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January 19 - February 14, 2020
many people do things in inefficient ways, and that small inefficiencies multiplied at industrial scale reduce productivity.
Peter Drucker, the sage of modern management, argued that without Taylor’s innovations, America would have been unable to defeat the Nazis.
that “by the late 1920s, it could seem that all of modern society had come under the sway of a single commanding idea: that waste was wrong and efficiency the highest good, and that eliminating one and achieving the other was best left to the experts.”
We still search faithfully for the one best way to do things; we still think of organizational leaders as planners, synchronizers, and coordinators—chess-player strategists responsible for overseeing interlocking troop movements, marketing initiatives, or global supply chains.
Whether imbued with a “lazy worker” Theory X or a “motivated worker” Theory Y disposition, the “org charts” of most multiperson endeavors look pretty similar: a combination of specialized vertical columns (departments or divisions) and horizontal tiers that denote levels of authority, with the most powerful literally on top—the only tier that can access all columns. At the top, we envision the strategic decision making. At the bottom, we imagine action by those taking direction.
reductionism laid the foundation of contemporary management.
We believe that the reductionist sum of everyone being their “most productive” will lead to the best overall results. We love the idea of a “best practice.”
Peter Drucker argued that Taylor, more than Karl Marx, deserves a place in the pantheon of modern intellectual thought alongside Darwin and Freud.
It is because of these changes that the Task Force’s “awesome machine,” excellent by all twentieth-century metrics, was failing.
When, several years later, Lorenz presented a paper about his findings, he titled it “Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?” The phrase “the butterfly effect” entered the world.
Things that are complex—living organisms, ecosystems, national economies—have a diverse array of connected elements that interact frequently. Because of this density of linkages, complex systems fluctuate extremely and exhibit unpredictability. In the case of weather, a small disturbance in one place could trigger a series of responses that build into unexpected and severe outcomes in another place, because of the billions of tiny interactions that link the origin and the outcome. In an ecosystem, one slightly mutated virus may spread like wildfire, causing a huge population depletion that, in
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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;
The density of interactions means that even a relatively small number of elements can quickly defy prediction.
Because of these dense interactions, complex systems exhibit nonlinear change.
line. A reductionist instruction card would be useless for playing chess—the interactions generate too many possibilities.
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
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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 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.
Products, events, nations, phenomena, and individuals have become more connected to, dependent on, and influenced by one another than ever before.
As a result, treating an ecosystem as though it were a machine with predictable trajectories from input to output is a dangerous folly.
He argued that national economies, unlike industrial production, could never be transformed into mechanical systems with reductionist solutions: their behavior results from the decision making of millions of people, and all these decisions influence one another, making it impossible to forecast how markets will move—as in a game of chess, there are just too many possibilities for a prescriptive instruction card. Butterfly effects in the economy, triggered by tiny initial disturbances, are common.
A predictive hubris, perhaps bred by centuries of success at applying Newtonian models to complicated problems, has fooled us into believing that with enough data and hard work, the complex riddles of economies can be decoded.
Attempts to control complex systems by using the kind of mechanical, reductionist thinking championed by thinkers from Newton to Taylor—breaking everything down into component parts, or optimizing individual elements—tend to be pointless at best or destructive at worst.
The predictability of this environment enabled Taylor to break complicated processes down into independent, repeatable actions and, at a larger scale, to divide whole organizations into independent departments. Because he could anticipate that tomorrow would bring the same eight varieties of pulp as today, he could reduce the chemistry of papermaking to a simple chart; because he knew that the same machines would be in place with the same flow of water, he could give workers precise instruction cards for their actions.
The baseline belief that any problem can be known in its entirety has never faded.
In Iraq, we were using complicated solutions to attack a complex problem.
For decades we had been able to execute our linear approach faster than the external environment could change, and as a result we believed we were doing something different from other organizations. In fact, we were as bureaucratic as anyone else; we were just more efficient in our execution. Efficiency was the defining excellence of our “awesome machine,” and it had enabled our assembly line of counterterrorism to keep humming along.
But by 2004, the world had outpaced us. In the time it took us to move a plan from creation to approval, the battlefield for which the pl...
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By the time it could be implemented, the plan—however ingenious in its initial design—was often irrelevant. We could not predict where the enemy would strike, and we...
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As with Lorenz’s butterflies, it was impossible to tell which events would lead to what kinds of results.
Meteorologists looking to predict the weather might think that forecasts might be perfected if they could just get enough information about butterfly wings.
Gaining understanding is not always the same as predicting.
We have moved from data-poor but fairly predictable settings to data-rich, uncertain ones.
We were stronger, more efficient, more robust. But AQI was agile and resilient. In complex environments, resilience often spells success, while even the most brilliantly engineered fixed solutions are often insufficient or counterproductive.
resilience as “the capacity of a system to absorb disturbance and still retain its basic function and structure.”
However, as with the Maginot Line, a robust protection against a known threat is not always sufficient; in complex systems, threats can flow from many places.
Room for the River accepts the reality that floods are inevitable, representing a shift in mentality from making the Netherlands floodproof to making it flood resilient.
Other countries and organizations are now following suit, stepping away from predictability and focusing on increasing resilience instead.
In a resilience paradigm, managers accept the reality that they will inevitably confront unpredicted threats; rather than erecting strong, specialized defenses, they create systems that aim to roll with the punches, or even benefit from them.
“antifragile systems.” Fragile systems, he argues, are those that are damaged by shocks; robust systems weather shocks; and antifragile systems, like immune systems, can benefit from shocks.
Resilience thinkers argue that we have inadvertently “fragilized” many of the systems that surround us. Our urge to specialize, reap efficiencies, and impose our demands for unnatural predictability has, like the rerouting of the Rhine, created new threats and damaged our ability to bounce back.
Robustness is achieved by strengthening parts of the system (the pyramid); resilience is the result of linking elements that allow them to reconfigure or adapt in response to change or damage (the coral reef).
from predicting to reconfiguring.
all the efficiency in the world has no value if it remains static in a volatile environment.
We had built an “awesome machine”—an efficient military assembly line—but it was too slow, too static, and too specialized—too efficient—to deal with that volatility.
We were robust, but not resilient.
If you have enough foresight to know with certainty what the “right thing” is in advance, then efficiency is an apt proxy for effectiveness. In the wayward swirl, however, the correlation between efficiency and effectiveness breaks down. The Task Force had built systems that were very good at doing things right, but too inflexible to do the right thing.
We needed to get the right things in the right place with speed and accuracy, so we could seize opportunities that might evaporate in just a few minutes. In effect, we needed a system that, without knowing in advance what would be required, could adapt to the challenges at hand; a system that, instead of converting a known x to a known y, would be able to create an unknown output from an unpredictable input.
many of the practices that are most efficient directly limited adaptability.
The chains of command that once guaranteed reliability now constrained our pace; the departmental dividers and security clearances that had kept our data safe now inhibited the exchanges we needed to fight an agile enemy; the competitive internal culture that used to keep us vigilant now made us dysfunctional; the rules and limitations that once prevented accidents now prevented creativity.
