Several weeks ago I read a short profile in the Wall Street Journal about Charles Wolf, a man who, while suffering left-brain damage from multiple bouts with cancer, has managed to build an investment portfolio that has beaten the DJIA and doubled its assets since 2003. For me, the most intriguing parts of the story concern various observations by researchers studying the behavior of individuals with various forms of left-brain damage, “some of whom,” writes author E.S. Browning, “have developed artistic, musical, and other talents, apparently using right-brain capabilities that ordinarily are hard to tap.”
Recounting his conversations with Bruce Miller, professor of neurology at UC-San Francisco, Browning writes thus:
“An almost obsessive focus is characteristic of people with left-brain damage who are using their right brains more. Research suggests that the left brain ordinarily serves as a traffic cop, imposing order on the vast information available from the right brain. Researchers believe left-brain damage may allow people to bypass preconceived ideas.”
The general idea is that, while the left brain is key to providing context and meaning around a given data set (like, um, our life experiences for example), its algorithmic quest for pattern recognition (and its accompanying assignment of data to a particular theory, belief, or other “frame”—be it economic, stylistic, linguistic, religious, etc.) is just as likely to fall for false patterns (theories, beliefs, etc.). By letting the data simply “be,” so to speak, left-brain damage may slow down or eliminate the rapid assignment of meaning to the data, giving it a chance to reveal its own patterns.
It just so happens that in the latest Wired magazine’s cover story, “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete,” Chris Anderson proclaims the arrival of “The Petabyte Age” in which “the new availability of huge amounts of data, along with the statistical tools to crunch these numbers, offers a whole new way of understanding the world. Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.” [In] a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear . . . out [goes] every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves.”
I find this incredibly interesting:
1. Google and similar algorithmic, data-intensive processing systems are lauded as a paradigm shift away from classical theoretical science, going so far as to proclaim the irrelevancy of “the model”. This is conceptually heralded as a good development.
2. Analogous brain function—which, were it an evolved behavior, would (one assumes) be hailed as similarly portentous for the future of cognitive processing—in fact emerges under conditions of otherwise significant disability and illness, e.g., in cases of left-brain lesions or other left-brain damage.
From an evolutionary perspective, ruling out all berries that look like the poisonous berry that killed your brother is a pretty good theory, a trade-off involving increased safety in exchange for decreased discovery, e.g. you miss out on a bunch of yummy berries that aren’t poisonous, but hey—you get to live another day/month/year). What Anderson’s article—and, oddly enough, the behavior of brain-damaged individuals like Charles Wolf—suggest, however, is that our own evolution has been a non-optimizing trade-off, and that future humans, evolving beyond the tyranny of frameworks, theories, and contexts levied by the left brain, may emerge with processing powers far beyond any we have yet seen . . . but without, perhaps, the contextual framework to understand why the results might ever matter in the first place, and lacking the ability to communicate what any of it means.
Stepping away from this very-real sci-fi digression, what can an entrepreneur learn from both stories?
1. Strive to maintain an “awareness of your awareness” – question yourself about what you think you see happening in your market, with your customers, with yourself.
2. Maintain a healthy skepticism about broad-scale prognostications (except mine, of course).
3. Don’t be too quick to ascribe causation. Sure, swift decisions and action are critical in many situations, but consider the advice an old friend and M.D. used to say to his newly graduated, over-eager medical residents: “Sometimes instead of telling yourself “Don’t just stand there; do something!”, you should tell yourself: “Don’t just do something; stand there!”
4. Recognize the difference between decisions that can benefit from more data and analysis, and decisions that can’t. This, of course, is where that ol’ left-brain might come in handy, even if Google doesn’t appear to use it.
