Anticipatory Intelligence and 

Adaptive Influence: A New Paradigm 

for Foreign Policy Development 

Josh Kerbel

LTC Tom Pike, USA

Complexity theory reveals a nation is not just a people with a 

government; it arises from competition and the compromises 

continually being made by the many groups at many levels within 

it. Anticipatory intelligence that combines this complex system

perspective with today’s powerful computational tools can provide the 

deeper insights needed to advance U.S. foreign policies, particularly 

promoting democracy, to adaptively influence the behavior of foreign 

populations. 

RESEARCH SHORT

CATALYST Sparking constructive conversations 

about the future of intelligence 

July 10, 2020

What this is: 

This report is the product 

of academic research. As the 

IC’s university, NIU is uniquely 

positioned to use academic 

approaches to research—and 

report on—subjects of interest 

to the community.

What this is not: 

Research Shorts are not

finished intelligence and are 

not IC-coordinated. The 

opinions expressed are solely 

the authors’ and do not 

represent those of the National 

Intelligence University, 

Defense Intelligence Agency, 

Department of Defense, or 

any other U.S. Government 

agency.

IMAGE FROM SHUTTERSTOCK
2

U.S. policymakers are continually pressing the IC to pursue more 

effective means of understanding complex challenges to better 

manage foreign affairs. What are the best measures to mitigate a 

global pandemic while maintaining a functional society? How do 

brutal dictators in Iran and North Korea retain power despite 

crushing isolation and sanctions? How can the Taliban continue 

to undermine the overwhelming military strength of the United 

States and its allies? Looking back, how did Mohammed 

Bouazizi, a seemingly irrelevant Tunisian fruit stand vendor, 

ignite the Arab Spring and how could the Islamic State, an alQa‘ida splinter group, grow to control 34,000 square miles of 

territory across the Middle East? 

Although social and behavioral scientists, as well as military strategists, have applied a variety 

of widely respected and useful methods aimed at better understanding populations for the 

purpose of policy formulation, 1 , 2 , 3 complex adaptive systems theory, also known as 

complexity theory, argues that the inherent reductionism of these approaches results in 

information loss that undermines their overall efficacy.

This Research Short argues that fusing complex adaptive systems theory with innovations in 

advanced computational methods can help transcend this problem. Complexity theory argues 

that the ability to address the above foreign policy challenges lies in understanding the 

interdependencies of a foreign state’s or region’s subcomponents (e.g., ethnic groups, 

businesses, political parties)—interactions that get filtered out with traditional, reductionist, or 

formulaic ways of understanding human behavior.4, 5, 6 By applying advanced computational 

methods and techniques to simulate the interdependent relationships of the systems’ 

subcomponents, researchers can generate virtual laboratories to test their understanding, which 

often uncovers counterintuitive insights. To understand the profound implications of these 

ideas, it is necessary to develop an understanding of and intuition for complex systems.

Complex Adaptive Systems

The study of complex systems and its applicability to anticipating human behavior dates back 

to the ancient Greeks, but traditional scientific approaches have failed to capture complex 

systems’ interdependent nature. Governments, public health systems, societies, and other 

complex systems are multilevel and multidimensional adaptive networks. 7 Societies, for 

example, exist at multiple levels: individuals compose families that compose neighborhoods 

that compose cities, and so on. Furthermore, multidimensional networks operate inside each 

level: individuals exist in family networks, friend networks, business networks, information 

networks, and political networks. These networks constantly transform as individuals, 

businesses, political parties, and other cohesive entities alter their behavior, which shifts 

their interdependencies with their environment, allies, competitors, and suppliers.8, 9, 10, 11, 12

These interdependencies mean a society’s behaviors cannot be understood by examining 

NIU’s Center for Anticipatory 

Intelligence and Adaptive Influence 

(C(AI)2) applies complexity theory 

and advanced computational methods 

to better understand intelligence 

problems and improve policymaking

to bridge an enduring gap in the 

academic-government nexus, bringing 

research and innovation to practical 

government application.
3

sub-elements in isolation—as is done in the reductionist approach that has dominated science. 

Splitting the whole to examine its individual parts breaks the interrelationships that produce 

the behaviors of interest. 

Studying such a rich and dynamic tapestry of relationships has 

been crushingly difficult, but the advent of advanced 

computational power offers a path forward. Before the digital 

computer, mathematical attempts to understand even simple 

versions of these types of interactions were so cumbersome 

they were ignored.13, 14 The computer’s ability to brute force 

its way through large numbers of calculations has greatly advanced the study of complex 

systems15, 16 in ways that have allowed us to fundamentally rethink some of the most basic 

processes of policy development. (See Appendix.) 

