How to Retain what you Learn

Many of us read books, but what do we remember? What do we use? How much do we lose after closing the cover?

The answer is: we lose a lot. Lectures and books are particularly bad ways to learn and retain knowledge. One of our community members, Maarten van Doorn, has written an amazing Medium.com essay called The Complete Guide to Effective Reading. It’s worth a look. Skim it to get the overall concept, then read (and take notes) if you are really interested.

How a Double Nobel-Prize Winner' Belief System was Proven Wrong

The tale of Linus Pauling is an amazing one. I won’t take much of your time today except to ask you to read a Vox article on Linus Pauling and how he mistakenly led millions to believe that vitamin C cures everything from the common cold to cancer:

Linus Pauling.jpg

Read the Linus Pauling Story

Hey, hold on a minute! Has new research vindicated Pauling’s idea?

No, it hasn’t.

Even when we are convinced we are right, when we repeat our beliefs over and over, and when we have the signal of a Nobel Prize, we should still try to be less wrong rather than more right.


The Right Answer to Almost Any Question

Let’s take a difficult one: Should we require doctors to have licenses?

This isn’t difficult at all, you say? It’s simple. Yes, we should require doctors to have licenses, OBVIOUSLY! You don’t want your surgeon to be the taxi driver who just took you to the hospital. DUH!

That turns out to be the wrong answer.

The right answer is: “I don’t know.”

Oh. Well. What should I do with that? You can always say “I don’t know” to anything, and there’s no discussion. Actually, what I mean is that the right answer is “I don’t know. How can we find out?” What method would we use to understand this particular problem?

The one method we use far too often is to look into our limited store of experiential data, pull out a few anecdotes or clips from CNN, and declare that we have a pretty good way to answer the question. After all, this is what they do at the Harvard Business School, and we know it doesn’t work.

The right method is what we call the outside view: how would a Martian descending to earth answer any question. The Martian would have to start doing research, gathering data, asking questions, and trying to put a puzzle together. Here are example good questions:

  • Is there anywhere that actually doesn’t require licenses? How are their results?

  • Are there varying degrees of licensing? Can we compare their results in some smart way?

  • What is the track record of different kinds of doctors with licenses in different places?

  • Are there any specialties where a license is particularly helpful or harmful? What data do we have on this?

  • Is there any academic research on this?

  • Are there any metastudies?

  • Have there been any natural experiments?

  • Have there been any controlled experiments?

Now we’re getting somewhere. So let me ask you: Should we require doctors to be licensed? Take the outside view and SHOW me some evidence in the comments by pointing to research you think is valid. DON’T give me your unvarnished opinion - save that for Twitter and Facebook.

If you don’t get Leandro Herrero’s amazing daily thoughts, I highly recommend them. Start with this post:

I don’t know, and I suspect you don’t know either.

Don’t forget to subscribe when you’re there!

Why Books Don't Work

Previously, I pointed to Maarten van Doorn’s excellent article on how to actively read a book.

Another good article is Why Books Don’t Work, by Andy Matuschak. I highly recommend reading that essay as well.

What both authors say is that we need to work with the material, exercise our muscles, repeat, and keep washing these concepts around in our brains to get them to stick and be useful. Books should be more interactive. They should ask you questions. They should ask you to review what you’ve just learned and put it into practice.

So Matuschak tried that for his latest book, Quantum Country. Well, it isn’t a book, it’s two chapters online, and each chapter has questions and exercises to really put the material to use. The material really teaches you about quantum computing - it’s not a layman’s simplification, it’s a clear explanation of the physics and the math involved. You can register, and they have an email system that sends you an email to help you work with the concepts from each chapter. They call it an “experimental mnemonic medium.”

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This is definitely the kind of experiment I hope we see more of.




How Should I Invest?

