For thousands of years philosophers have debated the essence of morality. Now, neuroscientists may have answers.
We ponder our insignificant place in the universe, and boldly go after stories of romance & cynicism in Outer Space.
What happens when there is no leader? We look at the bottom-up logic of cities, Google, and even our brains.
Like the De Vany podcast, I’m again a bit annoyed by this flat out dismissal of running as a valuable form of exercise.
It is widely understood that to improve times at any distance (which is presumably what runners aim to do - get better) some level of speedwork is required.
To hold "jogging" out there as a one size fits all non-diversified type of workout is a straw man.
Take anyone who has done even one month of consistent running (w/ proper technique to avoid injury), and I’m quite sure most people will say
1) they can run faster at the end of one month, and are on occasion tempted to push themselves hard on days they are "feeling good" (something that is only possible once you clear the hurdle of "omg, i’m running and this is horrible", which comes w/ time) and
2) they generally feel healthier and have improved mood.
To illustrate the wide level of variation in running, check out Hal Higdon’s website for training schedules for various races (from my experience, most runners are typically training for one type of race or another): http://www.halhigdon.com/ (I’m not at all affiliated with Higdon, but his training schedules are very widely read.)
Also, consider the popularity of fartleks (http://en.wikipedia.org/wiki/Fartlek) and "indian running" (http://thehighschoolrunner.com/2007/08/indian-runs.html).
The importance of variation is nothing new to runners.
Also, I would like to hear De Vany and Taleb specifically address the practice of persistence hunting, where early humans allegedly used their ability to sweat to keep cool while running long distances to exhaust animals that lacked this ability:
Having been around the sport for awhile and having heard people on one hand extol its virtues and on the other criticize it and having seen many arguments in between, I put De Vany and Taleb squarely in the category of people who don’t have the guts to run.
Conversely I would be hard pressed to think of a sport enjoyed by so many (soccer, tennis, swimming, cycling, golfing, etc.) for which I could muster the level of disdain these guys have for running.
I love your podcasts Russ, but I wish you would be more willing to grill your guests when they stray outside of their specialty.
For example, one line of questioning might be: "Running certainly seems to be an emergent phenomenon.
Marathon running has grown from 25k finishers in 1976 to 425k in 2008 (http://www.runningusa.org/node/16414), a CAGR of 9.3%.
My presumption is that participation in shorter races like 5k’s and 8k’s are growing even more rapidly.
Why do you think humans are adopting this allegedly self-destructive behavior at such a high rate?"
Great podcast with much wisdom.
Let me push back on a few points though.
It’s great that Taleb sees the transformative power of the internet starting in the 90s, but I wonder about his analysis of stars taking the gains.
While that is no doubt true, the question is how much that trend contributed to income inequality.
Aren’t Tiger Woods, Luciano Pavarotti, and J.K. Rowling a drop in the pond compared to the richest 1-5% that skewed the income inequality distribution recently?
Rather, I suspect that the inequality came from professional and technical positions being highly renumerated in the last decade or two, to try and approach the productivity of these highly skilled people.
For two people digging ditches or working on a factory floor, the productivity of any worker is fundamentally limited compared to any other worker, at most 2-3X.
Whereas with information work this multiple explodes to 10-50X- this has been shown to be true in computer science- I believe much of the income inequality came from the attempt to compensate in equal proportion to this productivity difference.
It is likely that employers then got into the star mentality and overpaid, just as you often see sports team owners overpay for star athletes, and might have been better off trying to raise the productivity of everyone by spreading best practices from the most productive to everyone else.
Either way, the opera singers and golf superstars that Taleb talks about will soon see a great reverse in their unique positions.
The internet initially augmented their positions greatly, now it will tear them down.
One Katie Couric on CBS will be replaced by hundreds of news anchors reading the news online, helping flatten out the income distribution in the process.
It was interesting to hear Taleb make Kling’s point that regulators helped cause this mess.
It would have been interesting to hear Taleb expand on his solution of nationalization followed by most of the market becoming unregulated and why he felt some portion of the market would need to stay nationalized.
Regarding his skepticism of theories and directed research, isn’t it possible that the scientific frontier expanded greatly in the last century and that both directed research and tinkering would have had much lower success rates as a result?
Although, the point he might be making is that researchers always have to be on the lookout for secondary effects that they weren’t looking for, rather than throwing that secondary data away, because that’s much more likely to be beneficial than the original hypothesis.
I think we ultimately have to theorize, Taleb’s point is simply that we need to emphasize empirical evidence much more where we can and not be so confident in existing theories.
