Weekend Reading & Selected Links
Happy weekend! Here are some links to things I've been reading, watching or listening to that you might also enjoy:
- My new podcast conversation, with Nassim Taleb. At the bottom of this email, I've reprinted five excerpts from the conversation.
- Nassim confirms that a transcript of our conversation will appear as a non-technical appendix in his technical book Statistical Consequences of Fat Tails.
- Foundations: Why Britain has stagnated, the new report by Ben Southwood, Samuel Hughes and Sam Bowman.
- Toby Ord dissects a black hole.
- What is Entropy?, a new short book by John Baez.
- 'Why to not write a book', a recent Gwern post.
- Terry Tao's notes on the foundations of probability theory.
- The new metascience podcast series, by the Institute for Progress.
- Mike Muthukrishna responds to Robin Hanson's response to my interview with Rob Boyd & Pete Richerson.
Have a great weekend,
Joe
Excerpts from my podcast with Nassim Taleb
1. On Churchill as the reverse-Napoleon
WALKER: Actually, on that, it's funny to think that Winston Churchill probably would have had a terrible Brier score. He was wrong on all these questions like the gold standard, India, Gallipoli... He was wrong on all these calls, but he was right on the big question of Hitler's intentions. So he was right in pay-off space, when it mattered.
TALEB: In pay-off space, when it mattered. Yeah, he was wrong in the small... You lose a battle and win the war. It's like the reverse of Napoleon.
Napoleon was only good at winning battles.
And he won, I don't know if numerically, look at how many battles he won.
WALKER: He did pretty well.
TALEB: He did well except for Waterloo.
WALKER: The reverse Churchill.
TALEB: Yeah, the reverse Churchill. And he's hyped up because they’ll say look how many battles he won. They were insignificant maybe compared to the rest. And after a while, actually, he stopped winning them. It became harder because people learnt from him.
So there's one thing … frequency space is a problem, because in the real world you're not paid in frequency, you're paid in dollars and cents.
WALKER: Yeah. It reminds me of that anecdote in Fooled by Randomness, the trader, who I assume is you, is simultaneously bullish on the market going up over the next week, but also short the market.
TALEB: Yeah, that was the one I was explaining. In frequency space, I'm bullish, and in pay-off space I'm bearish.
2. On Danny Kahneman
TALEB: As you advance in age, you go back. If you're doing things right, you go back to your childhood values. Your childhood values were about honour and taking a stand when needed. And then you continue. And every time I take a stand, I feel it's existential. I feel I've done something.
But what I'm saying is that Danny [Kahneman] doesn't have the same representation. And someone complained about him, among his circle of friends, jokingly. He said, “For me, happiness has a different value. For Danny, it’s eating mozzarella in Tuscany.” That's his idea of … you know, it’s hedonic. Therefore, he analysed everything in terms of the hedonic treadmill.
But I'm sure deep down Danny was not like that. He realised that was not what life was about.
WALKER: Yeah. It's more about goals and aspirations and values.
TALEB: Maybe. But he was an atheist. You know that. And the first time I met him, he ate prosciutto. I told him: “Prosciutto?” He said, “There's not a single religious bone in my body.” So then I realised that this is a different customer.
And when you're not religious, there's a lot of good things, but there could be bad things … You're too materialistic about your view of the world, and you're coming here to maximise mozzarella and prosciutto. It's very different.
3. The secret of Fooled by Randomness
TALEB: Okay so let me tell you the secret of Fooled by Randomness.
WALKER: Okay.
TALEB: I wrote Fooled by Randomness and it became very successful in the first edition. And it had no references. And it had no behavioural science, aside from how humans don't understand probability. Minimal of that.
Then I met Danny Kahneman in 2002.
WALKER: In Italy.
TALEB: In Italy. And then, okay, I spoke to him. He said, you don't have a lot of references for stuff like that and a lot of comments. So I said, no problem. So I went and I got about 100 books in psychology. I read them over a period of, say, six months. I went through the corpus, everything, figured out. You know, they think that their maths is complex; their maths is trivial—and wrong.
