Link to full article: Thetimes.co.uk
(Background. The Black Swan explains the domain-dependence of expertise: why the electrician, dentist, are experts, while the journalist, State Department bureaucrat, and macroeconomist are not. Since then, there has been a global movement against the pseudo-expert, the serial incompetence of a certain class of babbling and pompous operatives across bureaucrato-academic professions. Which leads to the question: who is the real expert? Who decides on who is and who is not expert? Where is the metaexpert? Time it is. Or, rather, Lindy.)
Lindy is a deli in New York, now a tourist trap, that proudly claims to be famous for its cheesecake, but in fact has been known for the fifty or so years of interpretation by physicists and mathematicians of the heuristic that developed there. Actors who hung out there gossiping about other actors discovered that Broadway shows that lasted, say one hundred days, had a future life expectancy of a hundred more. For those that lasted two hundred days, two hundred more. The heuristic became known as the Lindy Effect.
Click through to read the rest of the post on Medium.
Nassim will present his first medical paper on antifragility on Monday, November 28 at the University of Pennsylvania School of Medicine. On Facebook, Nassim says that this is “basically, a more technical version of the book Antifragile.” He also adds “Note that this is not making any standalone empirical point, rather gluing various phenomena under the convexity argument, with necessary connections (if… then necessarily).”
The Incerto, which comprises Fooled by Randomness, The Black Swan, The Bed of Procrustes, and Antifragile, is now available on Amazon as a box set.
The brokerage firm Alpari sponsors two seminars featuring Nassim, titled “Black Swan: predicting and winning in a world of chaos”, that will take place in Kiev, Ukraine on October 10th, and in Moscow on the 12th.
Nassim appears on Sophie Shevardnadze’s talk show on RT. The transcript of the interview is below:
Sophie Shevardnadze: Professor Nassim Nicholas Taleb, it’s a real pleasure to have you on our show today.
Nassim Taleb: Thank you, I’m honoured to be here. Thanks for inviting me.
SS: You’ve said that there’s no way to control economic cycles and prevent crashes, right? So, basically, I quote: “what we need is citizens to become robust to them and to be immune to their impact”. Now, how does that happen in a real world? How can you make yourself immune?
NT: So, before that, let’s talk about the error of trying to control economic cycles. It’s sort of like doctor trying to micromanage your body temperature. If you take anything organic and you try to control its variability, you’ll end up with less variability than you started with but the system would become more fragile. Take a forest. If you micromanage a forest to try to extinguish every single fire, you’ll end up with a lot of flammable material and you’ll have a forest with no cycles and the first fire you can’t control will destroy your forest. The same thing will happen with the economy. In order to micromanage the cycle… if you have no volatility for a long time, you never have a dip in economic activity – what happens to businesses that are around? You’re going to have a lot of fragile businesses, and so a lot of flammable material, so to speak, and these businesses… we will have more and more of them, okay, and the first crisis that you can no longer control will be vastly deeper than otherwise. So, a little bit of variability in the economy is very healthy.
SS: So, basically, not ever trying to control or prevent a crisis is what makes you immune to the next one?
NT: Exactly. So what do you do is, maybe, manage a big crisis, but the small ones – let things take care of themselves.
SS: Do you have a precise example of, like, when that happened in a country?
NT: Okay. We can talk about the U.S. There’s a fellow called Alan Greenspan, who discovered the business cycles and discovered monetary policy on the job. He came to the Fed in 1980s, around 1987, right before the crisis, the Crash, and he realised that, hey, you can lower rates and inject funds in a system to prevent crisis from happening. In 1987 it was very useful and allowed us to recover from the Big Dip in the stock market – it was the biggest crash ever, a one-day crash. We came back and it was good. So he thought he had the magic formula, and had you given him nature he would eliminate seasons. So he tried to do the same thing – every time the first sign of a problem, he would lower rates. So what happened is that he lowered rates so much that now we have rates at zero, we can no longer lower them, so we lost the effectiveness of that monetary policy. So, not only that, but we can’t bring rates back to their original level because the whole thing may collapse, and everybody is extremely scared of raising back rates. So, here you see what happened – 1987, 1991, 2000, again, 2002, and then again, I think, a couple of times, you know, micro-times after that. So he tried to manage a cycle. The way you should do things is be there for big problems, and let small things to care of themselves.
