In

part 1 of this series I talked about

Daniel Bernoulli's brilliant idea of using a concave

utility function to solve the

St. Petersburg paradox, a problem originally posed 300 years ago (and a couple of decades before Daniel's solution) by his cousin

Nicolas. I explicitly suggested a strong parallel with the way Apple is being undervalued as it gets bigger and bigger, and ended with a discussion of how the overused modern phrase "law of large numbers" (aka

lol numbers) as misused by current mainstream financial media talking heads (when referring to the "

limits of growth" concept) ironically is in contradiction with the actual fundamental probabilistic concept called the

Law of Large Numbers (LLN), first proved by Daniel's and Nicolas' uncle

Jacob Bernoulli: If Jacob's LLN applies, the likelihood that Apple will continue to grow would seem higher, given the historical empirical record, not lower (as the lol numbers trite cliche implies). On the other hand, if in fact there are limits to growth being reached, then the actual LLN is violated (independence of trials). In short, the semantic confussion and logical contradiction of invoking a misnamed "law" to justify persistent

multiple compression despite continued strong growth in Apple's earnings might get resolved by reinterpreting it not necessarily as limits to growth but instead as an expression of the concept of

diminishing marginal utility (although these two concepts might be related after all).

For this part 2 my goal was to explain why and how such a simple solution of a concave utility function works in assessing the pragmatic value placed by most people on such big yet unlikely potential returns as well as why and how the specific logarithmic utility proposed by Bernoulli might not work in every case, and how the theory eventually got refined over the years, decades and centuries. This is no simple task: to wade through 300 years of academic literature on economic theory, and most recently financial theory, from the incipient probability research to

modern portfolio theory and

CAPM. For an example of the nature of the literature, check out

this 850 page book (many pages omitted in that Google Books preview) focused only on a singular very specific thread (the

Kelly Criterion) within the broader subject. Even if I could digest and condense all of it into a blog post (which I definitely can't), I wouldn't want to submit my readers to such a dry academic dissertation. Besides, it's mostly way over my head. Hence, after being set back on my promise to post this within a couple of weeks of part 1, I've finally decided to go with a very simplified intuitive exposition, but leaning heavily on the multiple referenced links provided (with the caveat to only rely on Wikipedia for a superficial glance and to conduct your own research through more reliable sources if a deeper understanding is needed). Apologies if this ends up with significant theoretical holes.