And then I cited and I remodelled prospect theory. Because prospect theory itself, because it is convex-concave, it tells you itself that if you're going to lose money you take a big lump. It's more effective to make money slowly because people like to make a million dollars a day for a year, rather than 250 million and then nothing, okay? But it’s the reverse for losses. And there are a lot of things in it that's correct. So I like that aspect.
So anyway, I start putting references on sentences I've written before, not knowing anything about it, which was not the most honest thing but it was to link my ideas to that discipline. It's not like I got the ideas from these books. I got the ideas and then found confirmation in these books.
Then I met Danny. From the very first time I told him, “Your ideas don't work in the real world because they underestimate people. In the real world, they underestimate the tail event, whereas in your world they overestimate it. But there's a difference that in the real world you don't know the odds and you don't know the pay-off function very well. In your world, you know the odds and the pay-off function.”
So he liked the fact that I gave him a break in that sense, and still used his prospect theory, because the idea that the lost domain is convex, I like the idea. But by then I knew enough about the psychology literature and about all these decision-making theories. So by then I built myself a knowledge of that. I revised Fooled by Randomness. I put a section in the back connecting my ideas to that literature.
And then they started liking it in the world. Robert Shiller didn't like it. He said, “You had a great book. It was genuine. Now you have an academic tome.” That was Shiller. But the other people liked it.
4. On forecasting, and what he learned from Mandelbrot
WALKER: Alright, well, let's talk about forecasting...
If you had to boil it down, how would you describe the substantive disagreement between you and the broad intellectual project of superforecasting? Is it just about binary versus continuous pay-offs?
TALEB: Yeah... So the first one is binary versus continuous. And I knew that as an option trader, that the naive person would come in and think an out of the money binary option would go up in value when you fatten the tail. In fact, they go down in value when you fatten the tail, because a binary is a probability. So, just to give you the intuition, if I take the Gaussian curve, plus or minus one sigma is about 68 per cent. If I fatten the tail, exiting, in other words, the probabilities of being above or below, actually they drop. Why? Because the variance is more explained by rare events. The body of the distribution goes up.
WALKER: Yeah. The shoulders narrow.
TALEB: Exactly. You have more ordinary, because you have higher inequality, and the deviations that occur are much more pronounced.
So in other words, you're making the wrong bet using binary options or using anything that clips your upside. That we knew as option traders. And rookies usually, or people who are not option traders, sometimes PhD in economics or something, they always express their bet using these, alright? And we sell it to them, because it's a net of two options.
And there's a difference between making a bet where you get paid $1 and making a bet where you get paid a lot. And in Fooled by Randomness, I explained that difference by saying that I was bullish the market, but I was short. How? Well, I was bullish in a sense. What do you mean by bullish? I think the market had a higher probability of going up, but the expectation being short is bigger.
So these things don't translate well outside option trading. And of course, these guys don't get it in forecasting. The other one is they sub-select events that you can forecast, but they're inconsequential.
WALKER: They're very small, restricted questions?
TALEB: They're inconsequential. And also, they’re events. There's no such thing as an event. Like, for example, will there be a war, yes or no? I mean, there can be a war. Could kill two people. There could be a war that kills 600,000 people.
So in Extremistan that's one thing, one sentence Mandelbrot kept repeating to me: There is no such thing as a standard deviation in Extremistan. So you can't judge the event by saying there's a pandemic or no pandemic, because the size is a random variable.
Let me give you an example. If you have scale—that's the idea of having scale free distribution versus no scale—the ratio of people with $10 million over people with $5 million is the same as the ratio, approximately, of $20 million over $10 million.
WALKER: This is a Pareto.
TALEB: That's a Pareto. It's almost how you define it. But look at the consequences of that. The consequences of that … it tells you that there's no standard event.
WALKER: Right. There's no typical event.
TALEB: Exactly. No typical event. You cannot say there’s a typical event. No large deviation.
