State Security Dynamics and the Impact of Intervention to Build
Country Capacity
James R. Enos and John V. Farr
Center for Nation Reconstruction and Capacity Development
Department of Systems Engineering
United States Military Academy
West Point, NY 10996
Abstract
Recent history has shown that the armed forces will encounter significant challenges in
its future stability and reconstruction efforts that seek to establish a safe and secure environment
in the assisted country. In addition to establishing and maintain security, the military will ensure
government stability with democratic practices including fair elections rule of law, and human
rights; development of a robust economy; and assist the country in becoming a respected
member of the international community. The military has had some success at affecting the
social, governance, and economic fabric of a country. However, as recently demonstrated in
Afghanistan and Iraq this has come with a significant price tag in terms of human life and
investments. Few will deny that the US has struggled to invest our resources during stability and
reconstruction operation in an efficient and effective manner. The Department of Defense must
learn to better invest its resources before the outbreak of hostilities as well as throughout the
spectrum of conflict and post-conflict operations. More importantly, we must better understand
when to invest in building host nation capacity. This paper uses systems dynamics to understand
how and where these investments affect the long-term legitimacy and capacity of a nation.
Key Words: System dynamics, state security, government capacity, capacity development
Introduction
Since the 1970s, the greatest threats to the national security of the United States (U.S.)
have come from both emerging ambitious states and from nations unable or unwilling to meet
the basic needs and aspirations of their people. Subsequently, since the end of the Cold War the
U.S. and its allies have begun a new stability and reconstruction (S & R) operation every 18 to
24 months. The margin of victory in peace building will be measured in far different terms from
the wars of past. Time may be the ultimate arbiter of success: time to bring security to an
embattled populace; time to provide for the essential humanitarian needs of the people; time to
restore basic public order and a semblance of normalcy to life; and time to rebuild the institutions
of government and market economy that provide the foundations for enduring peace and
stability. Between forty and sixty nation states, home to nearly 2 billion people, are either
sliding backward or have already collapsed and the destructive power at the disposal of these
countries and their ability to cause regional and even global instability, more than any time in
history the global military and economic leaders need to invest in developing a sustainable global
peace. This can only be accomplished one country at a time with huge resource investments.
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According to the United Nations (2012):
+ 1.5 billion people live in conflict-affected and fragile states,
+ About 70% of fragile states have seen conflict since 1989,
+ Basic governance transformations may take 20-40 years, and
* 30% of official development assistance is spent in fragile and conflict-affected contexts
and these countries are furthest away from achieving the United Nation’s Millennium
Development Goals (MDGs) of legitimate politics, security, justice economic
foundations and revenues and service
In order to address these failed states and make progress towards the MDGs, now is the
time to address not so much the “why” but the “what and when” of investing scarce resources.
This paper demonstrates the utility in using system dynamics to model government capacity and
stability using a hypothetical country in Africa. Tradeoffs in allocating resources to security
intervention, partner nation training, infrastructure and capacity development, weapons and
technology sales, etc., will be analyzed to understand the effects on security/stability,
governance, economic development, and societal issues (e.g. human trafficking, drugs, etc.).
Background
The wars in Iraq and Afghanistan have demonstrated that we must strategically invest in
security, development, and governance in a much more deliberate and targeted manner. Simply
investing significant resources is not sustainable nor does it works well to address the complex
nature of state security. The Department of Defense, in concert with the whole goverment
team, must develop methods, processes, and tools that can look beyond simply investing
resources focused on short-term results to address medium- and long-term sustainability results.
These methods must address the “what and when” of harmonizing with the national and local
context in order to build country capacity.
A new philosophy is emerging in which global powers use their industrial and military
capacity to develop strategic partnerships designed for building relationships and host country
capacity in lieu of simply selling military equipment. By proactively building capacity of partner
countries, we can hope to create and reinforce relationships, supporting mutual goals and
interests, improve the quality of life in the partner nation, and perhaps prevent the emergence of
non-state actors who should seek to undermine lawful governance. Figure 1 depicts the
prototypical resource allocation for a major regional conflict for both a proactive and reactive
policy of intervention.