As for the benefits of religion, it would be nice if it were merely used as a moral code but one can point to religious wars as an example of religious theorizing and not just following the practices.
We have to theorize, we just have to be much more thoughtful, empirical, and humble when we do.
Nassim Taleb talks about the challenges of coping with uncertainty, predicting events, and understanding history. This wide-ranging conversation looks at investment, health, history and other areas where data play a key role. Taleb, the author of Fooled By Randomness and The Black Swan, imagines two countries, Mediocristan and Extremistan where the ability to understand the past and predict the future is radically different. In Mediocristan, events are generated by a underlying random process that is normally distributed. These events are often physical and observable and they tend to cluster around the middle. Most people are near the average height and no adult is more than nine feet tall. But in Extremistan, the right-hand tail of events is thick and long and the outlier, the seemingly wildly unlikely event is more common than our experience with Mediocristan would indicate. Bill Gates is more than a little wealthier than the average. The civil war in Lebabon or the events of 9/11 were more worse than just a typical bad day in the Beirut or New York City. Taleb’s contention is that we often bring our intuition from Mediocristan for the events of Extremistan, leading us to error. The result is a tendency to be blind-sided by the unexpected.
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Readings and Links related to this podcast
About this week’s guest:
Nassim Taleb’s Home page
About ideas and people mentioned in this podcast:
Books:The Black Swan: The Impact of the Highly Improbable, by Nassim Taleb. Random House, April 2007.
Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets, by Nassim Taleb.
Articles:"Risk versus Uncertainty, or Mr. Slate versus Great-Aunt Matilda", by Morgan Rose
Perception: Why Can’t We See What Is There To Be Seen?. Chapter 2 of Psychology of Intelligence Analysis, R. J. Heuer, Jr. Discusses "Interference in Visual Recognition,", by Jerome S. Bruner and Mary C. Potter. Science, Vol. 144 (1964), pp. 424-25.
"Order Defined in the Process of Its Emergence", James M. Buchanan, Literature of Liberty, 1982. Also available at the Online Library of Liberty.
"High Priests and Lowly Philosophers: The Battle for the Soul of Economics", by Peter Boettke, Christopher Coyne, and Peter Leeson.
Mercatus Center Working Paper, no. 44.
"The Smith-Hayek Economist: From Character to Identity", by Daniel B. Klein, Econlib, March 5, 2007.
What characterizes different kinds of economists and would a new name be helpful?
Podcasts and Blogs:Inteview with Nassim Taleb, by Arnold Kling. Blog entry on this podcast at EconLog.
Black Swans, by Russ Roberts. Blog entry on this podcast at Cafe Hayek.
Book Review: The Black Swan, by Charles Fitzgerald. Platformonomics, May 21, 2007.
My review of Taleb’s The Black Swan, by Tyler Cowen. Marginal Revolution, June 13, 2007.
0:37Intro. Large library, not as large as Umberto Eco. Thematic quote from Fooled by Randomness: We favor the visible, the embedded, the personal, the narrated, the tangible. We scorn the abstract. Tend to believe all swans are white because you’ve probably never seen a black swan. Confirmation bias is not taking seriously what you don’t see.
Works well in primitive environment but not in less primitive environments.
Real world has many observations dominated by fat-tailed distributions—dominated by a small number of observations.
Imagine two countries, Mediocristan vs. Extremistan, populated with a good sample of the world’s population.
Next, think of the heaviest person you could find.
How much of the total weight will that person represent?
The exceptional is inconsequential for weight.
Law of large numbers—as your sample becomes very large no single instance can influence the total.
Converge to some stable average.
Normal or Gaussian distribution characterizes Mediocristan.
Now, for Extremistan, instead of weight, think about income (which is not normally distributed).
Add in the richest person you can think of, say Bill Gates.
The exceptional in this case is not inconsequential.
The two random variables, weight and income, have different distributions.
Reischmark hyperinflation. Extreme events are often not Gaussian. 1992 Israeli interest rate incident.9:58Now, look around you.
Think about published serious novels: a handful will dominate the sales (even in a world without J. K. Rowling). Between 5 and 20 will dominate sales in any given year.
Scorning the abstract works well in Mediocristan, where you don’t need a large sample to become acquainted with the general properties.
(Common rule of thumb is that you need around 30 to figure out the general properties of the distribution.)
In Mediocristan, you don’t need to sample everyone.
It’s cost-effective to use a small sample.
The size of the sample may vary, but some small sample will do. The top 100 stocks won’t be enough of a sample to protect you; you probably need a broader index, say 500; but you still don’t need every single stock in existence. Bogle podcast.
Not just the risk, but the unexpected, the risk that is unknown.