So, to give you an idea, if I take a Gaussian, the expected deviation above three sigma is a little more than three sigma. And if you take five sigma, it's a little more than five sigma. It gets smaller. It's above zero sigma. It's about 0.8 of a sigma. As you go higher, it shrinks. It's like saying, what's your life expectancy? At zero it's 80 years old, but at 100 it's two years – two additional years. So as you increase the random variable …
Whereas in Extremistan, the scale stays the same. So the expected life, if we were distributed like company size, the expected company, as I said, what's the expected company higher than 10 million in sales? 15 million. 100 million in sales? 150 million. The average. Two billion in sales? 3 billion. Alright. So it's the same as saying, “Oh, he's 100 years old? He has another 50 to go.” “He's 1000 years old, another 500 to go.” You can't apply the same reasoning with humans. We know what an old person is. Because as you raise that number, things shrink. For Extremistan, you raise that number, things don't shrink, as a matter of fact: proportionally they stay the same, but in absolute they explode.
So this is why that explosion tells you that there's no standard large deviation. And that was Mandelbrot's sentence.
And just looking at the world from that standpoint, that there's no characteristic scale, changed my work better than the crash of ’87, because now I had a framework that is very simple to refer to, and they are probably basins.
So this is why I learnt a lot working with Mandelbrot. And people weren't conscious of that stark difference, operationally. Hence, I wrote the book Statistical Consequences of Fat Tails. And this is why I dedicated The Black Swan to Mandelbrot, based on that idea, that characteristic scale, that I explained in The Black Swan.
If you use that, then you have a problem with forecasting, because it is sterile in the sense that what comes above has a meaning. Is it higher than 10 million? Higher than 100 million? It has a meaning.
5. On venture capital
WALKER: I have a question about venture capital, but it perhaps has broader applications. There's a kind of inconsistency I noticed. So, on the one hand, as a consequence of the power or distribution of returns, one recommendation to, say, public market investors is they may want to pursue a barbell strategy, which you've written about. So say you have 90 per cent of your portfolio in very safe things like bonds, and then with 10 per cent you take lots of little speculative bets to maximise your optionality. The same logic could also be pursued by, say, book publishers, where, because the success of books is power law distributed, you might want to take lots of little bets to maximise your chances of publishing the next Harry Potter.
On the other hand, I've heard venture capitalists reason from the exact same premises—the power law distribution of start-up success—but come to an opposite conclusion, which is that they want to concentrate their bets really heavily in a handful of companies.
TALEB: Because the way you need to look at venture capital is that it's largely a compensation scheme. Largely like hedge funds: a compensation scheme.
WALKER: The 2 and 20?
TALEB: No, no, the mechanism. They don't make their money, venture capitalists, they don't make money by waiting for the company to really become successful. They make their money by hyping up an idea, by getting new investors and then cashing in as they're bringing in new investors. I mean, it's plain: look at how many extremely wealthy technology entrepreneurs are floating around while not having ever made a penny in net income. You see? So the income for venture capital comes from a greater fool approach.
WALKER: Okay, so a Ponzi kind of dynamic?
TALEB: Not necessarily Ponzi, because you're selling hope, you package an idea, it looks good, so you sell it to someone, and then they have a second round, a third round. They keep [doing] rounds so you can progressively cash in.
WALKER: Got it.
TALEB: It's not based on your real sales. Or your real cash flow. Particularly in an environment of low interest rates, where there was no penalty for playing that game.
WALKER: Do you think there's any skill in VC?
TALEB: They have skills, but most of their skills are in packaging. Not in …
WALKER: Not for the things people think.
TALEB: Exactly. Packaging, because they're trying to sell it to another person. It's a beauty contest.
WALKER: The Keynesian beauty contest.
TALEB: So they package a company. And look at the compensation of these venture capitalists. You can see it. I mean, either you have financing rounds where someone cashes in at high price or you have an initial public offering.
I come from old finance, old-school finance where you haven't really succeeded until the company gets a strong cash flow base.