Asymmetry: A Revised Mental Model for Enterprise Risk

Whether you turn on your television or read your iPad, smartphone or other mobile device, the cacophony of news around us has become more confusing and unsettling.  The never-ending wars in the Middle East, cybersecurity, global market rallies and capitulation, natural disaster, corporate layoffs… you get the picture!

If you are like me, you want nothing more than a return to a quieter time when things were better!  But the truth is, the past is seldom as we remember it or something we can return to.  We filter out the bad and remember the good.  Our ability to move forward in the face of uncertainty depends on our brain’s ability to discount the negative and remain optimistic for the future.

Welcome to the new world of Asymmetric Risks!

The world around us has changed in ways most could not have contemplated 10 years ago.  The benefits of mobile technology and social media has facilitated an explosion of cyber warfare.  Likewise, the euphoria of democracy espoused in the Arab Spring has deteriorated into a cauldron of warring factions and terrorists.  These events epitomize asymmetric risks, described as a situation in which the gains realized from one or more events differs significantly from the losses incurred by events in the opposite direction.

Asymmetric risks are different, but the phenomena is as old as mankind.  Asymmetry has been the basis for managing a variety of risks, from derivatives trading, health care research and military warfare to cybersecurity.  Asymmetric risks are pervasive but lie below the surface of awareness…. until some major event jars us from complacency.

To better understand asymmetry, it’s important to explain how these risks go unnoticed until we are faced with the consequences of a dealing with a crisis we have not planned for.  Asymmetric risks are harder to anticipate primarily due to the infrequent nature of these events.  Quantitative analysts call these events tail risks.  Nassim Nicholas Taleb described these events in his seminal book, “The Black Swan,” writing, “a black swan is a highly improbable event with three principle characteristics: it is unpredictable, it carries a massive impact and, after the fact, we concoct an explanation that makes it appear less random, and more predictable, than it was.”

Asymmetric risks build quietly and gradually, but they present themselves in very sudden, inexplicable ways.  The most recent example is the drop in oil prices simultaneously with the decline of China’s economic growth.  These two events are unrelated, but both events were predictable and should not have shocked global markets.  Anyone who has studied an analysis of historical oil patch economic boom cycles could have predicted that oversupply would eventually lead to lower prices and economic decline.  Correspondingly, China’s economic miracle has no historical precedence; as economies mature and grow, a reversion to the mean is inevitable.  The fact that these two unrelated events happened at the same time is less remarkable than the fact that the markets did not anticipate one or the other occurring.

The Efficient Market Theory, a rebuked hypothesis, postulated that all market participants receive and act on all of the relevant information as soon as it becomes available.  We are now more aware of the fallacy of this theory and the thinking behind it, which, in part, led to the Great Recession of 2008.  Oil price declines and economic growth models have significant historical data from which to model how these events, but not the timing, would eventually resolve themselves.  The fact that these two events (decline in oil prices and slowing of China’s economy) occurred at the same time may be a coincidence, or it may not; therein lies the risk of asymmetry.

The last oil boom-and-bust cycle ended more than 50 years ago, and who can remember when an emerging economy, such as China, rose to compete with America for the distinction of the world’s largest economy?  We fail to anticipate these events because of a phenomenon known as Availability Bias.  Availability bias is a focus on the most recent information we have in front of us on a daily basis. Examples include a focus on the latest poll results for presidential candidates, dips in the stock market or latest terrorist attack.

Availability bias distracts us from the kinds of asymmetric risks organizations must now begin to prepare for in a world where emerging economies, technology and scientific discovery will increase in velocity driven by advances in artificial intelligence, esoteric financial products and computing power.

Unlike Nassim Taleb, I am optimistic that we will learn to manage these risks more effectively as the ability to harness the power of historical data continues its trajectory of development.  In the meantime, organizations must begin to consider asymmetric risks within an enterprise risk framework.  Too often, strategic corporate objectives emphasize the positive outcomes of their plans for sales growth or market share acquisition without fully contemplating the asymmetry of events that might offset the proposed gains if the organization is not successful.  A lack of high-quality data for peering into the future is no longer an excuse.  Much of the data exists to model one or more outcomes with varying degrees of precision; however, many firms fail to model how these events might evolve in opposite directions, offsetting gains and losses.

Managing asymmetry in an enterprise risk framework requires a new mental model that is less based on some formulaic internal controls framework.  Asymmetric risk requires a revision of the enterprise risk framework re-imagined as an approach to make us more aware of blind spots in our thinking about risk.