Scott H Young

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How Good Is Your Theory?

August, 2018
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If you’ve ever played a game of chance, like poker, you know one of the most frustrating experiences can be when you make the “right” decision, and still lose.

In a casual game with some friends of mine, one player wasn’t taking the game very seriously. When his cards were dealt in the first hand, without looking at them, he decided to go all-in.

Another friend who was playing more seriously noticed that he was dealt two kings, a very good starting hand. Since it was the first hand, everyone had the same amount of chips, he also had to go all-in to match the bet. Worrisome, because if he lost, he’d be out of the game before he even got a chance to play. But, at the same time, it was the correct move since he should win with such good cards.

The friend who wasn’t playing seriously and hadn’t looked at his cards? He had two aces! He won the hand even though the odds were stacked against him.

This kind of situation can be frustrating. Yet, nobody looking at the game would have seen my friend—who went all in without checking his cards—and used that as a model for being a good poker player. This fluke win didn’t rule out that he was using a bad strategy.

Bad Strategy or Just Unlucky?

In games like poker, we can easily distinguish those who won because they had the right strategy, and those who won because they were lucky. Statistically, those two things tend to coincide, but there’s plenty of weird moments, like my friends’ game, which offer a counter-example.

One reason we can distinguish a good strategy from a bad one that happened to be lucky is that we have an extremely good theory for how winning should work in poker. Laws of probability mean that, provided there’s no cheating going on, we can estimate the probabilities of certain plays winning, given certain decisions, almost exactly.

The theory for poker is so good, that it’s easy to see the difference between bad-decisions-which-won and good-decisions-which-lost. My friend made the right choice, he just happened to be unlucky.

Most of the luck in life, however, doesn’t have nearly so good a theory. Success in your career, business, investing or relationships, doesn’t simply reduce to laws of combinatorial mathematics.

What should you do in those cases, where working out the correct decision is much harder?

In those cases, you can simply copy. Look at people who won and do what they did. Yes, this will cause you to copy some bad strategies—like my friend who bet everything without looking at his cards—and you’ll make mistakes. But you’ll also make mistakes if you end up following a theory which turns out to be completely wrong.

Copy or Theorize?

I’d like to put these two different approaches to making decisions on a spectrum. At one end you have blind copying. There’s no attempt to understand what is being copied, it is just trying to match as much as possible, in the hopes that you pick up something that works.

At the other hand, you have pure theory. You work out all the theory exactly until you figure out what is the “correct” approach and apply it. Ignore experience and examples which are counter to this as being just luck.

Most decisions in life are somewhere between those extremes. You use your reasoning to form some model of how the environment works, and try to plan out some strategy according to it. But, at the same time, you can’t be too certain your theory is correct, so you also copy strategies you’ve seen win in the past.

The downside of copying is superstition. You may end up copying elements which are superficially successful, but don’t matter at all.

Cargo cults represent the extreme of copying failure. These were islands who, during wartime, were used as air bases. The islanders, seeing the control towers and military officials bringing in supplies, started to copy them. They make fake control towers, fake landing pads, fake headphones, all to attract the cargo that they saw before. Unfortunately, none of these things work at all because the islanders had the wrong theory.

The downside of excessive theorizing is having a model of reality which turns out to be false. You follow the “correct” path but reality doesn’t work that way.

In my book club review of Seeing Like a State, I discuss how James C. Scott shows how excessive rationalization was behind many of the most catastrophic failures of the past century. Many modernization efforts failed completely because the theories just weren’t very good. Model cities, modernized agriculture and political systems which looked good on paper but failed in practice.

How to Make Good Decisions With Mediocre Theories and Examples

Copying, despite being seeing as a less sophisticated strategy, is actually not a bad starting point. This seems to be how human beings learn mostly anyways, and it embodies a lot of our cultural success. Find winners and do what they do, may seem simple, but it largely works.

That said, copying isn’t always possible, and sometimes you’ll waste a lot of energy copying the wrong things. In those cases, you want to adjust how much you copy based on how good a theory you have to offset it.

The more you learn, the better your theories will be. The more you understand probability, the better your poker game. The more you understand economics and finance, the better your investment decisions. The more you understand psychology and people, the better your relationship decisions.

However, it’s important here to note that, even if you learn everything the world has to teach you, many times your theories still won’t be all that good.

In those cases, you should deviate from the copying strategy only partially. Over-doing it—taking the theories you learn as being perfectly accurate—may make your strategy even worse if you switch completely from copying and try to create your own strategy from scratch.

Some Examples of Balancing Copying and Theorizing

Learning is a good example of an imperfect theory. There’s a lot we know about how people learn, and I have my own ideas both from informal experience and reading scientific research, that give me some sense of how to learn best.

However, I always try to use, as a starting point, how people successfully learn the thing I want to learn. If I want to learn languages, I start by looking at what people who have learned languages fluently actually did, not some abstract theory of memory or language acquisition.

