The use of unmanned aerial vehicles (UAVs), or drones, to carry out targeted killings of suspected terrorist leaders has become a centerpiece of the United States’ global counter-terrorism strategy. Each new report of a strike in Pakistan, Yemen, or Somalia provides new fodder to critics, who point to civilian casualties and the resulting anger at the United States as evidence that the strategy is counterproductive in the long term. Recently, one critic, Col. Gary Anderson, USMC (ret.), raised an even more fundamental question: “Why…do we think that targeting what we consider key terrorists with drone strikes will bring down their network as a whole?”
I address this question in my recently published book, Nonlinear Science and Warfare: Chaos, Complexity, and the U.S. Military in the Information Age. In it, I demonstrate that notions of collapsing networks by targeting key nodes is one of a number of lessons that the United States defense community has learned from its enlistment of language and concepts borrowed from nonlinear sciences such as chaos theory and complexity theory.
Since the Enlightenment, Western militaries have drawn inspiration from the natural sciences. Science is seen as a valuable body of knowledge for military professionals, a model for thinking about war, and a valuable source of language and concepts for speaking about war. This is reflected in the emergence of schools of professional military education, which initially emphasized the importance of a technical and scientific education for officers, as well as the proliferation of research centers like RAND and the Center for Naval Analyses. It is reflected in any number of treatises that purport to have discovered the “principles of war” based on scientific investigation. Finally, it is reflected in the common use of metaphors like “mass,” “momentum,” and “friction” to speak about war.
In the United States, nonlinear science began to capture the attention of military theorists and doctrine writers during the 1980s. During the 1990s, the U.S. defense community became increasingly interested in finding lessons in nonlinear science that could guide the U.S. military through the seemingly volatile, uncertain, complex, and ambiguous post-Cold War environment. At the turn of the new century, and in the wake of the terrorist attacks of September 11, 2001, lessons learned from nonlinear science shaped the theories, strategies, and doctrines that have guided U.S. military action in the “war on terrorism.”
One result of attempts to learn lessons from nonlinear science is the belief that terrorist organizations are network structures that behave like complex, adaptive systems. Such systems can be highly flexible, adaptive, and resilient, making them particularly dangerous foes. On the other hand, they can also suffer catastrophic collapse if struck in just the right spot to cause cascading failures. That right spot is often identified as one or more “key nodes” in the enemy network. In turn, those nodes are often the most highly connected individuals in the enemy organization. This is the logic behind why the U.S. believes that targeting key terrorists can collapse entire terrorist networks.
Where military use of nonlinear science is concerned, there are two things of note in Col. Anderson’s essay. First, by drawing from the language and concepts of nonlinear science to critique U.S. use of drones, his essay indicates a continued interest in nonlinear science on the part of some in the U.S. defense community. He notes that a drone strike against principal national security leaders in the United States would not collapse the country, just as the 9/11 attacks did not. The reason, he says, “is because the United States is a complex, adaptive system. Such systems don’t have single or even multiple points of failure.” This is also the reason, he says, why such strikes against terrorist networks are doomed to failure: “al Qaeda and its affiliates are also complex, adaptive systems.” While we strike terrorist leadership targets with drones and tell ourselves were are collapsing networks, instead those networks spread and grow stronger.
This continued interest in nonlinear science is unsurprising. As I demonstrate in the book, lessons supposedly learned from nonlinear science were central to the strategies and doctrines underlying the United States’ invasion of Iraq in 2003. They were also prominent in theories and doctrines of counterinsurgency meant to snatch victory from the jaws of defeat after the mission turned out not to have been accomplished as quickly or bloodlessly as the Bush administration had initially believed. Finally, nonlinear science figured prominently in one of the most important critiques of the United States’ post–9/11 foreign policy and battlefield strategy, the theory of Fourth Generation Warfare, to which Col. Anderson has made his own contributions.
The second thing of note, however, is that Col. Anderson’s essay inadvertently points to the limitations of drawing clear-cut lessons from nonlinear science for policy making and strategy formation. The notion that complex networks can be subject to cascading failures is drawn from the popularized literature on nonlinear science. But so is Col. Anderson’s observation that these networks can also display a remarkable degree of resilience. That is, both are correct observations about the nature of complex systems. But these and other observations about the nature of complex systems have led smart, well-meaning individuals to reach exactly the opposite conclusions about policy and strategy.
For centuries now, there has been a “battle for truth” in Western militaries, including the United States military. In this battle, the natural sciences have won an important victory and are now widely accepted as an appropriate method for learning the truth of war. But what to do as a result of that truth, how to translate that truth into policy and strategy, is not always a straightforward endeavor. The fact that proponents and critics of U.S. strategy both draw from nonlinear science, often from the same observations about the nature of complex systems, indicates that nonlinear science does not provide simple answers. Before we can learn lessons from nonlinear science that will help us fix the problems with our current strategy, we must first recognize that the current strategy is, in part, a result of prior attempts to learn lessons from nonlinear science.
[This post also appears at Forbes.com.]
