Complexity-Based Targeting: New Sciences Provide Effects

Col Robert W. Freniere, USAF
Cmdr John Q. Dickmann, USN, Retired
Cmdr Jeffrey R. Cares, USNR

Editorial Abstract: Air campaign planners historically focus on levels of destruction to determine success. The authors argue that by focusing on system complexity (the degree to which the system contains interacting entities with coherent behavior) and system entropy (the amount of work lost within the system when destructive forces are introduced), planners can take advantage of both kinetic and nonkinetic approaches to degrade system function and performance. By focusing on complex system characteristics, planners can induce cascading, chaotic behavior that achieves campaign objectives more dramatically and effectively.

Throughout the modern history of bombardment, targeting philosophies have remained deeply rooted in industrial-age mind-sets and mechanistic, linear analyses of systems as engineered entities. As a result, in most significant bombing campaigns, targets have been classified by their physical attributes alone. For example, in the “serial bombing” philosophy of World War II, aircraft attacked large sets of physical targets sequentially.¹ Contemporary targeting philosophy- the “parallel warfare” employed during the Gulf War- advocates attacking targets with more simultaneity yet still focuses almost exclusively on their physical attributes and their engineered physical interactions.² In general, these targeting constructs are exceedingly inefficient, requiring inordinate amounts of “inputs” (tonnage of bullets and bombs, amounts of information warfare [IW], etc.) often not justified by or traceable to observed “outputs” (effects). Since the end of Operation Desert Storm, bombing campaigns have evolved in concept toward an objective of having specific effects on the enemy and his systems; in practice, planners still choose targets based upon engineering analyses of physical systems and physical interactions inside those systems. Little has changed.

Recent research asserts that the American military has historically misunderstood the systemic nature of targets.³ Targeting has remained inefficient and unpredictable because most targets of military value are elements in complex adaptive systems, which behave according to a radically different operating dynamic than do mechanistic systems. An evolving body of scientific work, based on understanding the emergent behaviors of large collections of interacting entities, describes the behavior of these systems. Although this body of work is collectively referred to as the “new sciences,” this article uses the terms complexity theory or complex adaptive systems theory. Whereas industrial-age Newtonian analysis focuses on classifying targets according to their physical nature, complexity theory allows targeteers to focus on how targets interrelate, particularly in nonphysical ways. Complexity-based targeting emphasizes and exploits the characteristics of complex adaptive systems.

Theory of Complexity-Based
Targeting

Two concepts from complexity theory underpin complexity-based targeting: complexity and entropy. Complexity is a measure of the degree to which a system contains large numbers of interacting entities with coherent behavior. Notionally, one can measure complexity from a value of zero to some maximum number. Zero complexity indicates a completely simple system; few entities have either minimal or no interactions. Generally, one can account for the behavior of such a system with a simple set of equations or a short description- for example, contemporary military combat models, replete with attrition equations.⁴ Entropy, on the other hand, is a measure of the amount of work lost in a system due to destructive forces such as friction or interference. One can measure entropy on a scale from zero to one- zero indicating a completely linear system that loses no work and behaves predictably. Maximum entropy designates a completely chaotic system that loses all work and behaves randomly.

As the number of possible interactions in a system increases, entropy increases- as does the number of coherent behaviors. When the system becomes more complex, predicting specific events becomes more difficult, describing what is occurring in the system takes longer, and making mathematical calculations becomes more involved. Complexity increases to a point that the interacting entities and groups of entities become too numerous and interfere with each other, and the aggregate behavior of the system becomes more random. As interference increases, so does entropy, causing complexity to fall to zero because the system’s aggregate behavior becomes simple (i.e., all behaviors can cancel each other out, and one can usefully describe the system at some higher scale in much the same way one can describe the temperature of a gas without listing the temperature of each molecule).

Between the extremes of complete linear simplicity and complete chaotic simplicity lies a wide range of complex systems, including those containing most targets of military significance. Examples include electrical distribution grids, transportation networks, communications architectures, command and control organizations, naval missile exchanges, and ground combat. We call such examples complex adaptive systems because they meet our criterion of having a large number of interacting entities that can adapt to their environment as it changes (fig.1).⁵

Figure 1. Most Complex Adaptive Systems & Figure 2. Driving a System into Lockout or Chaos

Figure 1. Most Complex Adaptive Systems

Figure 2. Driving a System into Lockout or Chaos

Complex adaptive systems are difficult to defeat because they have many groups of entities with coherent behavior. In a military context, as some entities are attacked, others change their behavior or alter their interactions, allowing the larger system to adapt. For example, if bridges in a road network are destroyed, maneuver forces will find other means- such as alternate routes, temporary bridges, or river fords- to accomplish their mission. Complexity-based targeting seeks to prevent a complex adaptive system from using its attributes and mechanisms in response to an attack.