Risk of stock market is not just losing money, but also missing an opportunity from the unknown.17:01By contrast, think about Extremistan. In general, you tend to be fooled by randomness, to think only about what happened to you in the last few days in the stock market instead of a longer, larger sample.
Tend to say "What I’ve seen is sufficient."
In summer of 1982, U.S. banks suddenly lost more than they’d made cumulatively in the past.
They were fooled by small sampling.
They’d had many good years in a row, and had forgotten about the small probability of a very bad thing happening.
This kind of error is pervasive, way beyond financial markets.
Skeptical empiricist: don’t just tell me the narrative, the ex-post story, but give me the data so I can figure out the general distribution (which will include estimating the probability of extreme events).
This is in contrast to misuse, deceptive use of data to give data an air of scientism which is not scientific.
In The Black Swan, discuss Narrative Fallacy. Hindsight bias, retrospective view, after the fact.
We are suckers for stories, and ex post we have stories. 80% of epidemiological studies fail to represent real life—that is, they cannot be replicated.
What people with statistical data do is process it and find accidental associations.
Can even find a study where smoking lowers the risk of breast cancer.
Non-representative statistical knowledge. The more data, the worse our statistical knowledge will be!
The more you read the newspaper the dumber you get.
Sometimes the profusion of information is misleading.
"Once you have a theory in your head you are going to just look for confirmation."
Bruner and Potter paper: show blurry picture of a dog, too blurry to discern.
Now increase the resolution only gradually, you don’t see a dog; but if you increase the resolution less gradually, the viewer sees the dog.
The person who forms a lot of intermediate theories that it is not a dog, then relies on his experience. Global warming: you make errors if you only rely on stories and experience.28:32Riddle of induction. If you use a linear model, you have only one or two degrees of freedom, which means you have limited ability to make predictions.
But nonlinear models can allow too many degrees of freedom, so you can predict anything at all if you select the wrong one.
Forward and backward problem. See an ice cube on the floor and you can predict how much it will amount to when it melts.
If you see a puddle of water on the floor, it’s harder to tell where it came from.
Inherent bias in epidemiological (and economics) studies because if you find nothing it’s hard to publish it. Ed Leamer pointed this bias out.
George Stigler anecdote: when he was a young economist, if you wanted to test a theory, you might run only three or four regression analyses and you spent a lot of time thinking about what variables to test because you had to do a lot of computation by hand. Today you can run hundreds of regressions, and you can easily convince yourself that the "good" ones are the ones that worked out.
We are fooled by randomness, selecting the ones that confirm our bias.
If you have 1000 traders, you are bound to have 3 or 4 who by pure randomness do well.
They are the ones who attract capital.
Those with the same skills may be unable to get their careers started.
A good dentist who makes a lot of money is probably pretty good at being a good dentist; but a trader who makes a lot of money may be just lucky.
"Is this person skilled?" with regard to financial markets, the answer is "I don’t know."
You can’t infer skills from success; but we reward it the same way.
"We are more likely to mistake the random for non-random than the non-random for random," because of behavioral bias and statistics masquerading as science.37:37We tend to force the world into Mediocristan, but we really should think about Extremistan.
What advice for someone who wants to build wealth?
We do not know the downside risk, so how can we arrange our financial affairs to sleep well at night?
There are investments that are prone to negative black swans (e.g., banking, reinsurance); investments that are black-swan neutral; and those prone to positive black-swan error (e.g., biotech, publishing).
You want to mentally characterize headlines into these categories: want to select headlines that are black-swan neutral or immensely positive.
Chapter 13: How-to chapter.
People tend to confuse the risky and non-risky. Using Markowitz model to achieve medium risk can be prone to model error. Same average risk can be achieved with securing 80% of your cash with security guards, and with the other 20% taking all the risks you can.
To find out what to do with that 20%, go to parties, try to learn about fat tails, small chance of something fascinating happening, create exposure to create an envelope of serenity. Ramifications of confirmation bias.
But there is another central source of error, more extreme than Hayek described. Looking at a dog it can be believed it was the product of randomness.
But looking at a man-made object like a car it can be hard to believe it was the result of randomness.
Yet the car was the result of a random process, and maybe even a worse one than the dog.
When reading about scientific life, it seems to evolve as if it is by design. "I have the feeling that we are fooled by our own skills."
Most of what has been discovered technologically was found when not looking for it, with minimum amount of theories about what to look for. E.g., Internet, computer, laser—three recent very influential discoveries that were all found by accident.
National Cancer Institute (NCI), Mayers (sp.?), found that the NCI went through 130,000 compounds before iterating in on the ones to use for successful chemotherapy.
