Uncertainty and Non-Linearity

http://www2.hedgeye.com/unlocked_ideas/15367-uncertainty-and-non-linearity

08/25/11 08:00AM EDT

Uncertainty and Non-Linearity

Uncertainty and Non-Linearity


This note was originally published at 8am on August 22, 2011. INVESTOR and RISK MANAGER SUBSCRIBERS have access to the EARLY LOOK (published by 8am every trading day) and PORTFOLIO IDEAS in real-time.

“Linearity isn’t the norm in the world around us, non-linearity is.”

-Dan Gardner

That’s another quote from the book I referenced last week that I am in the middle of reading – “Future Babble – Why Expert Predictions Fail and Why We Believe The Anyway.”

What’s interesting about both Chapter 2 (“The Unpredictable World”) and the book is that it’s really accessible for non-scientifically inclined readers. You don’t have to have a Ph.D. in fractal math or applied physics to grasp the deep simplicity of a few very important concepts – Uncertainty and Non-Linearity.

In Hedgeye’s Research and Risk Management Process, Uncertainty is critical to accept. Maybe that’s why our models have had very different signals than consensus during both the 2008 and 2011 Growth Slowdowns. Wall Street/Washington models tend to command some level of certainty in their baseline assumptions. Being absolutely certain about models that don’t work is a problem.

In the real-world of accountability, successful Buy-Side Risk Managers like Ray Dalio (Founder of $100B Bridgewater Associates) have embraced Uncertainty as a core component of what it is that they do. As Dalio says in John Cassidy’s New Yorker article (“Mastering The Machine”, July 25, 2011):

“I’m always trying to figure out my probability of knowing… Given that I am never sure, I don’t want to have any concentrated bets.”

I love that.

Defining Non-Linearity is a little more complex. But, essentially, that’s the point – and why we’ve built all of our models and processes on Complexity (or Chaos) Theory.

Clients often ask me for reading primers on Chaos Theory. Here are a few:

  1. Complexity – The Emerging Science At The Edge of Order and Chaos”, by M. Mitchell Waldrop
  2. Deep Simplicity – Bringing Order to Chaos and Complexity”, by John Gribbin

After having consumed both of these books, you’ll realize that neither contain any applied market models. And that, too, is the point. Accepting Uncertainty and Non-Linearity in your risk management process is something that you have to really come to embrace in principle before you apply it to what it is that you do.

In “Future Babble”, Gardner doesn’t do Chaos Theory like I do, per se, but he does simplify the difference between Linear and Non-Linear systems. “Gravity, for example, is linear in mass. Double the mass and you get twice the gravity” (page 39). “A common component of non-linear systems, feedback, involves some element of the system looping back on itself…” (page 40).

I like that explanation because it’s simple. To a degree, Non-Linearity also rhymes with what George Soros calls “reflexivity.” And, again, in principle, it takes a fundamental acceptance that this is what drives market prices, volumes, and volatilities before you can really apply it to what it is that you do.

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