From my essay, Inreality.show …

Does fundamental investing work? Not so much. Everyone has the same information, which means “the fundamentals” are already priced in, so you can’t get an edge by studying reports and analyses. Stock market investing is a game of wolves and sheep, where the wolves are those with a computing and algorithmic or data advantage, and the sheep are all the rest of us. Hedge funds are in an arms race to create profitable algorithms, and now most of those funds have reached the same plateau. Even Warren Buffett says he can’t beat the market (and hasn’t in over 20 years). Those who have an edge over the market usually make money until their edge disappearsoften surprisingly quickly. The problem is skewness, which you can understand from reading this technical paper or by reading The Black Swan, by Nassim Taleb. My favorite book on investing is A Man for All Markets, by Ed Thorp.

So how should I invest? Passively. Most of us mortals can’t generate alpha, so it’s better to capture beta. That means take advantage of the average gains of various asset classes. Have exposure to global markets, real estate, and inefficient asset classes. Try to find “smart beta” opportunities. As Eliezer Yudkowsky says, “An efficient market is one where smart individuals should generally doubt that they can spot overpriced or underpriced assets.” Most markets are very efficient. If you can pick the winners ahead of time, by all means give it a try. You might get lucky.*

For a quick summary, read this excellent blog post by Paul Glance, PhD.


The Scientific Method

The world is far more complex than most of us think. Our lizard-brains want everything to fit into neat little bins, for there to be no ambiguity, and cause use to say things like “the science is settled.”

One thing I’ve learned is that if it is settled, it isn’t science. Let me put a Richard Feynman disc on the platter and spin it up for you:

If you are curious, I have curated a list of scientific method videos on YouTube:

Take Me to the YouTube Playlist!

The Prize Economy

I’m a big fan of prizes and challenges.

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Tim Ferris tells a good story about offering a serious prize to a group of Princeton students if they could contact a very famous person and get him/her to answer three questions. You should read that story - the outcome is interesting.

If I had a billion dollars, here are some of the prizes I would offer:

Make a robot who can throw a specially made flying disc (essentially, a Frisbee made of aluminum or magnesium or carbon graphite, with a sharp edge to cut the air) one mile and embed in a target no larger than 100 feet wide. It does not have to be caught by a human - in fact, I recommend humans stay out of the flight path. First group who does it gets $100k.

Make the world’s fastest human-powered land vehicle. Can have as many humans as you want, but no extra energy source. Has to set a speed record for average speed over a 200-m measuring distance on a flat course with no wind. I expect it will look a lot like a bicycle. There will be a bi-annual competition with $100k to the winners, $50k to second place, and $25k to third.

Make the world’s most efficient bike. The idea is to design a bicycle that can be ridden on a given route, which includes ascending and desending at least 2,000 vertical feet and goes for at least 50 miles, with the fewest watts necessary. This means it should have regenerative braking and a way to reapply energy put in to smooth out the load. No external power sources. In addition, this is a race, so the winner must show a combination of speed and efficiency.

Design a shrimp magnet - a device that gathers shrimp from the sea floor with less than 3 percent bycatch. Does not have to be fast, but it mustn’t harm the sea bed. Has to pass a reasonable test. Prize: $2 million. Amount doubles every two years until it is claimed (up to $32 million).

Design a vegan egg substitute. Many groups are working on this, and there are different definitions. Mine doesn’t need to make perfect omelettes, but it does have to really work in baking, pancakes, pasta, etc. $200k.

Design a plan for a vaccine for the common cold. There are about 30 cold viruses. If we could get one booster, rich people would be able to purchase immunity to the common cold. Over time, that price would come down, and everyone would benefit. The problem is that it’s very expensive to develop, test, and get approval for such a vaccine. Plus, viruses can mutate and thwart our efforts. The challenge is to find a clever way to solve that problem and also address any knock-on effects it may have. $1 million for the plan that can be implemented reasonably as judged by a panel of experts.

Create a company that designs and builds a bike specifically for cargo use in Africa. There have been many attempts at this. Designing the bike is only a small part of it. You have to design manufacturiing, assembly, distribution, service, and the economics. This should be a business plan plus prototype competition, and the winner gets $10 million in tranches to start building it. My suggestion: start in Rwanda, because Rwanda has roads.

I could keep going, but I’ll stop and ask … what competition would you design?