SS: I just came out of your lecture and you’ve said something very interesting that if you had a choice, you’d rather invest in Russia than Saudi Arabia. Now, for many that sounds odd. Can you explain why?
NT: Let me explain that idea. The first thing, I’ve always hesitated to talk about the investments I don’t have.
SS: But you’re a philosopher, so you can talk about that.
NT: My scheme in the game ethics force me to have something at risk – and currently I have nothing at risk in Russia, but thank God, nothing at risk in Saudi Arabia. Let me explain the point – if you look at history, you’ll realise that companies, countries, entities -let’s call them entities – that have sustained more trauma in this recent history, like a stock that bounces back, goes through hell and comes back, generally have given us information about how much heat they can sustain. So, on that account, the fact that Russia went through the problems of early 1990s shows that the system can handle a huge drop in economic activity, a huge rise in unemployment, severe disruptions in the institutional structure without falling apart. Now we have that, okay. We have that evidence. Russia has things that Saudi Arabia doesn’t have, okay. Iran, for example, compared to Saudi Arabia – these countries can take a lot of volatility. So, I’m much more comfortable in a place like that because I know that social order is not likely to collapse, no matter what happens. Saudi Arabia – I don’t know if SA can go through the dip in oil prices, I don’t know what would be the catalyst, but I know that eventually something is going to collapse.
SS: But then there’s another problem – Russia, like you’ve pointed out, has gone through so much chaos and crises and volatile situations that we really built our anti-fragility gene and we have a very high ability to adapt.
NT: I don’t think Russia is antifragile, yet. I think it is robust.
SS: Let’s talk about that, because I feel like we know how to adapt but then this sort of high-ability to adapt has in some way turned into…
SS: Indifference, yeah. Like, instead of tackling the problem you’re just accepting it, basically. How do you overcome that?
NT: It is a problem of Russia. If you take countries like what you see in South East Asia and places like that, these countries went through shocks and they bounce back with a vengeance. They came back with a vengeance. We know that China will have a problem, it will be some kind of economic problem that will lead to some kind of restructuring, maybe organic restructuring, and China will bounce back and impress everyone again. Now, Russia, you have two or three things hindering Russia. Let’s talk about them. When people talk about Russia – I don’t care about geopolitics, geopolitics has absolutely nothing to do with anything – what matters are two things. First one, you need a huge pool of middle-sized companies. You can retain your structure, you need a lot of small companies, the middle market that really made Germany. The second one you need to find ways to stop your brain-drain. In my field – probability theory – out of the 20 top names maybe 14 are Russian – I mean, this is mind-boggling. No field is dominated by any nationality to that extent. So, you don’t see that many Germans, you see few French people, you don’t see that many people from the UK. So where’s that talent? That talent is being exported, right? So that’s the problem of Russia, Russia needs to deal with its brain-drain and with middle-size companies. Now, maybe they’re connected, maybe you can find ways to encourage people to stay, I don’t know, give them some emotional support – whatever. I don’t know how these things are done, but I am saying that this is a problem of Russia.
SS: We keep bringing up China – is it a worrying sign that there are some real problems in Chinese economy, what would that mean for the world economy?
NT: What you’re saying is journalistic, and let me explain why. I’m going to give you a bit of hard time.
NT: You can frame things the way you want – I can look at the story of China, I don’t have the exact numbers, and I can tell you “Well, in the past 10-15 years it rose by that amount”, but I can look at the losses of the past two weeks – right. So, it depends on how you frame China. You’ve got to see where they started, where they’re now. If you frame it journalistically, people are going to take the most sensational. But journalists don’t invest, and investors don’t work for newspapers… So let’s frame it properly – I see zero problem in China so far. I am much more worried about things that have been fuelled by low interest rates, like the U.S., where we created inequality with economic policy that lowered rates monstrously, so assets went up disproportionately, stock market, real estate and luxury areas, and not for the regular American family. This I am more worried about and I am more worried that we may have a more severe effect from a drop in asset prices in U.S. Plus, we have to realize that the Chinese stock market is huge and the other thing for the rest of the world is that the Chinese don’t buy stuff from the rest of the world.
SS: We buy from them.
NT: I mean, I computed that the entire U.S. export to China is less than what’s sold in Walmart – I mean, it’s still substantial, but it’s few points, one point of GDP or two points… It’s more of a psychological thing than a practical thing. On the other hand, a slowdown in the U.S. would affect the Chinese big time. So you’ve got to worry about the U.S., not about China, if you’re concerned about China.