One can prevent a system from adapting by taking away options or by removing its internal structure and coherence. The former drives the system toward linearity, where it becomes predictable, allowing one to identify the viable options and “lock them out.” The latter drives the system toward chaos (another form of lockout), where systems experience cascades of functional failure (fig. 2). Importantly, one may achieve both of these effects without using lethal kinetic force.

Comparison of Contemporary and
Complexity-Based Targeting

Contemporary targeting philosophies do not exploit the complex adaptive nature of systems. Targets in, say, a transportation network tend to be physical (table 1). Because military forces do not target the adaptive properties and mechanisms inherent in the system, they must employ brute force to drive this system into lockout or chaos. Without such an effort, the system will continue to adapt and survive. Complexity-based targeting, however, focuses not only on the physical elements, but also on the adaptive mechanisms and properties of complex adaptive systems (table 2). Thus, one may envision a transportation network as a complexity-based target set (table 3).

*Rail*	*Sea*	*Road*	*Air*
Tracks	Ports	Bridges	Runways
Switching Stations	Handling Systems	Intersections	Hangars and Equipment
Freight Yards	Fueling Equipment	Primary Roads	Fueling Capacity
Trains	Ships	Secondary Roads	Airplanes
Trestles	Docks	Vehicles	Revetments

Historically, success in attacking physical targets (table 1) has generally depended on tremendous destructive effort, usually entailing a vast tonnage of munitions or costly precision ordnance. Even if one destroys half the targets in such a system, it likely will remain functional if the adaptive properties and mechanisms survive:

Property	Mechanism
Grouping	Similar elements of the system join together for a specific function.
Membership and Identification	Groups stay together and function because they have affinities and because they can distinguish themselves from other groups.
Nonlinearity	Levers and feedback govern the system’s dynamics; manipulating them causes cascading effects.
Rule Sets	Such sets determine the behavior of groups.
Networks and Flows	Groups move throughout the system and are subject to feedback and interaction.
Competition	Groups compete with each other as they interact. Competition can be either constructive or destructive.
Building Blocks	Each group can become part of a larger group, creating significant interlocking structures in the system.

Source: John H. Holland, Hidden Order: How Adaptation Builds Complexity (New York: Perseus Printers, 1996).

Even though one may hit each system simultaneously, clever people can find new ways of working around the damage until the destruction is nearly total. However, absolute destruction of a country’s infrastructure, particularly in a conflict of less scope than a major theater war, can cause even greater problems postbellum. Destroying the ability of the system to adapt without pummeling an enemy requires different information about system behavior than that produced by most existing methods of target analysis.

Table 3
Complexity-Based Transportation Targets

*Grouping*	Membership and Identification	*Nonlinearity*	*Rule Sets*	Networks and Flows	*Competition*	*Building Blocks*
Vehicles	Mile Markers	Intermodal Interruption	Rail Schedules	Rail Network	Container v. Bulk	Radio, Radar
Containers	Boxcar ID	Power-Grid Blackout	Approach Patterns	Port Operations	Bridges v. Ferries	People
Companies	Call Signs	Weather	Rules of the Road	Interstate Road System	Trucks v. Trains	Wheels, Tracks
Shipping Lines	Vessel Flags	Traffic Jams	Traffic Lights	Bridges	Commodity v. Retrograde	Vehicle Types
Yards, Ports	Road Signs	Commodity Flow	Commodity Requisitions	Off-Ramps		Transportation Subnets
Channels	Runway Markers			Fuel Stations		Engines
Routes	Tactical Air Navigation/ Identification, Friend or Foe					Fuel
Warehouses

Complexity-based targeting offers a different perspective on the target system or systems by focusing on the interrelationship of elements with the larger system. One devotes particular attention to those properties and mechanisms that account for coherent behavior in the system. This type of targeting provides a longer list of targets than contemporary targeting methods (compare tables 1 and 3) but does not necessarily mandate more effort. In fact, complexity-based targeting provides more information about the behavior of the entire target system, allowing one to more reliably identify and more logically derive the desired effects. In addition, one can coordinate kinetic, nonkinetic, and IW methods across the target set- a more economical approach than the current “stovepiped” application of these means. To produce a complexity-based target set (such as the one in table 3), one must

Because one targets the adaptation properties and mechanisms, simultaneous attack across each of the seven categories (table 3) will prove more effective in preventing system recovery than a high-volume, rapid attack on just the physical components of a system. Moreover, the fact that an enemy must defend in seven dimensions, rather than in only the physical dimension, significantly complicates his task. Once again, complexity-based targeting focuses on and exploits the complexity of an enemy’s system to drive it into either lockout or chaos.

