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Order and chaos – given the choice, humans would usually choose order.  Sure, we love to explore, but as we do so we also love to categorize, to organize, to create boxes and silos of knowledge.  M.C. Escher summarized these tendencies by noting, “We adore chaos because we love to produce order.”

The Limits of Risk Analysis

Nowhere is this more true than in finance.  Over the last 20 years our tools and models have reached ever-higher levels of sophistication and complication, and our understanding of risk has become deeper and more detailed.  When I began my career as a portfolio manager in the early 1990s, conversations with investors were usually creative “what if” discussions of companies and products, and the notion of risk for a fundamental investor was often limited to an examination of top holdings or industry exposures.

By the mid-2000s, many of those discussions had evolved into complicated debates over tracking error and active bets, incomprehensible to anyone outside of our own professional circles.  Our tools had improved, and these statistics were helpful, but along the way we may have lost some common sense and connection to the real world in which our investments live.

The advantage to increased order over chaos in investing is clear:  we now have better measures of correlated risks, counterparty risks, and all sorts of nuanced relationships that are not obvious from a simple glance at top holdings.  However, with all of our detailed reporting and tools, it’s easy to forget one central and uncomfortable truth:  these measures are incomplete.

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Order and chaos – given the choice, humans would usually choose order.  Sure, we love to explore, but as we do so we also love to categorize, to organize, to create boxes and silos of knowledge.  M.C. Escher summarized these tendencies by noting, “We adore chaos because we love to produce order.”

The Limits of Risk Analysis

Nowhere is this more true than in finance.  Over the last 20 years our tools and models have reached ever-higher levels of sophistication and complication, and our understanding of risk has become deeper and more detailed.  When I began my career as a portfolio manager in the early 1990s, conversations with investors were usually creative “what if” discussions of companies and products, and the notion of risk for a fundamental investor was often limited to an examination of top holdings or industry exposures.

By the mid-2000s, many of those discussions had evolved into complicated debates over tracking error and active bets, incomprehensible to anyone outside of our own professional circles.  Our tools had improved, and these statistics were helpful, but along the way we may have lost some common sense and connection to the real world in which our investments live.

The advantage to increased order over chaos in investing is clear:  we now have better measures of correlated risks, counterparty risks, and all sorts of nuanced relationships that are not obvious from a simple glance at top holdings.  However, with all of our detailed reporting and tools, it’s easy to forget one central and uncomfortable truth:  these measures are incomplete.

Risk vs. Uncertainty

One of the most vital concepts for every investor to understand is the distinction between risk and uncertainty, which has been examined by a number of leading thinkers over time, from Frank Knight in the early 20th century to Michael Mauboussin in more recent years. The concept of risk covers visible and measureable terrain, the “known unknowns”.  When we face a situation where the outcome is unknown but the range of outcomes is known, that’s risk.  You can model it, and make reasonable bets based upon the range of possible outcomes.

Uncertainty is trickier; it refers to the “unknown unknowns”.  What happens when we don’t know that range of possible outcomes, not because we are lazy or unintelligent, but because they are un-knowable?  That’s when we enter the realm of uncertainty.  Many of the biggest mistakes in investing can be traced to this basic confusion, thinking something is risky and model-able when in fact it is uncertain, with a range of outcomes beyond our models and sometimes even beyond our imaginations.  And let’s face it, the realm of uncertainty, with its lack of maps and models, is terrifying.

Evolving Models Hold Promise

Fortunately, new tools and mental models are available to investors.  One of the best resources is the growing body of research in complexity science and complexity economics.  In a complex adaptive system, the whole cannot be explained by the sum of the parts; there are emergent properties and adaptive responses within the system that shape its own development.  Sounds like the stock market, or the economy, right?  Leading the way in this research are the Santa Fe Institute (SFI) and the New England Complex Systems Institute, including the work of Brian Arthur and Yaneer Bar-Yam.  It’s at SFI that I’ve learned about everything from bitcoin to big data, and the Institute’s motto, “searching for order in the complexity of evolving worlds,” summarizes the quest of many investors.

Another resource is the field of biomimicry, looking to natural systems for patterns, principles, and wisdom that can illuminate other situations.  Biological systems and organisms offer a wonderful counterbalance to our more mechanized models of finance, highlighting different ways to think about risk, adaptability, and context.  Through my studies and connections with the Biomimicry Group and the Biomimicry Institute, I’ve been able to learn investment lessons from pine cones and sea slugs in a way that is completely independent from – and complementary to – my cherished spreadsheets. The central question biomimicry asks is “what would nature do?”  How would this function be performed in the natural world, and what can I learn from it?  Asking this seemingly simple question is revolutionary, as it demands that we clarify both what we are seeking and why – fundamental questions that are sometimes skipped-over in our data-intensive world of finance.

Though both of these fields are fairly new, the concepts they rely upon are ancient.  The more functional tools developed by complexity science and biomimicry in years to come are sure to provide different, and better, understanding of the blank space that is not addressed by current risk models.  With a better understanding of both risk and uncertainty, perhaps we will learn that Carl Jung was right:  “In all chaos there is a cosmos, in all disorder a secret order.”

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Katherine Collins is Founder and CEO of Honeybee Capital and author of The Nature of Investing. Previously, Katherine served at Fidelity Management and Research as head of US Equity Research and Portfolio Manager.