The Role of Science in Retirement Planning

IMO, judgment, common sense, creativity, etc. are the most important skills of the financial advisor; the more common pursuit of "science" may be a fool's errand.  One group advocating science suggests MCS modeling that ignores black swans.  Another group of scientists suggests much of this is pseudo science, and, among other things, demands inclusion of black swans (usually leading to safety-first for all).  A third group attempts to straddle the above two positions. 

I am not a CFA and I do not have a PhD in math or economics.  What I do have is a JD, CPA, and many years of analyzing and resolving complex international corporate tax and financial structuring projects.  There were always plenty of "scientists" (experts) willing to sell me the latest and greatest product for dealing with these projects (at $1m+ price tag).  Instead, I always started each project with a blank slate and built up the analysis from scratch; making sure I did not overlook any relevant and material issues.  In retrospect, there was never an appropriate cookie-cutter solution, even when offered by the world's greatest experts at their high price tag.  The same blank slate approach should be applied to retirement planning.

I am not suggesting that knowledge is unnecessary.  Knowledge is essential.  The BFP article does an excellent job of identifying expected future returns, and that knowledge is crucial to retirement planning.  But in addition to knowledge, the most important  skills are judgment, common sense and creativity.  The advisor should start with a blank slate and make certain he understands all of the relevant and material information of the client.  Perhaps most importantly, the information gathering must make sure that the advisor and the client are on the same wave length regarding spending.  I found that the only way to make sure there is a complete understanding on spending is to test relevant AAS scenarios with the client, as discussed in my earlier post.  It is up to the judgment of the advisor to decide which scenarios are relevant and important -- no science can do that for you.  There is no science that can give you exact probabilities of the chosen scenarios, again it is a matter of judgment.  IMO, good judgment will result in estimated probabilities that are as good as probabilities that are derived from 10,000 iterations  (which, perhaps, conveniently ignore black swans or autocorrelations).  Of course, all analyses must be regularly updated to take into account changes in facts and circumstances.