Limits of statistics applied to High Impact Technology and Existencial Risk

Nassim Taleb popularized the theory and wrote “the Black Swan”. He has written about the limits of Statistics and in particular limits in the Fourth Quadrant.

Applying his advice to avoid optimization, love redundancy applied to Lifeboat Foundation class threats or beneficial high impact technologies.

Do not try to optimize only one technology project – ie just the Tokomak approach to nuclear fusion, or one path to Artificial General Intelligence (AGI) or molecular nanotechnology. Have multiple projects and approaches. More chances to get lucky.

Do not optimize one strategy for defense of civilization but make the redundant civilization. More planets, space stations, hardened earth sites, etc…

One thing is that the casual observers “Black swan” can be someone else (who investigates and researches an issue) predicted and inevitable event, which they tried to warn the world or country about and actively tried to stop.

Like Warren Buffet saying 5 years ago that credit defaults swaps were weapons of financial mass destruction and getting all of his companies out of them.

Or state mortgage regulators and state attorneys generals trying to stop the overly loose Federal mortgage regulations and standards.

An alternative to statistics is to look closely at many of the data points and do the due diligence on high impact technological innovation and financial and existence risks.

The Map

The traps in misapplying statistics are:

First Quadrant: Simple binary decisions, in Mediocristan: Statistics does wonders. These situations are, unfortunately, more common in academia, laboratories, and games than real life—what I call the “ludic fallacy”. In other words, these are the situations in casinos, games, dice, and we tend to study them because we are successful in modeling them.

Second Quadrant: Simple decisions, in Extremistan: some well known problem studied in the literature. Except of course that there are not many simple decisions in Extremistan.

Third Quadrant: Complex decisions in Mediocristan: Statistical methods work surprisingly well.

Fourth Quadrant: Complex decisions in Extremistan: Welcome to the Black Swan domain. Here is where your limits are. Do not base your decisions on statistically based claims. Or, alternatively, try to move your exposure type to make it third-quadrant style (“clipping tails”).

Where there are heavy and/or unknown probability tails and no or unknown characteristic scale and complex payoffs then you are in the fourth quadrant.

Complex payoff examples are societal consequence of pandemics and benefits of innovative technology.

Phronetic Rules: What Is Wise To Do (Or Not Do) In The Fourth
First Phronetic Rules: What Is Wise To Do (Or Not Do) In The Fourth

Avoid Optimization, Learn to Love Redundancy Psychologists tell us that getting rich does not bring happiness—if you spend it. But if you hide it under the mattress, you are less vulnerable to a black swan. Only fools (such as Banks) optimize, not realizing that a simple model error can blow through their capital (as it just did). In one day in August 2007, Goldman Sachs experienced 24 x the average daily transaction volume—would 29 times have blown up the system? The only weak point I know of financial markets is their ability to drive people & companies to “efficiency” (to please a stock analyst’s earnings target) against risks of extreme events.

Indeed some systems tend to optimize—therefore become more fragile. Electricity grids for example optimize to the point of not coping with unexpected surges—Albert-Lazlo Barabasi warned us of the possibility of a NYC blackout like the one we had in August 2003. Quite prophetic, the fellow. Yet energy supply kept getting more and more efficient since. Commodity prices can double on a short burst in demand (oil, copper, wheat) —we no longer have any slack. Almost everyone who talks about “flat earth” does not realize that it is overoptimized to the point of maximal vulnerability.

Biological systems—those that survived millions of years—include huge redundancies. Just consider why we like sexual encounters (so redundant to do it so often!). Historically populations tended to produced around 4-12 children to get to the historical average of ~2 survivors to adulthood.

Option-theoretic analysis: redundancy is like long an option. You certainly pay for it, but it may be necessary for survival.