Trial and Error as Tamed Randomness

Search and How Errors Can Be Investments

Trial and error has one overriding value people fail to understand: it is not really random, rather, thanks to optionality, it requires some rationality. One needs to be intelligent in recognizing the favorable outcome and knowing what to discard.

And one needs to be rational in not making trial and error completely random. If you are looking for your misplaced wallet in your living room, in a trial and error mode, you exercise rationality by not looking in the same place twice. In many pursuits, every trial, every failure provides additional information, each more valuable than the previous one—if you know what does not work, or where the wallet is not located. With every trial one gets closer to something, assuming an environment in which one knows exactly what one is looking for. We can, from the trial that fails to deliver, figure out progressively where to go.

I can illustrate it best with the modus operandi of Greg Stemm, who specializes in pulling long-lost shipwrecks from the bottom of the sea. In 2007, he called his (then) biggest find “the Black Swan” after the idea of looking for positive extreme payoffs. The find was quite sizable, a treasure with precious metals now worth a billion dollars. His Black Swan is a Spanish frigate called Nuestra Señora de las Mercedes, which was sunk by the British off the southern coast of Portugal in 1804. Stemm proved to be a representative hunter of positive Black Swans, and someone who can illustrate that such a search is a highly controlled form of randomness.

I met him and shared ideas with him: his investors (like mine at the time, as I was still involved in that business) were for the most part not programmed to understand that for a treasure hunter, a “bad” quarter (meaning expenses of searching but no finds) was not indicative of distress, as it would be with a steady cash flow business like that of a dentist or prostitute. By some mental domain dependence, people can spend money on, say, office furniture and not call it a “loss,” rather an investment, but would treat cost of search as “loss.”

Stemm’s method is as follows. He does an extensive analysis of the general area where the ship could be. That data is synthesized into a map drawn with squares of probability. A search area is then designed, taking into account that they must have certainty that the shipwreck is not in a specific area before moving on to a lower probability area. It looks random but it is not. It is the equivalent of looking for a treasure in your house: every search has incrementally a higher probability of yielding a result, but only if you can be certain that the area you have searched does not hold the treasure.

Some readers might not be too excited about the morality of shipwreck-hunting, and could consider that these treasures are national, not private, property. So let us change domain. The method used by Stemm applies to oil and gas exploration, particularly at the bottom of the unexplored oceans, with a difference: in a shipwreck, the upside is limited to the value of the treasure, whereas a large resource discovery may have a much higher, though still finite, payoff.

Finally, recall my discussion of random drilling in Chapter 6 and how it seemed superior to more directed techniques. This optionality-driven method of search is not foolishly random. Thanks to optionality, it becomes tamed and harvested randomness.