SS: I want to talk to you about debt. You’ve said that debt actually causes wars, it’s never good, it’s never accumulated in moderation – well, today the world is suffering from vast debt. If you compare it to 2007 it has accumulated $57 trillion. Countries like China and America that we’ve been talking about – how are they going to deal with their debt? Austerity, more borrowing, maybe defaulting? What’s your take.
NT: That’s what worries me. Governments engage in debt. Why? Not because governments ever say “okay, economic policy means we going to have to borrow” – the point is that, the French government, for example, I think when I wrote “The Black Swan” I looked at 53 out of 54 years – they’ve underestimated their deficit, okay. That’s where debt comes in. So it’s something that civil servants who don’t understand, who underestimate uncertainty, have to face, when they raise what they can’t raise via taxation they raise via debt, and try to inflate things out of the system. So this is why debt is not very good. Now, if you read economic textbooks, they give you some models in which debt works, but then if you put the meta-model on top, much more rigorous analytically and takes into account model errors, then you realise that debt compounds all these problems that you have, beyond certain small amount to help families. This is why debt is not a good thing. Where we are now today? The crisis, the debt crisis, had the huge rise in debt, again, for businesses that do not necessarily need that debt, that’s a speculative thing, and they kept going. After the crisis we had a lot of borrowers who were not… you know, shouldn’t be borrowing. Who paid the price? The taxpayers. How? Because private debt was transformed via magic wands into public debt. That’s not good, you see.
SS: So what now?
NT: Now that we have a lot of debt, we’re facing situation where shrinking the debt would cause a huge contraction of economic activity. That is where we’re facing problems, so my point is that we should educate people, to undo all this debt we need education, we need to send the message that debt is not good. We can give them examples that Microsoft wasn’t built on debt, Apple is not built on debt. Name a company that is successful and let’s look at its debt history, and name a country that was successful, or the phase when it was successful and let’s look at its debt history , and you can realise that debt is something that economists like to promote simply because it’s good for civil servants, that’s it. So educate people to avoid debt.
SS:You have a recurring theme of forecast and how it’s silly to actually base your predictions analysing history or economics because it gives you a false illusion of knowing the world that you live in today. So, if you can’t really analyse your mistakes, right, you can’t analyse history – is there really no effective way to measure risks to predict the outcome, ever?
NT: Of course, you can. That was my lecture, that’s my last book, that’s everything I’ve done – I keep explaining that, this, I know, is very fragile, I know what would break it, but I cannot forecast with precision when it would break. But I know this is breakable, much more breakable than a styrofoam cup. So, we know that, we know which companies are likely to go bust, so we can measure fragility. You cannot predict the event, but you can say that company with debt for example cannot sustain the stress that the company without debt would be able to go through. Decentralised system can withstand shocks a lot better than a centralised system. Organic, self-organised system – and that’s complexity theory – let’s take the restaurant business, which is the ideal business. Have you ever had a restaurant crisis in the West?
SS: Nowhere, not only in the West.
NT: Nowhere. So, government doesn’t have to bail out restaurants. Why? Because it’s… it seems disorganised, but it’s very well organised, a self-organised system, in which people make their mistakes and go bust early, you see. And then they can start again, and consumers have the optimal price almost all the time, except in form of a payup, and you don’t have bailouts, you don’t have a generalised restaurant crisis, not like the banking crisis or not like a car-maker crisis. This system is not based on prediction, this system operates in a way that is organically very stable. Nature does not predict, what nature does is focus on robustness, and the metaphor I’ve used in the past is that nature or God – depends on your theology – gave you two kidneys. You don’t need two kidneys, you can operate perfectly on one, except that second kidney allows us to not have to predict the environment, so you don’t have to know exactly what will cause you to lose the kidney. So, it’s the same thing with corporations. If you have a buffer, some layers of redundancy, you can withstand economic shocks and you don’t have to hire some economist. And then let’s look at the track record of people in prediction – zero! Let’s go back to the point of forecasting, let me summarise – forecasting is the province of the charlatans. Unless it’s done properly. You can say rigorously that “this is fragile”, “this bridge is going to collapse”, instead of predicting what and where and who. You can build a better bridge. And forecasting makes you fragile because those who forecast develop overconfidence about the future and start to engage in debt and other risky activities. Look at track record of forecasting – pitiful.
SS: Can I ask you something – to what extent should we be accepting our fate?
