to reach world-expert levels of performance. There are certainly exceptions. Einstein and Galois were in their 20s when they did their best work. However, these exceptions are very uncommon. And even Einstein, despite being probably the smartest scientist of his century, only got his PhD at 26. We know little about Galois except that he was passionate, even obsessive, about Mathematics as a teenager and he was homeschooled.
  • Even the very best improve their skills only gradually. Musicians or athletes do not suddenly become measurably better from one performance to the other. We see them improve over months. This suggests that they need to train and practice.

    When you search in the past of people who burst on the scene, you often find that they have been training for years. In interviews with young mathematical prodigies, you typically find that they have been teaching themselves mathematics with a passion for many years.

A common counterpoint is to cite studies on identical twins showing that twins raised apart exhibit striking similarities in terms of skills. If you are doing well in school, and you have an identical twin raised apart, he is probably doing well in school. This would tend to show that skills are genetically determined. There are two key weaknesses to this point. Firstly, separated twins tend to live in similar (solidly middle class) homes. Is it any wonder that people who are genetically identical and live in similar environment end up with similar non-extraordinary abilities? Secondly, we have virtually no reported case of twins raised apart reaching world-class levels. It would be fascinating if twins, raised apart, simultaneously and independently reached Einstein-level abilities… Unfortunately, we have no such evidence.

As far as we know, if you are a world-class surgeon or programmer, you have had to work hard for many years.

Lately, the top salaries for computer science graduates have been increasing. Companies like Google are willing to pay what it takes to get their hands on the best programmers (which is well over 200k$ a year). I expect these salaries to keep on climbing for the next 20 years.

Simply put, a very good software engineer can generate a lot of value. One engineer working for Facebook can improve the life of millions of people significantly by working for a few months on a new feature. The counterpart is that many routine software jobs are easily outsourced and automated.

At least where I live, almost all high school students have access to some computer science education in high school. And that is generally a good thing, in the same way it is a good idea to learn how to cook or learn about the history of your civilization. A few courses here and there is enough for most however.

The Mayor of Chicago wants to Computer Science for all students, starting in first grade. He clearly imagines a future where programmers are everywhere. However, is the software industry a good bet for most kids?

I do not think so. Being a highly productive programmer is hard work. You need to constantly retrain yourself. You have to maintain the highest levels of professionalism. It is not for everyone.

Many of the great programmers I know worked long hours before they were good. They spent their week-ends reading technical documentation. They spent days discussing the finer point of memory allocation and alignment on posting boards.

Moreover, we have evidence that unless you are one of the best programmer in your class, your job prospects in the software industry are probably mediocre:

In computer and information science and in engineering, U.S. colleges graduate 50 percent more students than are hired into those fields each year; of the computer science graduates not entering the IT workforce, 32 percent say it is because IT jobs are unavailable, and 53 percent say they found better job opportunities outside of IT occupations. These responses suggest that the supply of graduates is substantially larger than the demand for them in industry. (EPI Report, 2013)

IT, the industry most vocal about its inability to find enough workers, hires only two-thirds of each year’s graduating class of bachelor’s degree computer scientists. By comparison, three-quarters or more of graduates in health fields are hired into related occupations (Salzman, 2013)

If you are really passionate about programming, go right ahead. I believe you will be able to earn salaries comparable to those of medical doctors or leading lawyers in the future with your skills. Maybe even better. But if you just want to get a regular job, programming could be a lot tougher than you might expect.

Update: Aner Ben-Artzi pointed out to me that the campaigns to promote programming as a career are often built on the assumption that we lump together all programmers as if they are interchangeable. We do not similarly hear calls that there are shortages of chefs, as it is understood that these are highly skilled and unique people.

Many people are worried about their social status and inequality. We live in what I call a culture of envy.

Matt Welsh, a software engineer who previously was a Harvard professor, wrote about the Fame trap last week, telling us that the pursuit of academic fame made him unhappy:

Once I had kids, I really started to appreciate the toll it was having on my family (…), and I started to realize that maybe I had my priorities all wrong. (…) I think chasing academic fame is not the best reason to go down that path. I wish I had known that when I was finishing my PhD.

The scholarly book of the year is probably Piketty’s Capital in the Twenty-First Century. This book has served as a rallying point for all of those who worry that managers are enriching themselves without bounds.

What is at stake in all cases, is that people insist on playing what Carse calls finite games. They may not realize that it is what they are doing… they may not even realize that they agreed to such games, but that is what they do nevertheless.

In finite games, there are winners and losers. The accumulation of wealth, the acquisition of a high social status… only make sense as games if there are winners or losers.

It is important to realize that this has nothing to do with absolute wealth and everything to do with envy. Let us put it clearly:

  • If you drive a luxury car, it is almost certainly to create the envy of those (like me) who drive a cheap Honda. Or to lessen the envy you feel with respect to those who drive luxury cars. Thorstein Veblen called it conspicuous consumption.
  • If you achieve fame, people will be envious of it. Your fame only makes sense if you have more of it than others, and if it is desired by others.

In a wealthy society, envy can quickly become the driving force. It is not about meeting your basic needs, but about achieving an envious position. In effect, you seek to be better off than most… in whatever finite game you are playing.

What is the alternative? Carse examines at length what he calls infinite games. You know that you are playing an infinite game when….

