Team of Teams: New Rules of Engagement for a Complex World

by Stanley McChrystal

We will argue that the familiar pursuit of efficiency must change course. Efficiency remains important, but the ability to adapt to complexity and continual change has become an imperative.

Specifically, we restructured our force from the ground up on principles of extremely transparent information sharing (what we call “shared consciousness”) and decentralized decision-making authority (“empowered execution”). We dissolved the barriers—the walls of our silos and the floors of our hierarchies—that had once made us efficient. We looked at the behaviors of our smallest units and found ways to extend them to an organization of thousands, spread across three continents. We became what we called “a team of teams”: a large command that captured at scale the traits of agility normally limited to small teams. Almost everything we did ran against the grain of military tradition and of general organizational practice. We abandoned many of the precepts that had helped establish our efficacy in the twentieth century, because the twenty-first century is a different game with different rules.

To our way of thinking, an organization without a predictable methodology or clear chain of command wasn’t really an organization at all—from our vantage, AQI should have devolved into internal anarchy. But it didn’t. It continued to function as persistently and implacably as ever, demonstrating a coherence of purpose and strategy. We saw no evidence that this inexplicable structure was the product of deliberate design; it seemed instead to have evolved through ongoing adaptation.

an organization’s fitness—like that of an organism—cannot be assessed in a vacuum; it is a product of compatibility with the surrounding environment.

Such unpredictability has happened not in spite of technological progress, but because of it. The technological developments of recent decades are of a fundamentally different variety from those of Taylor’s era. While we might think that our increased ability to track, measure, and communicate with people like Tarek would improve our precise “clockwork universe” management, the reality is the opposite:

Being complex is different from being complicated. Things that are complicated may have many parts, but those parts are joined, one to the next, in relatively simple ways: one cog turns, causing the next one to turn as well, and so on. The workings of a complicated device like an internal combustion engine might be confusing, but they ultimately can be broken down into a series of neat and tidy deterministic relationships; by the end, you will be able to predict with relative certainty what will happen when one part of the device is activated or altered.

The significance of Lorenz’s butterfly effect is not, however, just the nonlinear escalation of a minor input into a major output. There’s uncertainty involved; the amplification of the disturbance is not the product of a single, constant, identifiable magnifying factor—any number of seemingly insignificant inputs might—or might not—result in nonlinear escalation. If every butterfly’s fluttering always led to a hurricane halfway across the world two days later, weather would be predictable (if insane). The butterfly’s fluttering leads to a storm only if thousands of other minor conditions are just right. And those conditions are so precise as to be practically immeasurable, rendering the outcome unpredictable.

There are causes for the events in a complex system, but there are so many causes and so many events linked to one another through so many direct and indirect paths that the outcome is practically unpredictable, even if it is theoretically deterministic.

the term “butterfly effect” is almost always misused. It has become synonymous with “leverage”—the idea of a small thing that has a big impact, with the implication that, like a lever, it can be manipulated to a desired end. This misses the point of Lorenz’s insight. The reality is that small things in a complex system may have no effect or a massive one, and it is virtually impossible to know which will turn out to be the case.

some systems are essentially complex (like the human brain, or society), whereas other systems (like a big machine, or a factory) might appear complex because they have a lot of moving parts, but are essentially complicated.

The average forecasting error in the U.S. analyst community between 2001 and 2006 was 47 percent over twelve months and 93 percent over twenty-four months.

Debates still rage about whether we understand economics well enough to exert even slight interventions like adjusting discount rates,

the ability to predict is the most relevant criterion, and determining exactly when things become unpredictable is tricky. All the phenomena we have discussed are at some levels predictable: We can predict the rainfall in a particular city tomorrow with relative accuracy, just not in six months. We can reliably anticipate that inflation will cause shopkeepers to raise their prices this month, but not whether it might trigger a recession in a year.

Data-rich records can be wonderful for explaining how complex phenomena happened and how they might happen, but they can’t tell us when and where they will happen.

the ritual of strategic planning, which assumes “the future will be more or less like the present,” is more hindrance than help.