NT: I’ve spoken to a lot of people. A lot of people, a lot of successful people, a lot of unsuccessful people. It seems to me that those who have the best control of their environment are those who think that the environment is more random. And those who think that it’s all, you know, we can get the cause and effects and we can see everything, there’s no opacity – these people are the ones who fail. It’s very strange that those who succeed are those who control randomness the best by accepting that it’s there and working the best around the corners of things that are predictable and that other people are not predicting. You see, because, there are some pockets of predictability, I know that something very fragile is going to collapse. If you accept that unpredictability, then you engage in tinkering, so that when you’re wrong it will cost you little and when you’re right, it will make you a lot. It’s not what happened to the world that counts, it’s your strategy of minimising shocks from random events and opening up for the good randomness.
SS: Talking about “Black Swans”, I mean, we live in a very fast-paced world, and it’s very unpredictable. Do you feel like because everything’s happening so fast, there’s going to be more and more “black swans” to come? Are we to expect more of them?
NT: The only thing that’s happening today in our world is that we have more connectedness, therefore things can happen much faster than before. I wouldn’t worry about one central thing, because it’s not just money, it’s viruses, viral bacteria, germs.
NT: For example. I was very depressed when I saw reaction to Ebola.
SS: Because that’s more dangerous than spread of ISIS, in your opinion?
NT: That’s much more dangerous than anything because it multiplies, it multiplies very quickly. The world has had one plague and today everything seems under control. ISIS is definitely not the danger. They’re good on Youtube, but it’s not… the Millennials, if you’re talking about indigo generation, the millennials seem to care more about what happens on Youtube than reality, alright, and hopefully that would change through selection, but the worry is not Al-Qaeda, it’s not these guys. These guys are less dangerous than… it’s a fraction of suicides in the West, it’s a fraction of people killed falling from ladders – it’s nothing. The point that you have to worry about is the fact that the plague was travelling at the speed of 30 miles a day, maximum speed. Today, what is it?
SS: We don’t know, where’s Ebola now?
NT: Tomorrow I go back to New York and I’m going to be travelling several thousand miles in 10 hours… So, you realise, it will multiply much faster. So, Ebola was not the problem, but something similar… And what depressed me was the reaction by people, the complacency. They don’t realise that something like that needs to be systematically stopped at its source. You see, you don’t wait for things to multiply. Lucky countries like Singapore, places like that, they understand the point, but we need a little bit more active management of… you know, we need to sit down and say – ok, what if we have another ebola, how do we manage it? The journalists are using what I call “naive empiricism” of comparing it to other bigger diseases – yeah, but cancer is not doubling every week. You don’t have to worry, it’s not an epidemic, it’s just something that we have. Diabetes kills a lot of people, but the odds of that number changing hugely from year to the next are very small. These things are locally predictable. But Ebola has much higher degree of unpredictability because of what I call the “extreme strain” the “fat tail”, so this is a Black Swan domain, the Black Swan territory and we have to take it seriously. So, not Ebola, other things like that, we’re not equipped today. Tomorrow, if there’s an emergency like Ebola, you know… We have a very well organised system to prevent terrorists from travelling, you know, everybody’s blocking them and cooperates, but we don’t have the same level of cooperation to immediately stop the spread of something of this sort. That worries me because, one thing, antibiotic resistance is serious.
SS: Do you have power in you for one more question?
NT: Yeah, of course.
SS: I know that “Black Swan” has been huge. A lot of world leaders love your book. If politicians were to embrace your thinking, what would that mean for politics?
NT: I don’t pay attention to politicians. I am blunt about it because I think that politicians play a smaller role than you think. Politicians are more like actors put on a job and then they respond via polling, the environment and stuff like that. I don’t pay attention to politicians, I pay attention to… the structure of political life, unfortunately, has not been very adapted to the nature of the complex system that we have. So, I don’t pay attention to politicians at all. For me they don’t exist. It’s a parallel world and I don’t want to be part of it, I don’t want to go to Davos, I don’t want to do this or do that, I don’t want to advise anyone, I don’t want to be advised by anyone. It’s a separate world for me.
SS: Alright, hopefully, they will listen more to you, because I still live in a world where politics decide a lot of things. Thank you very much for this interview, it’s been a pleasure.