Then, when looking at the strategy, I might make changes to that based on my theory of how learning works. When designing my Year Without English project, for instance, I rejected a common idea that immersion needs to be 100% complete (meaning no asking questions in English to clarify grammar, nobody else can teach you using English, etc.). I felt confident enough to reject that as being probably a holdover from the fact that young children who learn languages well often do it that way, but that as an adult it made understanding harder.

I think this balance between copying and reasoning is often missed because people have theories for almost all areas of life, but there’s often not a clear sense of how good they are (nor, what the alternative is, if they aren’t very good.) Copying is often seen as being a stupid strategy, but I think it can often be quite intelligent when your theory of how something works is bad.

Book Club: The Structure of Scientific Revolutions (August 2018)

August, 2018
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This month we read The Structure of Scientific Revolutions by Thomas S. Kuhn.

If you would like to stream audio on your browser, click here listen on Soundcloud.

American historian and philosopher Thomas S. Kuhn was a leading contributor to the change of focus in the philosophy and sociology of science in the 1960s. Born in Cincinnati, Ohio, Kuhn received a doctorate in theoretical physics from Harvard University in 1949 and later shifted his interest to the history and philosophy of science, which he taught at Harvard, the University of California at Berkeley, Princeton University, and Massachusetts Institute of Technology (MIT).

In 1962, Kuhn published The Structure of Scientific Revolutions, which depicted the development of the basic natural sciences in an innovative way. According to Kuhn, the sciences do not uniformly progress strictly by scientific method. Rather, there are two fundamentally different phases of scientific development in the sciences. Kuhn’s theory has triggered widespread, controversial discussion across many scientific disciplines.

In many ways, Kuhn broke the understanding of science:

In Kuhn’s study, science as it was actually practiced, didn’t work like this at all. Instead it was an oscillation between the normal, commonplace expansion of existing theories and results, and revolutions whereby entire fields were upended and replaced with a new model from the ground up.

Although Kuhn rarely pointed directly to some easily recognizable object as being *the* paradigm he sought to describe… if you’re studying physics for instance, the paradigm is embodied by Newtonian mechanics is balls rolling down inclined planes, pendulums swinging at constant periods or celestial objects following elliptical orbits.

The view, prior to Kuhn, had been that science works via accumulation. Paradigms, in contrast, don’t work this way. Consider a pendulum:

In Newton’s day, it was known that a pendulum, once it started swinging, would continue to swing at the same rate, and the closer it approached ideal conditions (less friction or air resistance), it would keep swinging forever. Kinetic energy becoming potential and back again.

Kuhn argues that the Aristotelian view of a pendulum wouldn’t have been to see it that way. In other words, science didn’t just get an accumulation of new facts when it went from Aristotle to Newton. 

In Kuhn’s view, scientific revolutions, like political ones, are a violent affair.

They are not merely the supplanting of the current regime using the tools and structures currently available. Instead they’re a rejection of those tools and often supplant the new theory by breaking the accepted practice of the old one.

In fact, science, according to Kuhn, progresses in a process of three distinct phases: normal science, crisis and revolution.

Normal science is, well, normal. It’s the thing scientists do, except in the times of revolution. Kuhn argues that most of normal science is a kind of puzzle solving. The crisis eventually evolves and soon the anomalies are so prevalent that they cannot be contained in the current paradigm. As a result, scientists increasingly diverge, exploring stranger and broader methods for tackling the problem that begin to depart from the paradigm.

Finally, there’s success, a new theory or paradigm explains the anomalies so well, that other scientists are converted and a revolution is afoot. If the new theory can be pushed successfully to encompass enough of what was already known beforehand, it may triumph over its predecessor wholesale.

In my own life and writing, I feel like I’ve gone through the same process Kuhn describes with many of my ideas. I’ll start with some idea of how life or the world works, and then problems begin to appear in the theory which I push aside. Eventually a new idea comes around that resolves those problems better than before, and I switch over. The old ideas are usually not entirely wrong, but in the new way of thinking they’re wrongly conceived. They don’t match up with the concepts and ideas that now exist in my mind.

The Structure of Scientific Revolutions was our book for this month in my monthly book club. Each month, I read a new book, and I invite you to read it along with me. At the end of the month, I’ll post a recording or discussion podcast episode like this one, to share my takeaways from the book. I highly recommend reading at least a couple of the books, even if you can’t always keep up with the one-per-month pace.

I’ve been trying to pick books that I think are particularly important, not because they’re necessarily easy to read. My hope is to expose you, even if you just follow this podcast, to some books that are a little different from the usual self-help and business books that populate bookshelves. However, I think the effort you put into reading them can be well worth the effort, perhaps even provoking the revolution in thought that Kuhn described.

Next month, I’m going to be tackling a book that many of you may not agree with. Indeed, when I first encountered the ideas of the book, I was highly resistant, as these too formed an anomaly I wanted to reject. However, much to my chagrin, the book is incredibly good: extremely thoroughly researched, carefully argued and backed up with enormous amounts of data. The book is Bryan Caplan’s The Case Against Education, and I’ll be discussing it on next month’s episode.

Feel free to join in on our Facebook Group Discussion I’d love to discuss this book with you there.

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