Conclusions

Historically, targeting philosophies have reflected a mechanistic, industrial-age mind-set, but complexity-based targeting utilizes a more holistic, systems-oriented approach. In particular, it identifies the information that each element needs to function in a system with other elements. As a result, complexity-based targeting unifies kinetic and nonkinetic methods of attack, proves significantly more effective because of its close coupling of targets to desired effects, and successfully disables a system without destroying every physical target. Such a method provides more useful, systemic knowledge of a target set and uses that knowledge to lock out most of an enemy’s courses of action or rout that enemy by transforming coherent behavior into chaos and confusion.

1. For a critique of industrial-web targeting concepts, see Maj Steven M. Rinaldi, “Beyond the Industrial Web: Economic Synergies and Targeting Methodologies” (thesis, School of Advanced Airpower Studies, Maxwell AFB, Ala., June 1994).

2. David A. Deptula, Firing for Effect: Change in the Nature of Warfare (Arlington, Va.: Aerospace Education Foundation, 24 August 1995), 2. The term parallel warfare was coined by the Air Force Directorate of Warfighting Concepts Development (AF/ XOXW). The concepts themselves were based on the ideas of Col John A. Warden III, USAF, retired. See his article “The Enemy as a System,” Airpower Journal 9, no. 1 (spring 1995): 41–55.

3. John F. Schmitt, “Command and (out of) Control: The Military Implications of Complexity Theory,” Marine Corps Gazette 82, no. 9 (September 1998): 55–58; Tom Czerwinski, Coping with the Bounds: Speculations on Nonlinearity in Military Affairs (Washington, D.C.: National Defense University, 1998); Alan Beyerchen, “Clausewitz, Nonlinearity, and the Unpredictability of War,” International Security 17, no. 3 (winter 1992–1993): 59–90; and Rinaldi.

4. Indeed, historically, combat models have been poor descriptors of combat because the latter is often much more complex than the simple equations suggest. Still, these simple models abound.

5. James P. Crutchfield and Karl Young, “Computation at the Onset of Chaos,” in Complexity, Entropy and the Physics of Information, ed. Wojciech H. Zurek (New York: Addison-Wesley Publishing Company, 1990), 8: 224–27.

Cmdr John Q. Dickmann, USN, retired (USNA; MEng, Catholic University of America), is an operations analyst for Alidade Consulting. Previously, he was an operations analyst on the staff of the chief of naval operations’ Strategic Studies Group. He has served on attack and ballistic-missile submarines, on the staffs of the chief of naval operations and the commander of Navy Warfare Development Command, and at the Center for Naval Analyses. Commander Dickmann was also a staff instructor in electrical engineering at the Naval Nuclear Power School.

Col Robert W. “Lightning” Freniere (BS, The Citadel; MA, MS, University of Cincinnati; MS, US Naval War College) is chief of Strategic Studies Group I, Future Concept Development Division, Directorate of Strategic Planning, Headquarters US Air Force. He previously served as chief of intelligence, 439th Airlift Wing, Westover AFB, Massachusetts, and assisted with policy analysis for the Department of Defense’s Long-Range Master Plan for Combating Terrorism. Colonel Freniere was the first Air Force officer brought into the chief of naval operations’ Strategic Studies Group, serving on SSG XVIII.

Cmdr Jeffrey R. Cares, USNR (BA, Vanderbilt University; MS, Naval Postgraduate School; MA, Norwich University), is assistant chief of staff for operations, plans, and policy with the Naval Reserve Navy Command Center, Unit 106, where he is responsible for Reserve operational support to the highest levels of naval command for Operation Enduring Freedom. As a civilian, he is the president and chief executive officer of Alidade Consulting, a defense consulting service on future warfare concepts and transformation. He is also director of military programs for the New England Complex Systems Institute in Cambridge, Massachusetts. A graduate of the Naval War College and the Joint Forces Staff College, Commander Cares previously served as an operations research analyst for the chief of naval operations’ Strategic Studies Group.

The conclusions and opinions expressed in this document are those of the author cultivated in the freedom of expression, academic environment of Air University. They do not reflect the official position of the U.S. Government, Department of Defense, the United States Air Force or the Air University.