Hamming Series

One way to define an expert is as someone that has developed an intuition (a ‘feeling’) for solutions to complicated problems in some domain. We’re all experts in some laws of physics (do you use a calculator when catching a ball?) and also in reading the emotions of other humans (which computers can’t do). We aren’t born experts in these things, as my 6 month old could tell you. We learn by doing.

Expertise can be developed in other domains the same way. Here Hamming relates how he developed a feel for the trajectories of missiles he was simulating for the military:

Again, I developed a feeling for the behavior of the missile-I got to “feel” the forces on it as various programs of trajectory shaping were tried. Hanging over the output plotters as the solution slowly appeared gave me the time to absorb what was happening. I have often wondered what would have happened if I had had a modern, high speed computer. Would I ever have acquired the feeling for the missile, upon which so much depended in the final design? I often doubt hundreds more trajectories would have taught me as much-I simply do not know. But that is why I am suspicious, to this day, of getting too many solutions and not doing enough very careful thinking about what you have seen. Volume output seems to me to be a poor substitute for acquiring an intimate feeling for the situation being simulated.

With better computers, he would have spent less time pondering the results. That means he would have been less of an expert because that mulling over of information is how our brains train.

Powerful computers are amazing devices but for some tasks our perception, our ‘feel’ for things, is immmensely more powerful still. A pity our meta understanding of this (how does it work?) is poor, hence the limits of AI.

Hey, speaking of this, have you heard of the vest that sees? See this kickstarer campaign:

The idea of sensory substitution is not new: it was pioneered by Paul Bach-y-Rita in 1969 with blind participants. He developed a dental chair with an array of push pins on its back, which was attached to a video-camera feed. Blind participants sat in this chair, and felt what was presented in front of the camera. After practice, the participants began to develop a visual intuition for the sensations they felt. Today, the current incarnation of this device is called the Brainport, and blind individuals have been able to use this in complex visual tasks (like obstacle course navigation).

I’m constantly trying to strike the balance between intuition and computation in my day job. When building a model, loading the data into a framework is probably about 85% of the observable ‘work’ after debugging and whatnot. But I spend probably 2x that time just thinking about the project and how I feel about our approach. I do it on my commute, when I walk the dogs and when I sleep. And I do it at my desk with my head in my hands in front of some outputs I’m trying to make sense of.

But you also need to know when to stop yourself. In many ways expertise is a miraculous thing but it has blind spots, particularly when we think about the very complex. Here are some things that we aren’t good at feeling:

• non-linear systems
• multiple interactions of variables and constraints
• very low probability events

As a remedy, Hamming advocates for an interesting habit: building toy models.

The reason back of the envelop calculations are widely used by great scientists is clearly revealed-you get a good feeling for the truth or falsity of what was claimed, as well as realize which factors you were
inclined not to think about, such as exactly what was meant by the lifetime of a scientist. Having done the calculation you are much more likely to retain the results in your mind. Furthermore, such calculations keep the ability to model situations fresh and ready for more important applications as they arise. Thus I recommend when you hear quantitative remarks such as the above you turn to a quick modeling to see if you believe what is being said, especially when given in the public media like the press and TV. Very often you find what is being said is nonsense, either no definite statement is made which you can model, or if you can set up the model then the results of the model do not agree with what was said. I found it very valuable at the physics table I used to eat with; I sometimes cleared up misconceptions at the time they were being formed, thus advancing matters significantly.

Models force people to be explicit about what they believe and let the results think for themselves. It can build intuition but it also works in situations (like with the bullets above) where intuition is unreliable. This isn’t trivial. People are often taken advantage of when they don’t have an intuitive feel for a problem and simply trust what others say. For those so trained, it’s easy to find a ton of nonsense statistical claims out there. One defense against this is our ability to tell if someone is lying.

But what if, blinded by bias they don’t know they’re misleading you? Even the most sophisticated scientists fight viciously over complex questions. Read Paul Krugman’s blog for a few days to see a high status economist be as nasty as you like against his intellectual rivals because nobody can convince anyone else of anything in macroeconomics. Too complex for us to understand. Both sides will tell you different, but then why can’t they convince each other?

It’s easy to get down about partisan political debates but to be honest, I’m not sure they matter much. In your life and mine, improving one’s own expertise about problems that matter is totally achievable.

But it doesn’t happen by accident. Training your brain takes work.

The most obvious illustration is computers have given top management the power to micromanage their organization, and top management has shown little or no ability to resist using this power…

The persons on the spot usually have better knowledge than those at the top and hence can often (not always) make better decisions if things are not micromanaged. The people at the bottom do not have the larger, global view, but at the top they do not have the local view of all the details, many of which can often be very important, so either extreme gets poor results.

Here’s a question I think about: why do mature industries consolidate? I’m going to be referring often to large, highly optimized organizations in mature industries. That’s pretty boring to write and read so I’m going to call them Dragon Wagons.

Scale helps Dragon Wagons sell because it supports the fixed costs of strong branding, which gives customers comfort. Scale also helps production because Dragon Wagons’ products change slowly and linearly. Given enough time and size they can snuff out opposition with a small marginal advantage.

Here’s another advantage to consolidation and my response to Hamming’s problem of micromanagement: you can’t have a real bureaucracy unless you’re big.

There’s a tradeoff between efficiency and innovation and Dragon Wagons focus exclusively on the former. Those that work in a bureaucracy probably marvel at the waste and no doubt the waste is huge. But everything’s huge at huge companies. Dragon Wagons develop highly specialized, silo’d bureaucracies that are incredibly effective at their assigned tasks but otherwise think and move on a geologic time scale. Hamming’s not the first to observe that this sucks; in fact, I’d say his loathing of them is nearly universal.

