For the professional coach, these recent findings regarding complex, self-replicating systems pose a whole host of new questions and challenges. On the one hand, if organizations are self-replicating, then any change in the style or strategies of a specific leader would be hard to either initiate or maintain—for many other subsystems would have to change in a similar manner, given that these systems are all replicating one another. On the hand, if a small change can be initiated and maintained by one leader in one specific setting, then this could set off a chain-of-changes that spread throughout the complex organization in which all of the parts are inter-connected. The role of the professional coach thus becomes one of helping her client identify the key leverage points (Malcolm Gladwell’s tipping point and Buckminster Fuller’s trim tab). The coach must also be in the business of supporting and reassuring her client through this challenging moment of leadership—for the leverage point is not easily identified and the change will be resisted at all levels and in many different ways (both obvious and subtle) throughout all of the mirroring subsystems of the organization.

There is a third major contribution made by these pioneers of chaos and complexity: this contribution concerns the measurement of complex phenomena. Complex systems are difficult to measure because they are inherently unpredictable and vulnerable to slight shifts in initial conditions (the so-called “butterfly” effect). However, this isn’t the whole story. Complex systems have many nooks-and-crannies that are not easily measured; furthermore there are many different ways in which measures can be taken and many different ways to interpret the data that have been gathered. We can’t measure, let alone predict, the exact amount of “real” money that is lost during a specific stock market downturn, nor can we determine whether or not global warning (or global climate instability) is a reality.

It seems that the tool being used to measure a complex phenomenon may have as great an impact on the outcomes of the measurement process as the nature of the phenomenon being measured. The very act of attending to a phenomenon changes it in a fundamental way when we choose to measure this phenomenon, For instance, if we measure something up close, we obtain a quite different outcome, then if we measure it at a distance: we can predict with considerable accuracy how many people in the United States will choose to eat Cheerios for breakfast today, but we cannot predict with any success if George or Susan will choose to eat Cheerios today. If we ask George or Susan if they will be eating Cheerios, then this question will itself influence their decision.