The proactive approach to building capacity involves allocating resources and
intervention well before the large-scale crisis or hostilities. Even without early intervention, the
military must be able to plan and resource S & R efforts throughout the conflict life cycle shown
in Figure 1. Each phase requires a different focus and priority of investment in security,
governance, and economic development projects. The complex, dynamic environment our
whole government team, partners, and NGOs faces today requires an understanding of the
conditions that exist in the partner nation in order to make smart investments in capacity
development. The social, governance, and economic implications of these investments within
the partner country and the resulting effects can often lead to unforeseen second order effects.
Quantitative means are needed for not only prioritizing investments but to understand the short
and long-term effects of those investments. Currently, very few quantitative techniques exists in
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how to allocate resources in support of building a country’s capacity to provide for its populace
along with a fundamental understanding of how capacity is affected by economic, security, and
govemance investment.
Theater security cooperation and
building partner capacity agreements
and other strategic investments are
key to preventing crisis or at least
mitigate the effects
Resources
i or | Capacity Development Capacity Development
iHostilities + Nation Reconstruction Nation Reconstruction
Non-intervention Stabilization and Reconstruction (S&R)
Figure 1: Timeline of a Major Regional C onflict and Resources
Literature Review
One aspect of the literature that is essential to the basis of this paper is the description of
S & R and country capacity. Country capacity is the ability of a country and government to
perform the functions of providing for the populace, solves problems, and achieves objectives in
a sustainable manner. Nelson defines S&R as the process to achieve a locally led and
sustainable peace in a dangerous environment (2006). The military role in this process is halting
residual violence and ensuring order and security, including those reconstruction efforts required
to repair enough damage to enable restoration of the most essential services. Additionally, the
Department of Defense (DoD) defines building partner capacity (BPC) as “targeted efforts to
improve the collective capabilities and performance of the DoD and its partners” (2006). A
major component of BPC is security force assistance (SFA). The DoD defines SFA as
department activities that contribute to unified action by the U.S. government to support the
development of the capacity and capability of foreign security forces and their supporting
institutions (2010). The DoD builds partner capacity by training and equipping the partner
nation’s military and improving their quality of life through infrastructure improvements,
education, and equipping the civilian workforce. Governments train their militaries to fight and
win their nation’s wars; however, in moder conflicts to include S&R, non-military capacity
building actions have become as important as any kinetic weapon system.
System dynamics (SD) provides an excellent tool for analyzing the complex
interdependencies and feedback evident in nation reconstruction and capacity development that
creates the dynamic behavior of the system. This should lead to more defensible and transparent
government policies and investments. SD explains the behavior of systems over time as a direct
result of the system structure and aims to adjust individuals’ mental models of the system to
implement policies to improve system performance. Forrester described the potential for SD as
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an approach that should help in the important high-level management problems (1961). He
noted that solutions to small problems will only yield small results and that people get mediocre
results by setting improvement goals too low. He suggests that the change must be at the
enterprise level to achieve major improvement and that the goal should be to determine policies
that lead to greater success (Forrester, 1961).
Dynamics are the behavior of a system over time, which are generally complex and non-
linear in nature (Forrester, 1961). This complexity comes from feedback within the system, time
delays between decisions and effects, and the learning process of the system (Sterman, 2000).
Causal loops diagrams (CLD) are a key element of the system dynamics approach which are
signed diagrams that represent the reinforcing or balancing feedback within a system. Causal
loops are different from discrete, event-oriented perspective of individual causes and effects in
that they acknowledge that in a closed system any cause is an effect and any effect is a cause
(Richardson, 1991). One is able to describe the behavior of the system by talking through the
loop to tell the story of the interactions within the system (Meadows, Jorgen and Meadows,
2004).
As previously discussed, the purpose of this research is to demonstrate the utility of SD
as an analytical tool for this class of complex systems problems. Scholars have extensively used
SD to address military problems and components of government capacity. Robbins developed
an SD model focused mainly on combat operations (2005). Additionally, several authors have
used SD to examine at certain components of a government. For example, Sterman presents a
SD model of gross domestic product (GDP) (2000). Another paper deals with the role of water
in the Manas Basis in Africa (Shanshan, Lanhai, and Honggang, 2009). Crane used mainly a
Likert scale ratings for Democratic Republic of the Congo along with some open source data to
develop a conceptual SD model (2009). He used a set of what he termed Nation Building
Elements that consisted of security, humanitarian relief, economic stabilization, and governance.
