In his book – Fooled by Randomness, Nassim Nicholas Taleb discusses how success can easily by attributed to luck, and introduces the Monte Carlo engine to simulate purely random situations, avoiding conventional methods of attribute analysis.
By generating artificial scenarios with known attributes, the Monte Carlo engine can demonstrate outcomes based purely on luck, devoid of skill influence. Taleb proposes a thought experiment with 10,000 fictional investment managers, each having a 50% chance of making or losing $10,000 annually.
Managers with a single bad year are removed, akin to George Soros’s high standards. This simulation illustrates how, through pure chance, some managers can appear consistently successful.
Here’s an excerpt from the book:
I have often been faced with questions of the sort: “Who do you think you are to tell me that I might have been plain lucky in my life?” Well, nobody really believes that he or she was lucky. My approach is that, with our Monte Carlo engine, we can manufacture purely random situations.
We can do the exact opposite of conventional methods; in place of analyzing real people hunting for attributes we can create artificial ones with precisely known attributes. Thus we can manufacture situations that depend on pure, unadulterated luck, without the shadow of skills or whatever we have called non-luck in Table P.1.
In other words, we can man-make pure nobodies to laugh at; they will be by design stripped of any shadow of ability (exactly like a placebo drug).
We saw in Chapter 5 how people may survive owing to traits that momentarily fit the given structure of randomness. Here we take a far simpler situation where we know the structure of randomness; the first such exercise is a finessing of the old popular saying that even a broken clock is right twice a day. We will take it a bit further to show that statistics is a knife that cuts on both sides.
Let us use the Monte Carlo generator introduced earlier and construct a population of 10,000 fictional investment managers (the generator is not terribly necessary since we can use a coin, or even do plain algebra, but it is considerably more illustrative – and fun).
Assume that they each have a perfectly fair game; each one has a 50% probability of making $10,000 at the end of the year, and a 50% probability of losing $10,000.
Let us introduce an additional restriction; once a manager has a single bad year, he is thrown out of the sample, good-bye and have a nice life.
Thus we will operate like the legendary speculator George Soros who was said to tell his managers gathered in a room: “half of you guys will be out by next year” (with an Eastern European accent). Like Soros, we have extremely high standards; we are looking only for managers with an unblemished record. We have no patience for low performers.
The Monte Carlo generator will toss a coin; heads and the manager will make $10,000 over the year, tails and he will lose $10,000. We run it for the first year. At the end of the year, we expect 5,000 managers to be up $10,000 each, and 5,000 to be down $10,000.
Now we run the game a second year. Again, we can expect 2,500 managers to be up two years in a row; another year, 1,250; a fourth one, 625; a fifth, 313. We have now, simply in a fair game, 313 managers who made money for five years in a row. Out of pure luck.
You can find a copy of the book here:
Fooled by Randomness- Nassim Nicholas Taleb
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