Analyzing an equity investment for the long term is, or at least many of us would like to consider it, analogous to establishing hypotheses and searching for evidence or data that either supports or contradicts them. It is an iterative process, with more, better data begetting better questions and eventually and occasionally the sum of “small truths” coming out of this process becomes an actionable, high-conviction idea. Many have linked this process to that of investigative journalists, criminal investigators and, when we are feeling pretentious, to laboratory/empirical scientists.
There is a certain beauty and corresponding reverence to the scientific method, as well there should be; don’t worry, I’m not about to disagree with Richard Feynman on how hard it is to know things (disregard the specific example of organic food, focus on his distinction between science and pseudoscience). By the way, this other video with him has a perfect introduction about the scientific method.
The “problem” with the method is that humans are using it. As we know, subject to biases and, most importantly, incentive systems. It is not that easy to invest a career in a certain “hypotheses path” and have the data insist in disagreeing with it, or at least with a significant enough part of it that might make the experiment or the very hypothesis not valid. Analysts know what I am talking about: brushing away data points that seem “incoherent” with a theme, weighting independent factors disproportionately… We have all been there, no matter how hard we fight to be 100% rational and intellectually honest.
After this long introduction, I hope it’s clear why it was important to highlight this Wired article about a young billionaire who has declared, in the words of the magazine, “a war on bad science”. His foundation’s work apparently seeks to shed light on the instances of bad use of the scientific method, when data was perhaps ignored and corners perhaps cut. It would appear quite relevant to make science a bit less of a black box and bring even more accountability to it, and the call for increased openness to stimulate replication is particularly brilliant. Combine this with a long-term potential move to “open-source” scientific journals and the costs of scientific discovery could decrease.
I have no knowledge of the foundation beyond what I read in this article, so in line with staying “true” to the data, my compliments are based on a sample of one!
Related links on Buysiders.com:
I have posted about a similar theme in a 2012 post called “The boundaries of knowledge“.
Also read my first post mentioning Richard Feynman, which has a link to a 50-minute special called “The Pleasure of Finding Things Out” that is a real gem.