Democracy Viewed Through Complexity

Exploring the dynamics of democracy formation through a complex systems lens illustrates the 

challenges of understanding those complex systems and developing effective foreign policies 

that influence their behavior. Political systems embody the elegant nature of complex systems 

and the delicate balance that can exist within them. Democracy, for example, epitomizes the 

complexity concept known as the edge of chaos—the transition space between order and chaos. 

An easily understandable metaphor is to think of water in liquid form as the transition space 

between ice—a frozen, ordered regime where every molecule is locked in place (e.g., 

autocracy)—and vapor, where molecules are chaotically careening in all directions (e.g., 

anarchy). Ubiquitous in the universe, the edge of chaos is where life thrives and complex 

systems exist. Maintaining the edge is critical to life’s origins and its maintenance. If the 

universe’s matter had been too densely packed, no complex activities would have emerged, 

and if it had been too dispersed, no matter would have come together to create the complex 

elements that produced life. Similarly, if genomic networks were either too ordered or too 

chaotic, they would not be able to coordinate the essential biological activities needed for life.17

This concept should also resonate with anyone who has worked in an organization. Too many 

rules, too much bureaucracy—and the organization grinds to a halt because no new approaches 

can pass through the overly rigid structure. Conversely, a chaotic organization cannot identify 

who does what, so nothing happens. In the political realm, a democratic governance system 

best maintains society at the edge of chaos, because it enables the population to solve the hard 

problems 18 , 19 that promote broader prosperity. 20 Democracy offers problem solvers the 

flexibility to innovate (chaos) and sufficient structure to capitalize on their innovation (order). 

Understanding governance as a society’s attempt to find an optimal dynamic between order 

and chaos has profound implications. 

The universe’s tendency to thrive between order and chaos transmutes the rational and moral 

justifications for democratic forms of government into inherent laws and natural dynamics.21 The 

edge of chaos idea is profound in its revelation that a multitude of individuals and organizations 

Democracy, for example, epitomizes 

the complexity concept known as the 

edge of chaos—the transition space 

between order and chaos.
4

at multiple levels—all competing and compromising for their self-interest—is able to find 

solutions and solve complex problems more effectively than seemingly more efficient problemsolving regimes, such as a benevolent dictatorship.22 The challenge with the edge of chaos 

concept, much like complexity in general, is that its principles are not well understood at the level 

of detail needed to develop policy. The edge of chaos provides a frustrating heuristic for foreign 

policy because no known laws exist akin to Newton’s in physics that define the mix of structure 

and flexibility to optimize governance. Even simple computational experiments to explore the 

concept have proven controversial.23, 24, 25

Nevertheless, the edge of chaos can provide insight into the development of U.S. efforts to 

promote democracy abroad that would challenge conventional approaches because the theory 

redefines the concepts of state and government. Understanding democracy as a balance 

between order and chaos forces policymakers to look at a nation-state not as a homogenous 

entity being governed but as competing interests held by layers of groups and individuals 

influenced by their associated governance, both formal and informal. This layered complexity 

also gives validity to the ongoing battle in international relations theory, where different camps 

argue for the primacy of their particular approach (e.g., foreign policy realists versus domestic 

politics and elite decisionmakers). Complexity would contend they are each correct, but the 

details within the foreign system will determine the main driver(s) for specific situations. The 

abstraction of complex systems as adaptive networks is an effective perspective to make sense 

of those alliances and competitions among individuals, groups, cities—and on up to nations.

Governance and Networks

The power of a population’s network structure and the governance of the flow of resources 

across it shape our world. One stark example is China’s and Europe’s differing rates of 

historical development. Although the Chinese invented the printing press and gunpowder 

centuries before Europeans, these inventions had far less impact in China, in large part because 

of different network structures. Information and inventive energy easily flowed across 

Europe’s network of interconnected kingdoms, while the Emperor maintained tight control of 

information and imperial technologies under China’s tightly controlled hub and spoke 

network. 26 Understanding these network dynamics provides deeper insight into societal 

behavior and changes over time. 