Gives illusion of intention, but you are fooled by accidental finds.
Very little is found by controlled experiment.
Trial and error is inherently hit and miss.
Most successful books and movies are often not predicted.50:08But is everything luck?
Problem of separability.
If I need a tie to become a banker, does it mean that having a tie causes my success?
It is necessary to do research to find something.
Most people have trouble keeping that separate, distinguishing skill from luck.
Doesn’t mean all success is lucky.
Chapter 4: If I tell you that all terrorists are Moslem, people are likely to mistake it for all Moslems are terrorists.
All tigers are killers is not the same as all killers are tigers.
You need to make your own luck, as Pasteur said.
Look at book owners: they diversify to try to diminish the problem they have of predicting which exact ones will succeed.
Ludic and non-ludic games. Ludic games work within the rules, logical induction, assume experimenter is asking the right questions—rules of the game are clear, no ambiguity.
In typical ecological uncertainty, by contrast, you are unsure about the rules, don’t know probability structure.
No limit poker, as opposed to limit poker (where you know the distribution).
In real world, you don’t usually know the probabilities.
Statistical books are all ludic games—rules are very clear. Hayek’s critique. Idiot savant would be very good at a looting game.
Economics is not to be confused with finance.
Using Gaussian techniques in a non-Gaussian world, or equilibrium techniques in an (unknown) non-equilibrium world, can lead you to make errors.
Knowledge as a vast, unknowable landscape. James Buchanan—we take things like prices as data and look for how they come about, but those prices and preferences emerge in part from the process itself.
Sentient beings with imperfect information.
Hayek’s "Pretence of Knowledge".
People who discuss this become unknown.
Bastiat, Seen and Unseen.
French edition of The Black Swan will have more on Bastiat and also Fouchier (?sp.).
What is a good name for those who doubt in this way?
Hayek, Popper as philosophers.
Economists who think about these things used to be called political economists.
The Theory of Moral Sentiments, by the founding economist Adam Smith was a work of philosophy. If you say you are a "moral philosopher", it will cause most people to run away—will that do better at having good conversation at a party than saying you are an economist?
How about "empirical philosopher"?1:10:24The Black Swan uses statistical arguments against statistics itself.
Casino story: Running a casino sounds like an easy way to make money; but what about other problems of running a business?
A tiger attack like Seigfried and Roy, which can result in lawsuits and in having to find a new show? Risk can be outside your perception.
Same casino had multiple problems not related to tiger incident.
Gaussian distribution tells you how much data you need to identify it, self-reference.
Using the data you first have to discover the distribution you need.
Only with the Gaussian distribution can you do this and easily find the parameters.
Power laws—cannot find the parameters easily.
Chaos theory: result was that you cannot build models.
N-body, n-billiard-ball problem: much harder to capture a model from the top down with large numbers of people than with only two.
But that doesn’t mean you cannot predict anything.
Second problem: when you predict a variable for 25 years hence, your error won’t be just 25 your error from predicting 1 year forward. Third problem: psychological.
Prediction is a kind of therapy.
Have to look at your error after the fact.
Forecast error is central to decision-making.
Traveling to France, you know the size of suitcase for a given time of year; but need much bigger suitcase if traveling to Mars.
There are degrees in your ignorance about what you don’t know. The Enlightenment. "I want to turn knowledge into action"—Marx critiquing Hegel—vs. Taleb—"I want to turn lack of knowledge into action."
We should embrace the phrase "I don’t know."
We like experts, but not all domains have experts.
Why do we turn to nonprofits, NGOs and governments to solve society’s biggest problems? Michael Porter admits he’s biased, as a business school professor, but he wants you to hear his case for letting business try to solve massive problems like climate change and access to water. Why? Because when business solves a problem, it makes a profit — which lets that solution grow." name="description
Derek Sivers home, blog, projects
Why risk analysis needs to focus on the constant trade-offs between efficiency and thoroughness so typical of normal human behaviour.
In this episode of the Art of Manliness podcast I talk to Mark Divine, owner of SEALFIT and the author of the new book, The Way of the SEAL: Think Like an Elite Warrior to Lead and Succeed. Mark and I discuss his service as a SEAL, how he’s helped potential SEALs get ready for BUD/S, as well as how civilians can apply the principles that SEALs call upon to forge mental toughness.
Show highlights include:
How the military is experimenting with meditation and biofeedback to help soldiers forge mental resilience
What your Set Point is and why it’s so important you establish it
How to develop situational awareness
How and why to develop your intuition
The benefits men get in particular from following the Way of the SEAL
And much more!
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