But if we all hate bureaucracies, why do so many exist? Because they’re damn effective is why. Effective specifically at distributing knowledge through an organization and protecting it from new ideas, the vast majority of which are stupid. As Hamming himself says elsewhere:

Regularly I read or hear I am supposed to believe the new gimmick, typically these days the
computer, will make a significant difference in spite of all past promises which have apparently failed
miserably. Beware of the power of wishful thinking on your part—you would like it to be true so you assume
it is true!

Individuals love feelings of control, of purpose, of contribution. Individuals are stimulated by new ideas and new challenges. Bureaucracy exists because each of these impulses is toxic to a Dragon Wagon. Bureaucracy protects the company from all the better parts of its employees’ humanity.

Hamming frames this problem as one of leadership pushing bad decisions into the firm but a real bureaucracy fights back with passive aggressive intransigence. Here is a quote with a typical sentiment:

Next, an idea which arises in the field, based on the direct experience of the people doing the job, cannot get going in a centrally controlled system since the managers did not think of it themselves. The not invented here (NIH) syndrome is one of the major curses of our society.

I disagree with the last sentence, here’s how I see the fundamentals:

1. Change is hard. Especially in Dragon Wagons where unintended consequences of change can be catastrophic.
2. These problems can be overcome by exceptional people who are exceptionally motivated.
3. You cannot reliably find people who are those things.
4. So empowering people probably means destroying the company.

Don’t get me wrong. Good ideas exist and outstanding managers can identify them and implement them. But these saviors can’t be reliably found. And even with them the costs, whether intended or not, are bigger and harder to anticipate the bigger the firm gets.

And here’s a benefit: because the brilliant leaders are hard to find, having a business model that doesn’t require one means there’s no ‘key man risk’. A founding genius moves on and hands the reigns to the ‘Harvard guy’ with a lantern jaw, a good work ethic and a briefcase full of mindless rhetoric and buzzwords. Doesn’t matter, the firm runs itself.

A common narrative of consolidation is simple megalomania. But this is also about the leader writing himself out of the script. Dragon Wagons cannot be run by a small team but acquisitions can be. There is a peculiar alignment between the megalomaniac and the bureaucracy, if such an entity had a will and desire of its own.

This is a bleak vision, to be sure. And if they are in a competitive industry, Dragon Wagons eventually get cleared out out by startups armed with cutting edge technology. But for industries that don’t change quickly or that aren’t exposed to competition, a bureaucracy provides a protective shell around the vital business processes.

Bottom line: without them, it would be worse.

It is well known the drunken sailor who staggers to the left or right with n independent random steps will, on the average, end up about root n steps from the origin. But if there is a pretty girl in one direction, then his steps will tend to go in that direction and he will go a distance proportional to n. In a lifetime of many, many independent choices, small and large, a career with a vision will get you a distance proportional to n, while no vision will get you only the distance root n. In a sense, the main difference between those who go far and those who do not is some people have a vision and the others do not and therefore can only react to the current events as they happen.

Day-to-day, a leader exists to answer the question: what should I work on? Everyone in the world spends their days solving problems: problems at work, problems at home, problems with their parents, problems with their kids, problems with their car, their roof, their air conditioner. So if everyone is always solving problems, what’s the difference between you, me and, say, an engineer at Facebook or a hedge fund trader?

Well they’re solving different problems than us. But it’s not like they’re solving problems that other people couldn’t solve with some training. Lots of people could solve those problems but these people know something you and I don’t: they know which problems need solving. How do they know which problems to solve? Their leaders told them, that’s how.

Recently I was busily drinking heavily and talking nonsense with a good friend of mine in the military and we came to a realization that I’m pleased I remembered because I think about it all the time now: all organizations exist to solve problems and the job of people who run organziations is to:

1. find problems for the organization to solve
2. focus the organization on solving them

If the leader is good, the problems are well selected and the organization is successful in its goal (making money in my case, much higher goals in my friend’s). If the leader is poor, resources are wasted on valueless solutions to inconsequential problems.

What’s also interesting about Hamming’s point above is that the direction doesn’t matter. Here’s another excerpt:

One of the main tasks of this course is to start you on the path of creating in some detail your vision of your future. If I fail in this I fail in the whole course. You will probably object that if you try to get a vision now it is likely to be wrong—and my reply is from observation I have seen the accuracy of the vision matters less than you might suppose, getting anywhere is better than drifting, there are potentially many paths to greatness for you

This isn’t so applicable to the military, of course, but in business and research, the path doesn’t matter and maybe your first attempt at direction is wrong. So, to use the tech buzzword, you pivot.

Now, even the most focused organizations can run out of resources: be it money or time or, most importantly perhaps, energy. It is also the leader’s job to make sure that people aren’t just focused on the task but enthusiastic about the task. People need to buy in to the focus of the organization. They need to understand it. Luckily, focus tends to be accompanied by passion in the human emotional spectrum, and passion is contagious. A good leader infuses people with focus and energy at the same time.

Part of my Hamming series

I recently finished a book by Richard Hamming called “The Art of Science and Engineering”. This book received an extraordinary recommendation from Bret Victor, a pretty interesting guy in his own right (how cool is his website?). Then Bret went and posted the whole damn book online, so I put it on the list for right after I got through my exams this year.

It is as good as I was expecting so I’ll be writing a few blog posts about it over the next little while. Consider this a heads up.