On Medium, Nassim posts a continuation of his previous article on the Minority Rule:
Let us take the idea of the last chapter [the intransigent minority’s disproportional influence] one step further, get a bit more technical, and generalize. It will debunk some of the fallacies we hear in psychology, “evolutionary theory”, game theory, behavioral economics, neuroscience, and similar fields not subjected to proper logical (and mathematical) rigor, in spite of the occasional semi-complicated equations. For instance we will see why behavioral economics will necessarily fail us even if its results were true at the individual level and why use of brain science to explain behavior has been no more than great marketing for scientific papers.
Consider the following as a rule. Whenever you have nonlinearity, the average doesn’t matter anymore. Hence:
The more nonlinearity in the response, the less informational the average.
For instance, your benefit from drinking water would be linear if ten glasses of water were ten times as good as one single glass. If that is not the case, then necessarily the average water consumption matters less than something else that we will call “unevenness”, or volatility, or inequality in consumption. Say your average daily consumption needs to be one liter a day and I gave you ten liters one day and none for the remaining nine days, for an average of one liter a day. Odds are you won’t survive. You want your quantity of water to be as evenly distributed as possible. Within the day, you do not need to consume the same amount water every minute, but at the scale of the day, you want maximal evenness.
The effect of the nonlinearity in the response on the average –and the informational value of such an average –is something I’ve explained in some depth in Antifragile, as it was the theme of the book, so I will just assume a summary here is sufficient. From an informational standpoint, someone who tells you “We will supply you with 0ne liter of water liter day on average” is not conveying much information at all; there needs to be a second dimension, the variations around such an average. You are quite certain that you will die of thirst if his average comes from a cluster of a hundred liters every hundred days.
Note that an average and a sum are mathematically the same thing up to a simple division by a constant, so the fallacy of the average translate into the fallacy of summing, or aggregating, or looking at collective that has many components from the properties of a single unit.
As we saw, complex systems are characterized by the interactions between their components, and the resulting properties of the ensemble not (easily) seen from the parts.
There is a rich apparatus to study interactions originating from what is called the Ising problem, after the physicist Ernst Ising, originally in the ferromagnetic domain, but that has been adapted to many other areas. The model consists of discrete variables that represent atoms that can be in one of two states called “spins” but are in fact representing whether the state is what is nicknamed “up” or “down” (or can be dealt with using +1 or −1). The atoms are arranged in a lattice, allowing each unit to interact with its neighbors. In low dimensions, that is that for every atom you look at an interaction on a line (one dimensional) between two neighbors one to its left and one to its right, on a grid (two dimensional), the Ising model is simple and lend itself to simple solutions.
One method in such situations called “mean field” is to generalize from the “mean”, that is average interaction and apply to the ensemble. This is possible if and only if there is no dependence between one interaction and another –the procedure appears to be the opposite of renormalization from the last chapter. And, of course, this type of averaging is not possible if there are nonlinearities in the effect of the interactions.
More generally, the Übererror is to apply the “mean field” technique, by looking at the average and applying a function to it, instead of averaging the functions –a violation of Jensen’s inequality [Jensen’s Inequality, definition: a function of an average is not an average of a function, and the difference increases with disorder]. Distortions from mean field techniques will necessarily occur in the presence of nonlinearities.
What I am saying may appear to be complicated here –but it was not so with the story of the average water consumption. So let us produce equivalent simplifications across things that do not average.
From the last chapter [Minority Rule],
The average dietary preferences of the population will not allow us to understand the dietary preferences of the whole.
Some scientist observing the absence of peanuts in U.S. schools would infer that the average student is allergic to peanuts when only a very small percentage are so.
Or, more bothersome
The average behavior of the market participant will not allow us to understand the general behavior of the market.
These points appear clear thanks to our discussion about renormalization. They may cancel some stuff you know. But to show how under complexity the entire field of social science may fall apart, take one step further,
The psychological experiments on individuals showing “biases” do not allow us to understand aggregates or collective behavior, nor do they enlighten us about the behavior of groups.
Human nature is not defined outside of transactions involving other humans. Remember that we do not live alone, but in packs and almost nothing of relevance concerns a person in isolation –which is what is typically done in laboratory-style work.
Some “biases” deemed “irrational” by psycholophasters interested in pathologizing humans are not necessarily so if you look at their effect on the collective.
What I just said explains the failure of the so-called field of behavioral economics to give us any more information than orthodox economics (itself rather poor) on how to play the market or understand the economy, or generate policy.
