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I recently finished #RWRI #rwri16 course, organised by Real World Risk Institute and taught by @nntaleb, Robert Frey, Raphael Douady, @DrCirillo Here is a list of practical first principles (not exhaustive) I took away from this short and enlightening program
1. Take risks one can measure than measure risk one is (blindly) taking. In other words, ‘risk taking’ is better than ‘risk management’
2. Risk of ‘ruin’ from a fat tail is not the same as the risk from ‘tinkering’. Avoiding -ve consequences from small mistakes over time can lead to ruin
Read 10 tweets
I finished @nntaleb's Real World Risk Institute (#RWRI) two-week program this past week, and it was one of the most insightful, intellectually challenging, and fun learning experiences I've had in a long time.

Here's a thread with 10 very practical things I learned 🧵
1. Most real-world risk comes from fat tails.

WTF is a fat tail?

A fat-tailed system is one that is governed by a power law distribution (as opposed to a gaussian distribution).

In a fat-tailed system, one single event drives all the outcomes.
Here's an example:

Let's say 100 people are reading this tweet. The average height of the 100 people reading this tweet is 5'4". If LeBron James starts reading this tweet as well, the average height doesn't change much. Maybe it's now 5'5".
Read 15 tweets
"You'd rather impress your cardiologist than gym rats"

You need to be BOTH strong AND aerobically fit -it's not not either/or.

There are no RCS for both exercises jointly [only separately] but we know that strength training stiffens LV, aerobic reserse-remodels.

@DrDamluji Image
2) We humans are not made to specialize without costs. Doing "too much" aerobic is worse than the optimal level ["reasonably" fit but without too much hypertrophy], but still better than sedentary.
Being strong is a necessity. But there must be a similar optimum.
3) Rigorous longitudinal RCS: as we age, we need >> than the recommended 150 min/w. Add 90% MHR 1 day plus strength training 1 day, but much less than prof athletes.

Entire bandwidth: Utraslow walks + brisk + stairs/hikes/sprints as no 1 is a substitute.
Read 7 tweets
One of the points that was made yesterday in RWRI is that many of the irrational tendencies that people have, for example mental accounting, turn out to be perfectly rational when ecological considerations are taken into account

#RWRI
But today we talked about being fooled by randomness. @nntaleb put up a histogram. He generated 20 pairs of random numbers from two normal distributions that had a correlation of 0.2. This is a high correlation, but many of the sets, by chance, had *negative* correlation! Image
In real life, most people who observe a correlation, and have 20 observations, will have a high confidence in the correlation that they observe, despite the fact that they are very likely to be wrong. How can we reconcile our evolved intuitions with fooled by randomness?
Read 6 tweets
For a while I was in a multilevel. The product was good but highly overpriced. I got in bc a friend I owed asked. But I did see some interesting tings... Oportunity to learn about selling and talking to pple; reading books otherwise never would have read; learn about BS
Everybody that enter with me I told it was more a selling course than a "business", no one lost a penny or they won money or I cover it.
It's ok to play anygame if you really know what you are doing and why as long as you don't hurt anybody.
That is what I don't like about BTC they are so pushy... They could play the game they like and that's it... Why they have this need to advocate soooo much.
Read 7 tweets
Some highlights / takeaways / thoughts / comments from #RWRI 14, Day 10:

1. If your metric is not useful during times of crisis, then your metric is simply not useful.
Many metrics are some variation of reward divided by risk, where 'risk' can be further broken down as: measured risk divided by hidden risk.

If you increase your hidden risks, it artificially increases your reward to risk ratio.
It's very difficult to increase the expected return, so many will increase hidden risk in order to provide the illusion of a high reward-to-risk metric.
Read 41 tweets
Fraud topology and parting #RWRI stuff.

2008: We consolidated banks instead of breaking up banks.

Coordinated attacks on a small number of hubs can take out the entire network.
Biggest threats according to FSARC: maliciously executed trades, multi-day of a bank's wholesale payment system, multi-day SWIFT outage. Mostly talk unfortunately.
Moving to cashless society:
1. cost savings in not printing money and ease of transaction
2. crypto can dematerialize physical financial instruments
3. all money movement subject to surveillance and blocking
Read 21 tweets
Some highlights / takeaways / thoughts / comments from #RWRI 14, Day 9:

1. It took us decades to figure out that trans fats were harmful. GMOs can have the same problem.

Just because we don't have evidence of a problem does not mean that there is no problem.
2. When implementing a tail risk hedge strategy (whether in finance or other domains), your goal is to be at a point where you are okay with your worst case scenario.
3. Finance vs. Engineering.