System Dynamics Model
The military typically focuses on those activities that are consistent with its mission —
mainly security cooperation. However, the problems today’s military faces are much more
complex in nature and require a broader way of thinking about the problem. Kilcullen describes
an insurgency as a complex system that needs energy, in the form of acts of violence and
grievances against the government, to sustain itself through several feedback structures (2004).
The SD model presented represents those activities needed for a country to meet the needs of its
populace and the feedback observed in this complex system. The model accounts for and
simulates the energy required to ignite and fuel an insurgency as mentioned by Kilcullen. In
some situations, such as the presence of significant counterinsurgency operations, the military
would be heavily involved in restoring stability to a country. However, in general, the whole
government team and members of the international community are essential for accomplishing
non-security related activities that aim to build a country’s capacity before it falls into an
insurgency. Although the model does not specify which agency is responsible for building
capacity in the various areas, it demonstrates the benefits associated with improving a country’s
capacity in the security and non-security related areas.
The overall objective of BPC efforts is to increase the capacity of a country to a point in
which it can sustain itself. This prevents the security situation and government legitimacy from
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degrading to a point where the country spirals downward into a state of chaos. Figure 2 presents
aCLD of government capacity that includes the country’s education level, medical capacity,
security situation, infrastructure, and economic capabilities. The CLD represents the feedback
between these individual elements and the population of the nation, which creates the dynamic
behavior observed. Additionally, each of these individual system views of a government’s
capacity contains their own intemal feedback structures that generate specific behaviors. By
combining the behavior at the individual system level, the SD model is able to capture the
behavior of the complex system over time.
Figure 2: Macro Causal Loop Diagram of Government C apacity
The model includes structure for each of these sub-systems; however, this paper will
present only an examination of one of the system level models demonstrates the structure seen in
several of the systems. Figure 3 presents the system view of the medical capacity portion of the
SD model. This view shows how the population system interacts with the medical capacity as
the total population of the country has a positive relationship to the desired medical capacity.
Impact of Decreased — z
a St Medical Capacity on
Impact of increased
Medical Capacity on Te romsel Capaciyy Effect of Decreased
Capactiy megeatcapecly in
Time for Medical
‘Average time to Capacity to Decay
cone a medical
ani
Pea
Medial ‘Capacity
+ a
{
Medical Medical Capacity
Expandatures ActualMedical “per Project _ Medical
+ Projects \ Capacity Gap
Fe eee + P
| Desired Medical Desired Medical
Projects + Capacity +
\ Maximum number of
Medical Projects based "
Desired Hospital
fi Bed Density
|
Average costofa
medical capacity
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Figure 3: Medical Capacity Sub-System Structure
Page |5
The model utilizes an average medical bed density of ten developed countries as the
“Desired Hospital Bed Density” for the goal of the system to achieve (Central Intelligence
Agency, 2012). Based on this desired medical capacity, the model calculates a gap and
determines the number of projects to complete to fill this gap. However, the reconstruction
budget allocated for medical capacity constrains the total number of medical projects, thus
limiting the increase in medical capacity. Additionally, the model accounts for the delay in
construction time with a simple first order material delay before the government realizes an
increase in capacity from increased medical capacity. Through the use of auxiliary variables,
any increase or decrease in the medical capacity has a similar effect on the overall capacity of the
government.
This paper does not present the education or infrastructure model; however, the model
utilizes a similar structure for these systems with a desired capacity for each based on an average
value from several developed countries. For the education sub-model, the model measures the
impact a country’s literacy rate has on government capacity. For the infrastructure contribution
to capacity, if further decomposed this factor into electrical grid, improved water, and improved
sanitation.
Figure 4 presents the economic system structure that includes the nation’s expenditures
on infrastructure, medical, security, and education. Sterman provides a basic model of gross
domestic product (GDP), the proxy measurement for the nation’s economy, which demonstrates
how a nation’s GDP will adjust to the aggregate demand for its goods and services (2000). For
this application, the model expands on his base model by endoginizing, or making internal, the
variable of government expenditures by linking other system level model into the economy
model. These government expenditures come from money spent on increasing the medical,
security, education, and infrastructure capacity of the system. As a government spends resources
on increasing the capacity of one of these sub-systems, it creates jobs, purchases goods, and
increases production, thereby increasing the GDP and improving their overall stability.