But, going further, there is this thing called, or as Fat Tony would say, this ting called game theory that hasn’t done much for us other than produce loads of verbiage. Why?
The average interaction as studied in game theory insofar as it reveals individual behavior does not allow us to generalize across preferences and behavior of groups.
Groups are units on their own. There are qualitative differences between a group of ten and a group of, say 395,435. Each is a different animal, in the literal sense, as different as a book is from an office building. When we focus on commonalities, we get confused, but, at a certain scale, things become different. Mathematically different. The higher the dimension, in other words the number of possible interactions, the more difficult to understand the macro from the micro, the general from the units.
Or, in spite of the huge excitement about our ability to see into the brain using the so-called field of neuroscience:
Understanding how the subparts of the brain (say, neurons) work will never allow us to understand how the brain works.
So far we have no f***g idea how the brain of the worm C elegans works, which has around three hundred neurons. C elegans was the first living unit to have its gene sequenced. Now consider that the human brain has about one hundred billion neurons. and that going from 300 to 301 neurons may double the complexity. [I have actually found situations where a single additional dimension may more than double some aspect of the complexity, say going from a 1000 to 1001 may cause complexity to be multiplied by a billion times.] So use of never here is appropriate. And if you also want to understand why, in spite of the trumpeted “advances” in sequencing the DNA, we are largely unable to get information except in small isolated pockets of some diseases.
Understanding the genetic make-up of a unit will never allow us to understand the behavior of the unit itself.
A reminder that what I am writing here isn’t an opinion. It is a straightforward mathematical property.
I cannot resist this:
Much of the local research in experimental biology, in spite of its seemingly “scientific” and evidentiary attributes fail a simple test of mathematical rigor.
This means we need to be careful of what conclusions we can and cannot make about what we see, no matter how locally robust it seems. It is impossible, because of the curse of dimensionality, to produce information about a complex system from the reduction of conventional experimental methods in science. Impossible.
My colleague Bar Yam has applied the failure of mean-field to evolutionary theory of the selfish-gene narrative trumpeted by such aggressive journalists as Richard Dawkins and Steven Pinker and other naive celebrities with more mastery of English than probability theory. He shows that local properties fail, for simple geographical reasons, hence if there is such a thing as a selfish gene, it may not be the one they are talking about. We have addressed the flaws of “selfishness” of a gene as shown mathematically by Nowak and his colleagues.
Hayek, who had a deep understanding of the properties of complex systems, promoted the idea of “scientism” to debunk statements that are nonsense dressed up as science, used by its practitioners to get power, money, friends, decorations, invitations to dinner with the Norwegian minister of culture, use of the VIP transit lounge at Kazan Airport, and similar perks. It is easier to take a faker seriously, since science doesn’t look neat and cosmetically appealing. So with the growth of science, we will see a rise of scientism, and my general heuristics are as follows: 1) look for the presence of simple nonlinearity, hence Jensen’s Inequality. If there is such nonlinearity, then call Yaneer Bar Yam at the New England Complex Systems Institute for a friendly conversation about the solidity of the results ; 2) If the paper writers use anything that remotely looks like a “regression” and “p-values”, ignore the quantitative results.
Discussing the concept of antifragility, Nassim remotely addresses the Theory of Constraints International Conference held in Capetown South Africa from September 6th to 9th, 2015. The theme of the conference was how to use the Theory of Constraints to transform organizations and people from fragile (harmed by volatility) to robust (not harmed by volatility) to antifragile (benefiting from volatility).
This video is of Nassim’s keynote address at this year’s Fletcher Conference on Managing Political Risk.
The conference’s website provides the text of some of the address, as follows:
Keynote address by Nassim Nicholas Taleb
Nadim Shehadi, moderator
Let’s start with the notion of fat tails. A fat tail is a situation in which a small number of observations create the largest effect. When you have a lot of data, and the event is explained by the smallest number of observations. In finance, almost everything is fat tails. A small number of companies represent most of the sales; in pharmaceuticals, a small number of drugs represent almost all the sales. The law of large numbers: the outlier determines outcomes. In wealth, if you sample the top 1% of wealthy people you get half the wealth. In violence – a few conflicts (e.g. World Wars I and II) represent most of the deaths in combat: that is a super fat tail.
So why is the world becoming more and more characterized by fat tails? Because of globalization. More “winner takes all” effects. You have fewer crises, but when they happen they are more consequential. And the mean is not visible by conventional methods.