When do we Deserve Happiness?

One of our community members is a prolific writer. Maarten Van Door has written a good piece on how to get off the hamster wheel of work-money-pleasure-satisfaction that many of us are on. It has 1,200 claps, which is normal for him, because he’s a good writer. I enjoy the quotes he includes and the insights he provides, even though he’s only 26. Today, I recommend reading his piece:

Science and the Myth of Being Worthy

What is Self-Sovereign Identity?

I’ve written about self-sovereign identity, a scheme in which you control your own identity. But I haven’t really gone into depth on it. Ron Kreutzer and the team at Pillar are working on it. Here is Christopher Allen explaining the basics:

And here is the incomparable Drummond Reed, who has spent his career studying and promoting digital identity, explaining the DID standard, which I believe is the future standard for identity:

How One School Gamed the System and Won

One of my pet peeves is education, because education is broken. We shouldn’t fix education, we should replace it with something more relevant. Today I want to highlight the story of one man, Richard Freeland, who took NorthEastern University to the top-30 in the rankings of US News and World Report, which is the gold standard of university rankings. It’s an amazing story, well worth the next ten minutes of your time:

How Northeastern Gamed the Rankings System and Won

Six Myths of Portfolio Construction, Part II

Myth #4: Private Equity is the Place to Be
It’s getting harder and harder to get killer deals in private equity. There are so many PE shops scouring the globe that getting the right thing at a bargain price is wishful thinking. If the price is a bargain, it’s that way for a reason. Private equity is becoming more and more like public equity. The Internet is making this asset class more efficient every day.

One exciting development for people in private equity is that we’re learning much more about how to run companies than we ever knew before (and than they still teach in business school). By becoming lean and agile, most companies will become far more efficient and innovative, able to better keep up with today’s increasing pace of innovation. I have written a book about this and have a web site dedicated to business agility. Since most PE fund managers have very little operational experience, they should seek out people who can help them increase productivity, employee satisfaction, and customer wow using the principles of business agility.

Myth #5: Smart Venture Capitalists Beat the Markets
According to the Kauffman Foundation’s excellent report, We Have Met the Enemy, and He is Us, venture capital is a poor asset class. The people who think they can pick the winners are fooling themselves, as we have seen above. This article from CB Insights shows that, even though all the VCs think they can pick the billion-dollar companies beforehand, the data shows otherwise. In fact, recent studies have shown that venture capitalists have a strong bias toward funding good-looking white males.

It’s important for investors and venture capitalists to understand that a venture capital fund is not a machine that generates either companies or profits. A VC fund can only do so much. One thing they can do is get cash to entrepreneurs who probably can get it from other sources (they rarely fund companies that don’t look tasty, trendy, and backable). Another thing they do is give advice that the entrepreneurs can probably get from other sources. And a third thing the VCs do is help them find exits that are good for the fund (the sooner the better). Any venture capitalist who thinks he can steer his fund into the top quartile of returns, above the dividing line between 1st and 2nd quartile, doesn’t understand statistical variance. Once again, 25% of funds HAVE to be above that line, by definition, and several funds have found themselves above that line several years in a row, but cause and effect is dubious at best. It’s likely that in ten years of venture investing a fund will be able to point to two or three deals that contributed to most of the profits, and if you look at those deals carefully you’ll see that timing and luck had a huge amount to do with the outcomes. Bessemer Ventures has published a list of companies they passed on, any one of which would probably have outperformed their entire portfolio.

Believe it or not, there are ways to make money in venture capital, but not the way most VCs do it. You need to harness the power of convexity – making small investments that have big payoffs, capturing beta, rather than alpha. A few funds are already on this track, and a couple of them actually have the statistical understanding to make it work. More on that in a minute.