  • You are never playing against other people, against other teams. You are playing with others.
  • Rules can be reinvented…
  • The game may never end…
  • Players have no compelling reason to be envious of each other…

Though some people are stronger than others within finite games… there are no victories by which you can acquire power over others. In contrast, if you have just acquired $1 billion, or a tenured professorship at Harvard, you may not be interested in having these victories be forgotten. You may want society to keep on celebrating these victories, and for others to keep playing the game.

It is also very important to you, in the context of finite games, that victories feel earned. If you become a highly paid CEO by chance, you need to at least play the part of the genius… otherwise you are putting the entire game into question.

However, you cannot force others to play your finite games. Matt Welsh decided to drop out of the academic fame game. He had the tenured professorship at Harvard but he decided to play other games, elsewhere.

The same is true of money… you can buy a BMW and a big house to create envy in others… but these people can decide at any time that they are not players in this game. They may choose not to pursue the acquisition of luxury goods, and not to be envious of you.

I think that it is time that we pay less attention to people who take finite games seriously. I believe that it is the irony of a book like Piketty’s. You are most likely to be upset by financial inequalities if you take finite games seriously. So who is Piketty? He earned his PhD from the London School of Economics at 22. He went on to become a professor at MIT, and he ended up creating and leading his own prestigious school in Paris. If anyone takes finite games seriously, it is Piketty. The intellectual leader of Occupy Wall-Street, David Graeber, is similarly a competitive over-achiever… a professor at Yale University and now a professor at the London School of Economics. These are people who would not have taken “regular jobs”.

I believe that if there is one thing humanity needs to do to get through the next few centuries, is to reject envy and stop taking finite games seriously. It also happens to be a recipe to be happier on an individual basis. Take the regular jobs. Avoid conspicuous consumption. Be ok with just being a dad to your kids, as opposed to a famous star. Be ok if your kids grow up to be just regular happy folks that never win anything.

Next time you feel envious, please pay attention. And choose your games carefully.

Most people will never have to write a research grant. That is a good thing.

How do you write a successful grant application?

  • Your work should follow established methodologies. It should follow closely prior work. Departures from either your own work or other people’s work will sink your proposal.

    Your results have to be predictable. Years ahead of time.

  • Also… Your work should lead to major breakthroughs.

It does not compute.

I do not care what kind of research you do: a predictable breakthrough is no breakthrough at all.

The good scientists always have speculative ideas. Sometimes these ideas come out of nowhere, in the moment. Most of these ideas are very bad… but a few represent the real breakthroughs. And that is what research is really about. Trial and error on a massive scale. You try things until it sticks. If you knew what you were doing, it would not be research. But that is not what you will find in research grant proposals.

What you find in grant proposals are soviet-like 5-year plans… any scientist that follows such plans is doomed to mediocrity. So, what do good scientists do? They lie about what they will do. To each other. All the time.

The Matthew effect says that “the rich get richer and the poor get poorer”. With this sole principle in mind, you would think that the future is easily predicted. Whoever is rich or famous today is going to be rich or famous tomorrow.

So which programming language should you learn if you are a programmer? The most popular language right now, or the fastest growing language? If you believe in the power of Matthew effect, you should always focus on the most popular language right now, since you believe that challengers are unlikely to succeed.

At a personal level, the Matthew effect can be depressing: your starting position in life determines the rest.

Mazloumian asked an interesting related question. Given a scientist, which is a better indicator of his future success (measured by citations):

  • the total number of citations received so far,
  • the average number of published papers per year,
  • the average annual citations,
  • the annual citations at the time of prediction,
  • the average citations per paper,
  • and so on.

Can you guess the best indicator of future success?

First, it is worth stating that Mazloumian found that the Matthew effect was weak:

Our results have shown that the existing citation indices do not predict citations of future work well, and hence should not be given significant weight in evaluating academic potential. Including various indicators and testing various prediction time horizons, our results are still in agreement with Hirsch’s study “past performance is not predictive of future performance.” Even combining multiple citation indicators did not significantly improve the prediction: apart from citation indicators, no better predictor of the impact of future work exists.

But, if you are going to use a single measure to predict the future success of a scientist, you should go with the annual citations at the time of prediction. This is consistent with saying that the past is a poor predictor of the future.

Of course, the Matthew effect is real. If you start out strong, you will tend to outdo your poorer peers. However, the Matthew effect is often much weaker than people believe. People at the top of their game are beaten by challengers coming from nowhere all the time.

In some sense, it is troubling because it says that we know less than we think we know. When recruiting a scientist, for example, it is very tempting to use his past performance over many years to predict his future performance. But this heuristic is weak.

It also means that it is hard to build lasting capital. Working hard today may not be sufficient to establish a long stream of successes. To keep on succeeding, you need to keep on working hard and be lucky.

On the plus side, it means that if you have not succeeded early, you can always make it big later. It does not mean that it is easy to rise up at the top from the bottom. By definition, only 1% of all players can be part of the top 1%. Even without any Matthew effect, you would still be unlikely to reach the top 1%. What is says however is that life is probably fairer than you think.

So how do you predict someone’s performance? With humility. And this includes yourself. You do not know how well or how poorly you will do in the future. Most times, you should avoid both arrogance and defeatism.

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