A similar network analysis can reshape how one understands the impact of U.S. aid, moral 

suasion, or military action and can reveal how Washington’s policies can, counterintuitively, 

work against U.S. interests. The traditional approach would conclude that the best way to support 

a foreign “government” would be to provide resources to support it. From a complex systems 

perspective, however, it becomes clear that the resources the United States provides go to a 

subgroup or set of groups in conflict with other groups pushing competing interests. The United 

States becomes a wealthy new node in the network that alters the foreign groups’ internal 

competition because the group the United States supports has less incentive to compromise with 

other groups to form a more broadly supported and stable long-term government.27The supported 
5

group can compel others to adopt measures that would solidify its hold on power, encouraging 

autocracy—as was seen during the Karzai governance of Afghanistan.28 By altering the flow of 

resources within a country, U.S. Government policy changes how those foreign networks evolve. 

The complex systems perspective suggests the United States will better achieve its democracy 

promotion objectives if it pursues policies that maintain balance among competing groups, a 

vastly more difficult challenge than picking potential winners. 

Anticipate and Adapt

The new perspectives for understanding population dynamics that complexity theory offers 

will require a new intelligence analysis and policy formation paradigm. 

• The term anticipatory intelligence must reclaim its original link to foreseeing the 

behavior of complex challenges. In this context, it means to assess the competition 

dynamics within foreign populations to anticipate possible outcomes, including those 

driven by U.S. policy.29 Critically, these systems are “deterministically unpredictable,” 

much like the weather. Because there is always a point after which the dynamics cannot 

be predicted,

30 the IC must strive to anticipate what is possible—but recognize that the 

future is unknowable and that policy will always have unintended consequences. 

• This unpredictability and the unintended consequences inherent in complex systems 

mean policy must have adaptive influence, meaning foreign policy’s purpose is to 

influence the adaptive path of a foreign population by altering its interdependencies, 

its flow, and its resources across its network to create a balance of competing interests 

that promote a healthy democracy. Policymakers will need to constantly adapt their 

policies as the unexpected occurs. 

What specifically this anticipatory intelligence and adaptive influence look like, how long

policies may last, or how often they need to adapt is unknown and will vary in each situation.

Anticipation and Adaptation Require Computation

Accepting this new complex systems paradigm presents the practical question of how can those 

tasked with developing foreign policy understand the competing interests and internal 

governance mechanisms necessary to create effective policy? It is here that computation 

becomes critical. Computation is merely the process of following a pre-defined set of 

instructions—mathematical or not—and is now synonymous with computers, which can brute 

force their way through billions of instructions per second. Computation is a tool for complex 

systems exploration, much like an astronomer needs a telescope to see distant stars or a doctor 

needs a stethoscope to listen to the heart and lungs. Understanding and influencing the 

competitive dynamics of a foreign population requires computational support for three reasons.

First, the sheer size of the problem demands computational tools be part of analysis and policy 

development to exploit their processing capability. Humans can process in their heads or on 
6

paper only a small portion of the complex web of competing interests that form a nation, and 

this task becomes exponentially harder when those competing interests are adapting. 

Computational tools are the only way to process not only the interactions of a foreign system’s 

main groups and their adaptations but also the interactions and adaptations of the families, 

villages, cities, regions, and other entities that make up that country. Just as computers are 

required to process the massive amounts of data now produced, computers are needed to 

simulate the layers of individuals and groups of concern and their adaptation.

Second, computational power is needed to simulate the interactive mechanisms of the various 

groups to see if these interactions can be influenced to form a democracy. Foreign populations 

must be understood as a set of interactive mechanisms31 that defy simple analytic equations 

(e.g., E= MC2 or A2+B2=C2). If behavior emerges from the interactions of the groups within a 

foreign system, then the challenge is to determine what essential mechanisms must exist within 

that system to produce behavior consistent with U.S. foreign policy goals. Finding, describing, 

and understanding these mechanisms requires IC professionals to synthesize the local 

behaviors and understand those behaviors’ relationships with the emergent properties of the 

system. This is a subtle but crucial difference compared to the analytic approach which assumes 

the pieces are directly related to the system behavior. This analytic approach can be seen in 

frameworks like PMESII (Political, Military, Economic, Social, Infrastructure, Information) 

that assume to understand a population an analyst must describe each part (e.g., P+M+E+S+I+I 

= foreign nation). Policy formation is equally linear in its approach; for example, enact laws to 

encourage a population to adapt from a controlled to an open economy. 32 Both are 

inappropriate for understanding and influencing complex, nonlinear systems that require 

synthesizing the interactions of competing elements and then trying to understand the 

relationships of this synthesis to the emergent behavior, which may be counterintuitive. As the 

saying goes “if you didn’t grow it [simulate the micro interaction of the agents and produce the 

same macro phenomenon] then you didn’t understand it...”33

Third, details matter. A team of analysts and policy developers could hand-process a few 

groups and interactive mechanisms to see how a system could function, but understanding the 

diversity within the population matters. In one of the first attempts to apply computational tools 

to real life dynamics, analysts replicated the archaeological record of Long House Valley in 

Arizona. Long House Valley was home to the Kayenta Anasazi, the ancestors of the southwest 

indigenous tribes now known as the Pueblo. The model sought to replicate the archaeological 

record from 800 to 1350 Common Era, which included abandonment of the area by the Anasazi 

around 1300. The modelers programmed their agents to replicate aggregate population 

statistics such as life span, birth rates, etc., to recreate the Long House Valley population. 