Gross Domestic
Product
5,-Change in GDP
+a
—
Production
‘Adjustment Time
Aggregate
4, Demand
Aa
7
+Government
Expenditures /
Marginal
Pe an NO ti,
lavestment Consume
Expected Income
Expectation
Formation Time
‘Consumption ¢—
.
Figure 4: Economic Sub-System Structure (Sterman, 2000)
A major component of government capacity is the security component and the ability for
security forces to maintain the peace within a country. Choucri, et al built a model of state
Page |6
security that included the movement from the pro-government population to dissidents to
insurgents that had the potential to be removed by security force (2006). Their model provided
the basis for the structure of the security view of this system of system model of a nation’s
capacity.
Figure 5 presents the stock and flow diagram of the security system that shows how the
military aged population can become dissident based upon opposition recruitment. Additionally,
some fraction of the dissident population will become insurgents. The model accounts for both
the host nation security forces and external security forces. As the number of insurgents
increases in the model, the acts of violence subsequently increase to a point that it triggers
intervention from an external nation to help with security and reconstruction. Additionally, the
model incorporates the decision to deploy additional forces as the acts of violence continue to
rise. This decision rule is consistent with deployments to Iraq and Afghanistan, where an initial
number of soldiers deployed and then the U.S. increased the number of soldiers deployed as the
violence in these nations increased. The model also incorporates the blowback from insurgent
removal activities as it increases the effect of the opposition’s recruitment message and builds
increased support for the opposition. This portion of the model links to government capacity and
the budget for security forces and uses portions of the population model as well.
Table for Eftectot
ie a Security Forces
“sisi /
ai \ .
— Average Time as vo Effect of Security Forces
a Dissident Security Force ‘onTime to Remove Normal Time to
\) a er eos
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Figure 5: Security Sub-System Structure (adapted from Chourci et al, 2006)
Figure 6 presents the final system view for the model and includes the population of the
country divided into three stocks of individuals: children, adult population (15 years), and elderly
(65 years). The structure is a simple aging chain between the different stocks with additional
outflows for migration and deaths. The Central Intelligence A gency (CIA) World Factbook
Page |7
provides data for each of these populations, fractional birth rates, infant motility rates, life
expectancy, death rates, and migration rates, so the model can match any country’s initial
conditions (2011). Again, to endogenizes these variables, the country’s medical capacity will
have a direct impact on several of these variables and can be quantified in the mathematical
relations within the model.
Fractonalinfant
__NetDeatyRate___
a Sy
\ :
\ , be (
enanyttay cog ean cinbeot Ss] tery Death u
2 ethan te oe tow :
= f+ chistine ' AasTine -ite gectancy
~ Children ‘Adult Population Elderly
agian | ey ee ae YT ianetbem
we —* [ mee ( —%
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ate Ssh pene SBP migatontow
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eal
— Net Migration
ae
Figure 6: Population Sub-System Structure
Potential Policy Recommendations
The power of SD models comes in their ability to model system’s behavior over time
and the ability to compare different policy recommendations. This model utilizes data from the
Central Intelligence A gency W orld Factbook for a typical sub Sahara country as a base case to
determine if the model will generate observed historic behavior as government capacity
decreases and sends the country into a state of chaos. As a country’s security level decreases to a
certain point, the model triggers intervention on the part of a security force nation in the form of
soldiers and money, which is the traditional method of intervention. The model will output the
amount of capital invested by the country over time and determines a total amount of capital
invested by the security force nation. Additionally, the model can also simulate the behavior
over time for a nation’s capacity and security if a developed nation adopts a preemptive
intervention strategy in the form of building partner capacity. A comparison of these two
policies shows the advantages, both economic and social, of adopting a preemptive partner
building policy.
Figure 7 presents the structure of the external security forces sub-system of the model. In
this sub-system, acts of violence and the insurgency drive the need for external security forces.