Now, moral hazard. Banks like to make money. Under fat tails, large numbers operate slowly. Let’s say you get a bonus for each year you make money. Then in 1982, banks lost more money than they did in their history. Then in 2007-2008, $4.7 trillion were lost. Then bankers wrote letters about how the odds were so low that the event was as much of a surprise to them as it was to you. Any situation in which you see the upside without the downside, you are inviting risks. People will tell you something is very safe, when in fact it is dangerous. Visible profits, and invisible losses. People are getting bonuses on things that are extremely high risk. And then the system collapses.
If you have skin in the game at all times, this does not happen. Modernity: a situation in which people get benefits from the action, but the adverse effects do not touch them. You hide risks to improve your year end job assessment. Bear Stearns never lost money – until they lost money.
Hedge fund managers are forced to eat their own cooking. When the fund loses money, the hedge fund manager loses his own money: he has skin in the game. You have fools of randomness, and crooks of randomness. Driving on a highway, you could go against traffic and kill 30 people – why does that not happen more often? Because types of people who would do this kill themselves along with others, so they filter themselves out of the system. Entrepreneurs, who make mistakes, are effectively dead if there is a filtering system. Suicide bombers kill themselves – so we can’t talk about them as a real threat to the system. So there is a filtering mechanism. People don’t survive high risk. If they have skin in the game, traders don’t like high risk.
Let’s now talk about fragility. The past does not predict the future. The black swan idea is not to predict – it is to describe this phenomena, and how to build systems that can resist black swan events. We define fragility as something that does not like disorder. What is disorder? Imagine driving a car 50 times into a wall at 1 mph, and then once at 50 mph: which would hurt you more. So there is an acceleration of fragility. The goal is to be anti-fragile.
There are two types of portfolios: 1) if there is bad news you lose money, 2) if there is bad news you win money. One doesn’t like disorder, one likes disorder. One is fragile, one is anti-fragile. Size (such as size of debt, size of a corporation) makes you more fragile to disorder.
Questions & Answers:
- Do the people of ISIS returning home pose a risk?
- This is not a risk. Debt is a risk. ISIS makes the newspapers and people talk about it but the real risks are not ISIS – the real risk is ebola, because it can spread. And the next ebola will be worse. So when people ask me to talk about risk, an epidemic is the biggest risk.
- Can you discuss some examples in the world that are fragile, examples of the fat tail?
- The Soviet Union did not collapse because of the regime but because of the size. Similarly, a lot of people don’t fully understand the history of Italy, before unification. There was constant, low grade turmoil. After unification, there were infrequent but deep problems. The risks facing us today, are the real things that can harm us and spread uncontrollably.
- Should we still think about risks on a country level? How do we think about transnational risks?
- Cybersecurity – banks spend 5% of their money on it. Netflix engineers failures every day. They pay an army of agents to try to destroy their system, to discover their vulnerabilities. Things that experience constant stress are more stable. In cybersecurity, there are a lot of risks, but we’re doing so much to protect against it that we don’t need to worry much. But eventually the cost of controlling these risks might explode.
- What is your blind spot?
- If I knew my blind spots, they wouldn’t be blind spots. I’m developing something that is improving stress testing. The good thing about fragility theory is you can touch a lot of things. I want to make narrow improvements, little by little, not try to save the world.
- Is statistics useless or are there some redeeming qualities?
- Any science becomes applied mathematics and if it’s not applied mathematics yet, it is not a science. Stats is used mechanistically. Statisticians need to make risk an application of probability theory. A lot of the people doing this come from the insurance industry.
- How does bad data effect your work?
- When you have a lot of variables, but not much data per variable, you are more likely to have spurious correlations. And when you have a lot of data, you are likely to find a stock figure that correlates with your blood pressure – that’s spurious. More data is not always good.
- Another problem is that if I want to write a paper, I test, test, test something until it fits my expectations – and I won’t reveal to you how many times I have tried. If there is someone doing this for a living, for money, then I don’t trust them.
- This is a great system you’re developing but can it be misused?
- The problem is in the math and in the ethics.
- If we stop using statistics, how can we make decisions? Don’t we have to make assumptions?
- Have skin in the game. Only use statistics for decisions if the stats are reliable. Joseph Stiglitz is blocking evolution – he made a prediction about Fannie Mae not collapsing, and it collapsed – and yet he’s still lecturing us on what to do next.
FightMediocrity on the notion of antifragility in this animated short.