Taleb explains this phenomenon the best with an analogy.
Imagine you put a 1000 people together, and you’d like to take the sum total of their weight.
Even the most heavy person in the world, if they were in that sample, would only constitute at most 0.5% of the weight.
Now, intead, say we were to put a 1000 people together, and were to measure the sum total of their wealth.
If we include Bill Gates in that sample, the rest of the 999 are simply rounding errors.
This is to portray a type of scenaraio where there are massive wins that outweigh a lot of losses.
I think that for most of our lives we were trained to predict in the first type of scenario, but don’t do as well in the second type of scenario. This became very apparent to me in an outreach related campaign that I’m doing. Recently I’ve received offers in that campaign that seem very good, but I know that I won’t take them. The reason being that the longer I stay in the game, the higher the chance of an unlikely but immense upside.
This is really interesting to consider, when trying to do high upside outreach related campaigns