Initially, events trigger the deployment of an external security force to the host nation. As the
security situation deteriorates, the model triggers the deployment of additional soldiers to address
the security concerns in the host nation. As the security situation improves, forces re-deploy
based on an average deployment length without replacements. This structure enables the
simulation of both the reactive policy, described above, and the proactive policy. In the
proactive policy, a small security and capacity building force deploy to the host nation to assist
Page | 8
with security and capacity development. In the model, a switch for a partnership force activates
this policy to evaluate the behavior over time of the proactive policy.
Force
B
Initial Partnership af
Force Size —
x
Parmership
Time to Deploy
Forces
Force Gap inant
“aye i) a ee
We —
Deploymentof Fores Redeployment
Forces, y of Forces
r )
1 Eternal Security Forces
External Security ‘cceptable Acts of
orce Gap jolence
\ Act o
—— — emcee 2%"
vital Security secur Force size Violence on Bernal 7
ee Se Stuy Fores
‘Table for Effectof Acts of
~~. "Violence on Exteral
Secunty Forces
Figure 7: Structure of Security Force Sub-System
The major output of the model is the variable of government capacity, which is a
weighted measure of the country’s ability to provide for its people. It is a combination of the
medical, education, infrastructure, security, and economic capacities of a country. Figure 8
presents the output of the model for the variable government capacity in both the reactive policy
and the proactive policy. As shown, the proactive policy initially underperforms the reactive
policy; however, eventually it overtakes the reactive policy, as the nation is able to sustain itself.
Additionally, the external security forces are able to accomplish this with significantly fewer
resources, both troops and financially.
Government Capacity
0 120 240 360 480 600 720 840 960 1080 1200
Time (Month)
ovement Capacity : Reactive Policy
Goverment Capacity : Proactive Policy
Figure 8: Government Capacity
From the security view of the model, the number of insurgents and the number of
extemal security forces provide valuable insights into the overall capacity of the government.
The number of insurgents indicates the nation’s ability to conduct counterinsurgency operations
and maintain a stable environment to grow their country. Figure 9 presents the output of the
Page |9
model and includes both the proactive policy of becoming partners with the host nation and a
reactive policy of deploying security forces after a crisis arises in the country. As shown, the
level of insurgents in a proactive policy is drastically lower than the reactive policy. This is due
to the improved training of host nation security forces when partner forces deploy to a country to
assist in building capacity. Figure 10 shows the level of security forces that a partner country
deploys in both policies. In a reactive policy, the security forces deploy as the result of a crisis in
the country and deploy additional forces to combat the high level of insurgents in the nation.
This is similar to the “surge” observed in Operation Iraqi Freedom. However, in the proactive
policy, a constant force of 5,000 soldiers from the partner nation is able to training host nation
security forces with a much smaller number of soldiers.
Insurgents Extemal Security Forces
Ireugents
Seoutity Forces,
=G
ae
0 480 600 720 840 960 1080 1200 720 240 360 480 600 720 640 960 1080 1200
(Month) ‘Time (Morth)
Insurgents : Fores : Proactive Policy
Insurgents > Forces: Reactive Policy -
Figure 9: Model Output — Insurgents Figure 10: Model Output - External Security Forces
The output of the budget portion of the model presents the monthly investment by
security forces in the host nation and determines the total of all investments. Figure 11 presents
the monthly investment by security forces in the host nation. Although the output of the model
does not exactly match the theoretical resources over time presented earlier, the behavior is
generally the same.
Investment by Security Forces Total Investment by Security Forces
300 M 1508 re a
ah
25M 11258
150M 75B
4
a
a
75M i 37.5B eo ee
o o
0” 120 240 360 480 600 720 640 960 1080 1200 120 240 360 480 600 720 €10 960 1080 1200
‘Time (Month) ‘Time (Month)
Figure 11: Model Output - Investment by Security Figure 12: Model Output - Total Investment by
Forces Security Forces
In a reactive policy, the partner nation rapidly increases investment in the country in
reaction to a crisis and over time is able to slowly decrease its investment as the host nation’s
capacity is increased. For the proactive policy, a constant investment over the simulation
demonstrates the requirements for a partnership to maintain the host nation’s capacity. Figure 12
Page | 10
presents the total investment by security forces in the host nation. As shown, the proactive
policy demonstrates significant savings over the reactive policy.
