The Simplicity Standard

For the simplicity on this side of complexity, I wouldn’t give you a fig. But for the simplicity on the other side of complexity, for that I would give you anything I have.

Oliver Wendell Holmes Jr1

Obviously, simple stuff is better than complex stuff.  Its easier to remember and easier to apply.  But as countless wise men and parables have noted, there is an important distinction between simplicity and being simplistic.²  Edward De Bono describes this as the difference between being simple after knowing (simplicity), and  being simple without knowing (simplistic).  Clearly our goal is simplicity.

To acheive this simplicity we must become fluent in the tools we are applying.  This means not only understanding them, but applying and practicing them in all their complex variations.  Barbara Oakley described this difference between understanding a language and being fluent in it as follows³:

“Where my language classmates had often been content to concentrate on simply understanding Russian they heard or read, I instead tried to gain an internalized, deep-rooted fluency with the words and language structure. I wouldn’t just be satisfied to know that понимать meant “to understand.” I’d practice with the verb—putting it through its paces by conjugating it repeatedly with all sorts of tenses, and then moving on to putting it into sentences, and then finally to understanding not only when to use this form of the verb, but also when not to use it. I practiced recalling all these aspects and variations quickly. “

The pitfall of working on this simplicity is that we get carried away with the increase in knowledge that it brings and end up knowing lots and understanding little.  Financial theory and financial services marketing is full of such examples.  Ideas which have an important and insightful essence, try too hard to codify this essence and end up being precisely wrong rather than roughly right.4  Ben Graham, as always, was onto this early:

“Mathematics is ordinarily considered as producing precise and dependable results; but in the stock market the more elaborate and abstruse the mathematics the more uncertain and speculative are the conclusions we draw there from. Whenever calculus is brought in, or higher algebra, you could take it as a warning that the operator was trying to substitute theory for experience, and usually also to give to speculation the deceptive guise of investment”

Whilst we don’t want to get bogged down in complexity, we can’t avoid passing through either.  In the same way that it is the exception that makes the rule, it is the wrinkles around our idea that are important.  In the Black Swan, Nassim Taleb calls this the Platonic fold, the place where simplistic models colide with reality. It is this collision that presents both traps and opportunity for investment analysis.

Our objectives therefore are twofold. Firstly to gain enough fluency in our tools that we can express our ideas with simplicity, whilst simultaneously avoiding being simplistic by over reliance on these simple tools to solve a complex problem.

Footnotes
1. The original quote from Oliver Wendell Holmes was “The only simplicity for which I would give a straw is that which is on the other side of the complex – not that which has never divined it”.  However, in the theme of simplicity, I’ve used the more often cited quote, which seems to present the same idea in a simpler manner.  https://en.wikiquote.org/wiki/Oliver_Wendell_Holmes_Jr.
2. In addition to the quotes of Lao Tze, Einstein and Wendell Holmes noted on these pages, consider the old saying, “A little knowledge is a dangerous thing.”  Aristotle also expressed a similar idea when he noted “It is the mark of an educated mind to expect that amount of exactness which the nature of the particular subject admits.”
3. http://nautil.us/issue/40/learning/how-i-rewired-my-brain-to-become-fluent-in-math-rp?
4. Options pricing and portfolio theory are two examples which spring immediately to mind with disastourous consequences for those who relied too heavily on false models.  See When Genius Failed.