Myth #6: The Future Looks Like the Past
The next five years in investing are going to be nothing like the last five years. Mark Spitznagel has a compelling argument for our current stock market situation being another house of cards, ready for a significant correction. Why do we keep falling for the rosy scenarios when things are good and then believing the world is over every time markets collapse? Neither is true. Mean reversion is more powerful than prediction. If you believe in mean reversion, then you must admit that the road ahead is extremely risky. It’s impossible to predict the future, but it’s very likely that some huge corrections are in store, somewhere, at some time in the next several years. There is almost zero percent chance that the next five years will look like the last five. There is hidden inflation that isn’t reflected in the official numbers (we’re not really measuring inflation correctly to begin with). There is hidden unemployment. There are dangers in the increasing gap between ultra wealthy and middle class. In short, it’s the unknown unknowns that often show up as black-swan events and trash portfolios, even portfolios based on MPT.

Modern Portfolio Theory has disappointed many of its customers, and that’s because we don’t live in a world with normally distributed events. We live in a world of black swans, complexity, emotion, and surprises. Perhaps the biggest myth in portfolio construction for the past twenty years has been the belief that modern portfolio theory models the world and its uncertainties accurately. It doesn’t. It’s best to remember that all models are wrong – it’s a matter of how wrong – and we don’t get to find out until later.

To put these myths into a larger perspective, Doug Hubbard, author of The Failure of Risk Management, explains:

In response to the 2008 financial crisis, several of the major consulting firms and standards organizations have charged in with a variety of “solutions” for risk management, none of which is better than consulting astrologers. The worldwide financial system remains as interdependent, fragile, and poorly understood as ever.

Investor alpha is largely an illusion, a story told by people who have gotten lucky. I believe one should hope for the best but be prepared for the worst, taking the growing list of human cognitive biases into account.

Six Myths of Portfolio Construction, Part I

This is reprinted in two parts from my blog post on Hedgeconnection.com

All portfolios have at least one weakness. Many portfolio managers think they have their weaknesses covered, but as Nassim Taleb points out in his book, Fooled by Randomness, most managers build beautifully detailed models based on bad assumptions and make the mistake of thinking that their models reflect our complex world. Here are six common myths about portfolios and investor alpha that every family office and institutional investor should understand …

Myth #1: The Track-Record Bias
Would you put a portfolio together consisting of the highest-performing mutual funds of the last five years? You probably wouldn’t, because you know that these funds have more downside than upside. You know that the environment that powered these funds to success may not continue. The same goes for most hedge-fund managers. There are about 10,000 hedge funds investing around $2 trillion worldwide. Let’s suppose that 200 hedge funds have fantastic track records simply by being lucky, and perhaps 20 of those are actually good at investing. If you’re running a family office or making large investments, the only funds you will see are those with good track records. All of these funds will have a story – a story of cause and effect, showing with powerpoint slides and data that they know what they are doing. Managers with less than excellent track records won’t make it to your desk.

We know that hedge funds as an asset class do not outperform the broader markets. Assuming a few hedge fund managers actually have skill and can pick the winners, can you tell, going forward, who the winners will be? Almost all the research to date says you can’t tell skill from luck by looking at track records. Statistically, there simply have to be a fairly large number of funds with great track records and a good story – there always have been, and there always will be. Investors and hedge-fund marketers are fooled by this bias and are surprised when, sooner or later, the “absolute returns” stop returning.

Many track records are compared against a straw-man benchmark, like the S&P 500. The problem with many of the big indexes is that they are cap weighted, so companies with high valuations are over-represented. Equal-weight indexes tend to outperform cap-weighted indexes, and many mid- and small-cap indexes do even better. So if a fund has outperformed the S&P, better to ask whether it has outperformed a better index, after taxes.

Myth #2: The Smart Guys Can Pick the Winners
Buying public equities is like betting on horses. No one has been shown to be a consistently good stock picker, not even Warren Buffett. Sometimes he doessometimes he doesn’t. It depends on the measuring stick and time frame you use. A portfolio of carefully selected public stocks is either going to go up or down. If it goes up, it gives you false confidence that you know what you’re doing. If it goes down, you blame external factors. Now that we’re coming to the end of an unprecedented period of government injection of cash into public markets, do you really think the strategies that have been successful in the past four years will outperform in the future? Is Warren Buffet a member of an elite group of superinvestors who know value when they see it, or has he just been lucky? Has Ray Dalio really built a cause-and-effect machine, or has he gotten lucky? In Dalio’s case, my guess is about half of each, yet he tends to take full credit for the good years. Long/short funds with active managers must get lucky to outperform the markets. And some do.