Although the model functions without errors and provides an output, it is not calibrated to
historical data for use as a predictive model for future policies. The output appears to align with
theoretical projections of investment of resources and security force levels; however, more work
can be done to refine the model. Additionally, the model contains several assumptions that
would need to be validated in order to provide a more accurate prediction of the behavior over
time.
Conclusion
The model presented in this paper is a general representation of the behavior of S & R
activities for a generic nation state. As designed, the model can adapt to simulate conditions for
a specific scenario/country and then be base lined against historical data. System dynamics
provides a mythology to understanding resource allocation, secondary effects, priorities for
developing government capacity and maintaining security for emerging countries. However, a
more detailed model is the only way to validate the interdependencies and complexities of state
security. Given that many of these S & R activities involve billions of dollars, a quantitative tool
is needed to better understand how strategic investments can best be utilized. This research
shows promise in that SD modeling could provide a tool to prioritize and eventually optimize
strategic investments alternatives that maximize the ability of a country to care and provide for
its people.
The structure of the model facilitates extension of the model to any developing country
based on some minor calibration to the model. The model uses inputs mainly from the Central
Intelligence A gency, which are readily available for any country in the world (CIA, 2012). With
these inputs alone, the model could replicate observed patterns of behavior in any country.
Additionally, the use of additional data for other countries may provide an opportunity to better
calibrate the model. Ideally, the model this paper presents could simulate the behavior of a
government’s capacity with minor modification to the initial variables. However, it is unlikely
that the exact conditions exist in multiple counties of interest, so the model may not apply to all
countries. The true replication opportunity exists in the ability to apply the techniques and
methodology this paper presents. Although a model of the country may slightly differ from that
of the model presented, the idea of combining several measurable metrics; security, medical
capacity, economy, and infrastructure, into a measure of a government’s capacity is widely
applicable. However, several basic research questions warrant further discussion to include:
1) How would the models differ be for a developed versus non-developed country?
2) Are the elements of country capacity presented the main drivers of behavior?
3) What is the right level of resolution for a nation state model?
Research into the challenges of verification and validation, resolution, data, accurately
capturing independences and synergies, second and third order relationships is starting to
emerge. Unfortunately, complexity is evolving as fast, or faster than our ability to study and
model state security with several states, including several in the Middle East or Northern A frica,
falling into conflict during this research. Correct tools and methodologies, applied to these
Page | 11
problems, will provide the only meaningful analysis into the wicked problems of society and
state security. As researchers attack these types of problems, gaps in modeling ability and
analyze will emerge, thus paving the way for relevant research.
References
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About the Authors
Major James Enos is currently an assistant professor in the Department of Systems Engineering
at the United States Military Academy, West Point, NY. Throughout his military service, he has
held numerous leadership positions as an infantry officer, including Rifle Company Commander,
Ranger Instructor, and Platoon Leader. He graduated from the US Military Academy at West
Point in 2000 with a Bachelor of Science degree in Engineering Management. He earned his
Master's of Science in Engineering and Management in 2009 from the Systems Design and
Management program at MIT. He teaches classes in modeling and simulation, systems
engineering, and system dynamics.
John V. Farr is a Professor of Engineering Management and Director of the Center for Nation
Reconstruction and Capacity Development at the United States Military Academy at West Point.
Prior to returning to West Point in 2010 he was a Professor of Systems Engineering and
Engineering Management in the School of Systems and Enterprises at Stevens Institute of
Technology (SSE). He was the founding Director of the Department of Systems Engineering
and Engineering Management at Stevens, which he led from 2000 to 2007. He served as
Associate Dean for Academics from 2007 to 2010 in SSE. He taught at West Point from 1992 to
2000. He is a past president and Fellow of the American Society for Engineering Management, a
Fellow of the American Society of Civil Engineers, former member of the Army Science Board,
Commissioner for the Engineering Accreditation Commission of the Accreditation Board of
Engineering and Technology, and a member of the Air Force Studies Board of the National
Academies. He is a former editor of the Journal of Management in Engineering and the founder
of the Engineering Management Practice Periodical. He has authored over 180 technical
publications including three textbooks. He is a registered Civil Engineer in New Y ork and
Mississippi.
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