However, the model could not replicate the archaeological record until the modelers diversified 

their population. It mattered whether one person’s life was longer or shorter than another’s, or 

that a person gave birth earlier or later. Once this diversity was added, the simulation matched 

the record.34 Eloquently, this model showed the unique experiences of an individual life matter.

Moving from a manually produced sparse understanding to computationally supported detailed 

understanding will provide greater insights into the dynamics that are producing the emergent 
7

behavior within the complex system of interest. Ideally, intelligence professionals across 

government could rapidly simulate a particular complex system to use as a virtual laboratory—

enabling them to better understand system behavior and allowing policymakers to test new 

policies in silico before implementing them. This approach will ensure more rigor and conserve 

U.S. resources, as policies that expend the minimum resources for maximum benefit can be more 

easily identified. The challenge is “democratizing” these simulations, so they become more 

pervasive than PowerPoint. They also must be supported by a vibrant ecosystem that constantly 

allows the users to bring in new knowledge. Although it goes beyond the scope of this paper, 

there are nascent efforts to build the necessary supporting ecosystem to make this vision a reality. 

Even if this vision becomes a reality, however, there will always be more details than analysts 

and U.S. foreign policy entities can capture. Computational tools are understanding aids, not 

answer machines. They will provide more rigor and insight than had been possible but will not 

give the users the perfect policy or replicate the situation exactly. Although the routine and 

widespread use of simulations will not be perfect, they have the potential to reveal insights as 

significant as the discovery of bacteria or the structure of the human genome. 

New Paradigms for Foreign Policy

Complexity theory reveals nations are not just a people with a government; nations arise from 

competition and the compromises continually made by the many groups at many levels within 

them. Basing assessments and implementing policy on this new perspective will provide better 

understanding and more effective policy. Anticipatory intelligence and adaptive influence 

capture this approach. Examining the fundamentals of complex systems reveals ways to 

improve the foundational concepts that guide intelligence and foreign policy. Within this 

understanding is a tremendous number of unanswered questions about the dynamics and 

features of complex adaptive systems, but by exploring them, we can understand them. 

Adopting and integrating these new paradigms challenges an IC that must continue to produce 

analysis and assessments and cannot afford to fundamentally rethink every aspect if its 

embedded processes. Complex systems theory would argue the only way to proceed is to 

nurture new species (i.e., start-ups) of intelligence entities attempting different variations of 

these ideas and allowing them to mature. This approach is consistent with both ecological 

systems and capitalist economies. These IC “start-ups” must be groups of passionate 

individuals trying the seemingly impossible—or at least the very difficult. Although many will 

fail, those that succeed will bring about new paradigms that will allow the United States to 

maintain its global competitive advantage. 

Josh Kerbel and LTC Tom Pike are faculty members at the National Intelligence University where they 

also serve as co-directors of the Center for Anticipatory Intelligence and Adaptive Influence C(AI)2.

If you have comments, questions, or a suggestion for a Research Short topic or article, please contact 

the NIU Office of Research at NIU_OOR@dodiis.mil.
8

Appendix: A First Step Forward

With complexity theory penetrating deeper into the social sciences and the barriers of entry to 

computational tools dropping,35, 36, 37 the ability to provide U.S. policymakers with greater 

understanding of the environment facilitating more effective foreign policy initiatives is greatly 

enhanced. But, one of the great paradoxes of complex systems science is that at the same time 

that computers make it possible to effectively grapple with complexity, those same computers 

and their associated networks also increase the very complexity in question. Nonetheless, the 

competitive nature of international relations requires the IC, think tanks, and academic 

researchers to aggressively apply complex system concepts to their analysis to broaden 

understanding and enable policymakers to take more effective action. This may well be only a 

first step as the dynamics of complex systems are poorly understood and further investigation 

may result in fundamentally new scientific conceptualizations.
9

Endnotes

1 Claudio Cioffi-Revilla, Introduction to Computational Social Science: Principle and Applications, eds. Fred B. 

Schneider, David Gries, and Orit Hazzan (Cham, Switzerland: Springer, 2017), https://doi.org/10.1007/978-3-319-

50131-4.