In finance, tail risk hedging is often scorned. In engineering, tail risk hedging is the entire point (i.e. you are ensuring your bridge won't collapse).
Read 7 tweets
Some highlights / takeaways / thoughts / comments from #RWRI 14, Day 8:

1. To deal with bad actors in a system, ensure damages to the system will also damage the bad actor.

This is difficult to do in practice, but Bitcoin accomplishes it.
The value of Bitcoin is largely based on trust of the system. So if bad actors are able to break the trust of the system, the value of Bitcoin could crater, making the bad actors' heist of Bitcoin lose its value.
The bad actors are concave (low upside, huge downside) in this scenario. It makes more sense for the bad actor to steal cash and buy Bitcoin with it or steal Bitcoin from a custodian like Coinbase, than to attack the network directly.
Read 18 tweets
Some highlights / takeaways / thoughts / comments from #RWRI 14, Day 7:

1. Just because x is normally distributed does not mean that f(x) is normally distributed.
In other words, just because you can predict a specific variable / input for a model does NOT mean that the model itself should be used to forecast.
This is because x could be solely probability-based and have no second-order consequences. f(x), however, is based on the *effect* of that probability, which introduces second-order consequences (i.e. degrees of separation between the probability and the end result).
Read 23 tweets
Some highlights / takeaways / thoughts / comments from #RWRI 14, Day 6:

1. Chaotic systems occur when tiny differences in initial conditions result in wildly different results (e.g. changing a variable from 10 to 10.0001 gives a completely different result).
To predict a system like this, you would need infinite precision, which you don't have. These systems are "unpredictable but characterizable," as you can predict the possible states but not where it will be at any given point.
Example: You can't predict the weather 1 year from now, but you can know the range of outcomes of what the weather could be 1 year from now.
Read 20 tweets
Some highlights / takeaways / thoughts / comments from #RWRI 14, Day 5:

1. Optionality provides you with convexity: you can throw out the bad option and choose a better option.
Optionality is worth something, so the whole idea is to avoid paying too much for it. Many times, it isn't priced in at all (e.g. a restaurant reservation that you can cancel for free if you find a better one).
2. In any field, what you actually need to know is not what outsiders think you should know. As a practitioner, you gain a deep understanding of the properties that outsiders (e.g. academics) simply can't obtain.
Read 17 tweets
Day 5 #RWRI 14
1/n

Started with a Q&A, and then moved to optionality, convexity and model error. @DrCirillo talked about EVT and @financequant was able to join us again.

Personal highlights
- Optionality: You have the right but not the obligation. Notice the asymmetry here.
2/n #RWRI 14

- Optionality gives you convex payoffs (very rarely optionality <=> convexity doesn't hold)
- Example of optionality: trial and error (T&E)
- How to find hidden optionality?
A. Not easy. You need to train for it. Domain dependent, but T&E gives you optionality
3/n #RWRI 14
- Optionality implies an asymmetry. Can you have optionality without a sucker on one side?
A. Yes. Trial and error
-Low diversification makes you fragile. If you don't have a tail hedge, you'll eventually blow up.
Read 10 tweets
Some highlights / takeaways / thoughts / comments from #RWRI 14, Day 4 (probably my favorite day thus far):

1. If the payout is convex, you don't care about the average; you care about the second order effects.
2. The more optimized a system, the more fragile it is. The tradeoff for more optimization is less redundancy. So when hyper-optimized organizations meet a large stressor, they don't have enough safety margin to survive.
Example: a hyperoptimized retail storefront plows ALL available cash into inventory. A less optimized retail store keeps lots of cash in reserve. Which one has a better chance of surviving a massive loss of sales due to COVID (or other stressor)?
Read 22 tweets
Day 4 #RWRI 14
1/n

Today we continued talking about fat tails and convexity. @financequant's talk had to be postpone because power went out in his location.

Personal highlights
- We have said before that we can't forecast fat tail variables. But why?
2/n
Ans: Because fat tails are determine by one or a few points. The fatter the tail, the bigger the effect of one observation.

- This argument also explains why correlation has no meaning under fat tail variables. The relationship between two fat tails is given by a point
3/n

that you probably don't even have. Hence correlation is meaningless, or as Nassim said the first day, corr is not corr. Here's a paper by Nassim related to this.

academia.edu/39797871/Foole…

- Only produce forecasts where the LLN applies.
Read 9 tweets
Some highlights / takeaways / thoughts / comments from #RWRI 14, Day 3:

1. Biggest takeaway overall through three days is: "Science isn't about naive evidence, it's about understanding properties."
It's pretty easy to run a linear regression on two variables, or develop an algorithm, and call it science. It's very difficult (and sometimes impossible) to deeply understand the properties of the parts of a system and how they interact with each other.
2. Deterministic systems can produce unpredictable results. In complex systems, a slight tweak to the probability can yield completely different results. Or, sometimes, the end result will be the same no matter how large you tweak the probability.
Read 10 tweets
Some highlights / takeaways / thoughts / comments from #RWRI 14, Day 2:

1. There are two major types of distributions: Mediocristan (e.g. height) and Extremistan (e.g. wealth).
In Mediocristan, the average of a small sample (n=100 or even n=10) will be reasonably close to the average of the entire population. If you take the average height of 100 people, you won't be too far off the average height of everyone in the world.
In Extremistan, the average of almost any sample is worthless, because a few data points contain almost all of the data. The average wealth of 100 people is nowhere near the average wealth of everyone in the world, since Gates / Bezos / etc. hold such a huge portion of wealth.
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I have an idea.

Two years ago I set up a London meet-up (after attending #RWRI) to discuss the ideas of @nntaleb - this happened with the help of @grumpyinnovator. Then I went to a meet-up in the Basque country set up by the lovely @Ekaitzsaies where I stayed with the...
..nicest and most thoughtful person someone could know, @urtatses. Now, I'm organising a meet-up with @ashokatluri in New Delhi (lockdown and other things have conspired to delay it).

What I would like to do is set-up a day where we have a few speakers talk about the ways...
they escaped the Rat Race - entrepreneurs, drop-outs, rich, poor, artists, business-owners, whatever.

ATTENDANCE IS FREE; drinks and refreshments - not a strict timetable - interesting people only.
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Thank you to the brilliant team (@nntaleb, Raphael Douady, @financequant, Alicia Bentham Williams, @normonics, @trishankkarthik, Arié H, and Tom Messina) and my fellow participants who made #RWRI 13 an experience that will touch many parts of life. My immediate to do list...
1) Stress test the big stuff. If it accelerates, get out. @nntaleb
2) I'm over allocated, move more into assets (e.g. real gold not a tracker) that are not concave or subjected to model error (@financequant)
3)Change all my passwords and ensure these never map onto each other (live lesson from @stefan_frei )
4) Assume you will be hacked anyway and make sure you don't give a monkey (@nntaleb - not quite verbatim)
5) Figure out what @trishankkarthik suggests re: antivirus software.
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npr.org/sections/goats… Pneumonic plague in China
Third case of plague in China amp.usatoday.com/amp/4228543002 #chinaplague
Sars-Cov2 Resources
tinyurl.com/wq3fwlt -- Persistence of coronaviruses
tinyurl.com/wqrbn43 -- Sars-Cov2 persistence (being reviewed)
tinyurl.com/vrdfw5j -- Replication n Pathogenesis of Corona Viruses
tinyurl.com/scpjjmh -- Sars-Cov2 Covid19 patients
#Wuhanvirus
Read 7 tweets
My personal generalized thoughts and conclusions from #RWRI 12 @nntaleb

-Statistics is about the elimination of noise
-Decision making gets easier in uncertain conditions
-Skin in the game is a filtering mechanism
-Group behavior is not expressed by individual preferences
-Nature is scale invariant
-You create tail events simply by regulating the process - it’s an unnatural constraint
-It’s misconstrued that lowering variability actually lowers risk
-McDonalds exists as an insurance policy
-Everything non-linear is necessarily convex/concave
-Don’t theorize about what people want, just act on what they do
-Have optionality on the convexity of random events
-PTSD vs Post traumatic stress growth. One is talked about because it makes $$$
-Floor your losses
-Zero intelligence trading works when you have convexity bias
Read 13 tweets
“If you put a blueprint on anything that works organically you’ll f*ck it up.”
-@nntaleb

[Day 5] #RWRI 12
@nntaleb “Anyone who gives a zero probability is either a religious fanatic or an idiot.”
-@nntaleb
@nntaleb “Thou shall not buy a put from anyone who’s performance is correlated to that put.”
-@nntaleb
Read 6 tweets
“Just knowing that the tail is there is half the battle.”
-@nntaleb

[Day 4] #RWRI 12 … some nuggets of wisdom
@nntaleb “If you need a good reputation to survive, you’re f*cked.”
-@nntaleb
@nntaleb “Any system that needs confidence to function is about to go bust.”
-@nntaleb
Read 6 tweets
“Rule of thumb: you’re in a drawdown state 70 % of the time. And more than half the time in a major drawdown.”
-@financequant

[Day 4] #RWRI 12 … facts about 180 years of stock market history
@financequant [Drawdown history without the great depression] “…its’ like saying that instead of having 100 pounds of TNT blowing up you have 98 pounds of TNT blowing up.”
-@financequant
@financequant “If everyone is running for the exit does it really matter why?”
-@financequant
Read 4 tweets

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