Myth #3: Successful People Know How the World Works
We learn much more from failure than we do from success. Success is a poor teacher. Anyone who has had success also has a story about how he earned it. We hear it all the time, from Ashton Kutcher to Donald Trump. Their message is: work hard, play fair, and watch for good opportunities. They don’t have any idea how many people out there follow the same advice and the opportunities don’t come their way, or they just don’t get as lucky. Luck plays a huge role in success. Few billionaires are willing to admit that if things had gone just slightly differently, they would not have 90% of their fame and wealth.

Let’s take a statistical look at wealth and luck. This article claims that 45% of billionaires are in the top 1% of cognitive ability, showing that billionaires are more than hardworking people who got lucky. They are also incredibly smart. Let’s break that down.

First, the other 55% of billionaires may be smart, but they aren’t in the top 1% – how did they get their money? By being almost super smart? Are these people ranked lower in total assets than the 45% who are said to be smarter? Seriously, you can imagine this correlation if you want to, but it’s more likely that there is tremendous variance here, and that, as Michael Mauboussin points out, you need both skill and luck to be above-average successful.

Let’s go back to the 45% of billionaires who got good grades and performed well on standardized tests. In the developed world, where the best schools are, there are about a billion people, so the top 1% pool represents ten million people. Accounting for children and non-business people, let’s take it way down to 100,000 – the top 1% of the top 1%. This must be a group of very smart people. There are about 1,600 billionaires in the world. 45% of that would be about 700. So, out of about 100,000 super-smart people on earth, 700 have become billionaires and 99,000 haven’t. Where is the cause-and-effect in that? Did all those other people end up doing very well for themselves but made under $1 billion, or is this authorforgetting the base rate and measuring the wrong things?

One of my favorite examples is Richard Branson, who quite simply has gotten very lucky several times. Someone has to, and he’s the poster child. A great and fun guy, to be sure, and smart enough to hire people who execute well, but the vast amount of his wealth can be attributed to timing. He sold his retail empire to raise money for his airline at exactly the time when retail music was falling off a cliff and air travel increasing exponentially. There’s really no such thing as a timing genius. Timing is essentially doing what other forward-thinking people are doing and being the lucky one. Out of millions of struggling entrepreneurs, a handful come out with a few back-to-back trades that land them on the Forbes list. Richard Branson is one of them. If he were really good at timing, he could have made a hell of a lot more money than he has. Richard Branson has been the fortunate victim of a few positive black-swan events. Whoops – I should have said Sir Richard. How many hard-working people have been knighted for simply being in the right place at the right time?

It has been said that George Soros has been skillful in timing the markets, because he has made so many individual trades. But a power law applies – a small number of very big bets went his way and generated most of his cash. The vast majority of his trades could have gone either way without significant impact on the portfolio. Not surprisingly, in the few hugely successful trades, he had a systemic advantage. There is skill involved, but there is also a lot of luck.

Tomorrow: Three more myths

Randomistas

This is such an important book that I hope it goes straight to the top of your summer reading list:

Randomistas book cover.jpg


Buy it at the US Amazon store or wherever you buy books.

In chapter after chapter, Andrew Leigh gives detailed accounts of randomly controlled trials destroying commonly held beliefs. What we think should work often doesn’t. RCTs have influenced and shaped our world more than we think, yet we are just starting to insist on them.

Build an Experimental Culture
Google, Amazon, Facebook, Capital One - these companies are constantly running hundreds of experiments with randomized control groups. Scott Cook, chairman of Intuit, talks about replacing executive decisionmaking with randomly controlled trials.