2 James Gleick, Chaos: Making a New Science (New York: Penguin, 1987).

3 Ilya Prigogine and Isabella Stengers, Order Out of Chaos: Man’s New Dialogue with Nature (New York: Bantam 

Books, 1984).

4 Robert Axtell, “Why Agents?: On the Varied Motivations for Agent Computing in the Social Sciences,” 

The Brookings Institution, Center on Social and Economics Dynamic, Working Paper No. 17, 2000, 1–23, 

https://www.brookings.edu/research/why-agents-on-the-varied-motivations-for-agent-computing-in-the-socialsciences/.

5 Cioffi-Revilla, Introduction to Computational Social Science.

6 Volker Grimm et al., “Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology,” 

Science 310, no. 5750 (2005): 987–91, https://doi.org/10.1126/science.1116681.

7 David Krakauer, “The Abstractions of Collectives,” Presentation at the Computational Social Science Society of 

the America Annual Meeting, Santa Fe, 2017.

8 Eric D Beinhocker, The Origin of Wealth (Boston: Harvard Business School Press, 2006).

9 Cioffi-Revilla, Introduction to Computational Social Science.

10 John H. Miller and Scott E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social 

Life 27 (2007), http://books.google.com/books?id=XQUHZC8wcdMC&pgis=1.

11 Melanie Mitchell, Complexity: A Guided Tour (Oxford: Oxford University Press, 2009).

12 Prigogine and Stengers, Order Out of Chaos.

13 Gleick, Chaos.

14 Prigogine and Stengers, Order Out of Chaos.

15 Edward Lorenz, “Deterministic Nonperiodic Flow,” Journal of the Atmospheric Sciences 20, no. 2 (1963): 130–41,

https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.

16 Cioffi-Revilla, Introduction to Computational Social Science.

17 Stuart Kauffman, At Home in the Universe: The Search for the Laws of Self-Organization and Complexity (Oxford: 

Oxford University Press, 1995).

18 Kauffman, At Home.

19 Miller and Page, Complex Adaptive Systems.

20 Kauffman, At Home.

21 Kauffman, At Home.

22 Kauffman, At Home.

23 Chris G. Langton, “Computation at the Edge of Chaos: Phase Transitions and Emergent Computation,” Physica D 

42 (1990): 12–37, https://doi.org/10.1016/0167-2789(90)90064-V.

24 Miller and Page, Complex Adaptive Systems.

25 Melanie Mitchell, Peter Hraber, and James Crutchfield, “Revisiting the Edge of Chaos: Evolving Cellular 

Automata to Perform Computations,” Complex Systems 7 (1993): 89–130.

26 Hilton Root, Network Origins of the Global Economy: East vs. West in a Complex Systems Perspective

(Cambridge: Cambridge University Press, 2020).

27 Bruce Bueno De Mesquita et al., The Logic of Political Survival (Cambridge, Massachusetts: The MIT Press, 

2003).

28 David Kilcullen, Out of the Mountains: The Coming Age of the Urban Guerilla (Oxford: Oxford University Press, 

2013).

29 Josh Kerbel, “Coming to Terms with Anticipatory Intelligence,” War on the Rocks, August 2019,

https://warontherocks.com/2019/08/coming-to-terms-with-anticipatory-intelligence/.

30 Steven H. Strogatz, “Chaos,” Great Courses (DVD) Science & Mathematics, no. PD1333, (The Teaching Company).

31 Grimm et al., “Pattern Oriented Modeling.”

32 Clifford G. Gaddy and Barry W. Ickes, Russia’s Virtual Economy (Washington, DC: Brookings Institution Press,

2002).
10

33 Joshua M. Epstein, Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton: 

Princeton University Press, 2006).

34 Robert Axtell et al., “Population Growth and Collapse in a Multiagent Model of the Kayenta Anasazi in Long 

House Valley,” Proceedings of the National Academy of Sciences 99, no. 1 (2002): 7275–79, 

https://doi.org/10.1073/pnas.092080799.

35 Cioffi-Revilla, Introduction to Computational Social Science.

36 William Dunn, Public Policy Analysis: An Integrated Approach, 6th ed. (New York: Routledge Taylor and Francis, 

2018).

37 Robert Geyer and Paul Cairney, eds., Handbook on Complexity and Public Policy (Northhampton, Massachusetts: 

Edward Elgar Publishing Inc., 2015).