There are three steps here:

  1. Be aware of the value of RCTs.

  2. Create a culture that values RCTs and knows when to use them.

  3. Build strong muscles for designing RCTs and evaluating their data.

Step three is probably the hardest. Actually interpreting statistical data is beyond the capabilities of most statisticians. They are easily fooled by biases (as I showed in my Apple watch post recently). In fact, a sharp researcher recently discovered flaws in the way randomization was done in thousands of experiments, forcing many of those researchers to re-evaluate their data. It’s harder than you think.

Experimental Agility
I think it’s valuable to learn to do mini-trials that are quick and give a first-order answer that you can use to move forward with while continuing to refine your questions and designing trials. RCTs should be part of any agile approach to government, institutions, and companies. Even families and individuals can use them.

Do you? Have you ever done an RCT to answer a question?



The Money Illusion

[Note: I had a Libra call yesterday, but I wasn’t happy with my presentation, so I’m not putting it online.]

We often hear that inflation is evil. Look how it destroys the value of your money! Then we are shown some version of this famous illustration:

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You go to a movie theater or get a ski lift ticket, and you say “Geez, I remember when this same ticket cost half this amount.” But you’re forgetting that your take-home pay was even less than half the amount you’re getting today.

That’s the money illusion.

The money illusion is the idea that you can compare today’s dollars with yesterday’s dollars without taking inflation into account. And when you take inflation into account, you must include the fact that your salary has gone up by even more than the amount by which prices have risen.

But then you hear: “Really? Is that true?” Did you know, David Siegel, that Real wages haven’t changed for Americans in decades?”

I have to admit I was worried about this as well. But now we learn that people who say this are also not comparing apples to apples.

Don Boudreau has a short piece on this, and he explains it for us in this video:

Top Takes on Libra

It’s been a wild week in the stablecoin world! The reason is that cryptocurrencies now represent around $200 billion of value, and stable coins should reach $trillions far sooner. It ALL depends on regulators! Here are some top articles from the last week of buzz I think are worth reading:

How Will Facebook’s Libra “Blockchain” Really Work?, by Jameson Lopp (technical and good)

Whitepaper Deep Dive — Move: Facebook Libra Blockchain’s New Programming Language, by Lee Ting Ting

Facebook’s Libra Strategy — Deep-diving the Documents, by Matthew Hine (This is one of the best pieces so far. It’s good technically but misses the Association problem I outline)

The awesome Eric Voorhees Tweetstorm on Libra summarizes it succinctly. (He mentions that the Association will have the ability to stop some transactions, but I don’t really see that happening unless they are somehow forced to do it by some government. It’s a lovely read.)

My Four Big Questions the Libra Team Must Answer, highlighting some of the governance issues.

Barry James’s bid for a “People’s Node” in the Libra network. (Interesting and should exist, but they have to fix the association first.)

I would say the main misunderstandings of Libra are …

  1. Libra is not Facebook

  2. David Marcus is not Mark Zuckerberg

  3. Can we trust Libra? The correct answer is: we don’t know yet. The incorrect answer is: “No!”

  4. Libra isn’t really a currency, it’s a meta-currency

  5. Libra is a threat to central banks. Wrong. Libra will wake up smaller central banks first if their people prefer Libra to their own currency (Gresham’s Law applies). Libra has nothing to do with large currency central-bank policies until it hits at least $1 trillion in value.

  6. Libra’s Association is a Foundation. It isn’t. It’s an investment club. It’s a huge windfall of profit for the investors if regulators don’t kill the project.

  7. Libra is a trojan horse for Facebook to get your financial data.

  8. Libra is bad for cryptocurrencies. Wrong. If there are hundreds of millions of wallets, cryptocurrencies will scale up bigtime. I would say bitcoin is not overpriced at this point.

Preston Brewer, a legal analyst at Bloomberg, has an excellent piece on how Facebook will likely get the SEC to comply with its plan (because the Libra should not pass the Howey test) and outlines other legal challenges. Worth reading.

And, of course, government officials are absolutely clueless, posturing this way and that, hoping to get a bit of media attention to help their next bid for re-election.

I will have another live Libra call this Sunday, the 30th. At noon Eastern US Time I will go through the project and describe it. By around 12:30pm we will start the Q&A and discussion. I will record it and try to get it online that evening.