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3.7 Modern Warfare: An M&S Examination of the Dynamic Impact of 
Warlords and Insurgents on State Stability 

Modern Warfare: An M&S Examination of the Dynamic Impact of 
Warlords and Insurgents on State Stability 

Phillip N. Jones 
MYMIC, LLC. 
phillip.iones@mvmic.net 

Nick Drucker 
MYMIC, LLC. 
nick.drucker@mvmic.net 

Noah Schwartz, Ph.D. 

Christopher Newport University 
noah.schwartz@cnu.edu 


Abstract: 9/1 1 changed the world as we knew it. Part of this change was to redirect the military of the United States away from 
focusing primarily on conventional conflict to a primary focus on unconventional or irregular conflict. This change required a 
tremendous learning effort by the military and their supporting research and development community. This learning effort 
included relearning of old but largely forgotten lessons as well as acquiring newly discovered knowledge. During the process of 
our immediate 9/11 response, we identified that we were engaged in Iraq and Afghanistan in an insurgency. Subsequently, our 
focus converged upon the description of insurgencies and the requirements for counterinsurgency. This paper argues that 
emerging conditions now allow the re-evaluation of the type of conflict occurring today and into the foreseeable future: that we, 
including the modeling and simulation world, emerge from a singular focus on orthodox insurgencies and start to consider the 
consequences and opportunities of the complexity of current conflicts. As an example of complexity, this paper will use the 
relatively common phenomenon of the Warlord or Warlordism. The paper will provide a definition of this phenomenon and then 
describe the implications for modelers. The paper will conclude by demonstrating the impact of incorporating this one rather 
prosaic complexity into an insurgency model, using agent based modeling (ABM). 


1. INTRODUCTION 

9/1 1 changed the World as we knew it. 
Part of this change was to redirect the 
military of the United States away from an 
almost exclusive focus on conventional 
conflict to a primary focus on 
unconventional or irregular conflict. This 
change required a tremendous learning 
effort by the military. The supporting 
research and development community was 
part of this learning process. This effort 
included relearning old but largely forgotten 
lessons as well as acquiring newly 
discovered knowledge. 

The exigencies of our response to 9/1 1 , 
largely wrapped up in Operations Enduring 
and Iraqi Freedom, and our general 
unpreparedness for the unconventional 


conflicts emerging from these operations, 
required a strategically rushed response. 
Clausewitz observed that the first 
requirement of war is to identify the form of 
war you are fighting. During the process of 
our immediate 9/11 response, we identified 
that, in Afghanistan and eventually in Iraq, 
we were fighting an insurgency. 
Subsequently, our focus converged upon 
the description of insurgencies and the 
requirements for counterinsurgency, or 
COIN. Even though security intellectuals 
are debating the pertinence of terms such 
as guerilla war, hybrid war, fourth 
generation war, unrestricted war, new war, 
etc to describe our current conflicts, 
operators have moved forward in labeling 
the current conflicts as insurgencies and our 
response, necessarily, as 
counterinsurgency, or COIN. This 


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deduction was solidified by the publication 
of the US Army and Marine Corps doctrine 
manual on Counterinsurgency: FM 3-24/ 
MCWP 3-33.5 [1], 

FM 3-24 borrows from Joint Publication 
1-02 in defining insurgency as: “...an 
organized, protracted politico-military 
struggle designed to weaken the control and 
legitimacy of an established government, 
occupying power, or other political authority 
while increasing insurgent control” [2]. The 
same manuals provide a highly relative 
definition of COIN: “Those military, 
paramilitary, political, economic, 
psychological, and civic actions taken by a 
government to defeat insurgency” [2], There 
is a level of monolithism in these definitions 
centered on the strategic goals of an 
insurgency: the transfer of political power 
from one group to another. This 
monolithism is also found in the common 
works on insurgency, including older works 
such as Galula’s Counterinsurgency 
Warfare and Trinquier’s Modern Warfare , 
and newer works such as Smith’s The Utility 
of Force and Kilcullen’s Accidental Guerrilla. 

This paper argues that emerging 
conditions now allow the re-evaluation of 
the type of conflict occurring today and into 
the foreseeable future: that we, including 
the modeling and simulation world, emerge 
from a singular focus on orthodox 
insurgencies and start to consider the 
consequences and opportunities of the 
complexity of current conflicts. This first 
requires an appreciation of the complexity of 
the current conflict environment and its 
inherent and potential complexity and an 
appreciation of the implication of that 
complexity. 

As an example of this complexity, this 
paper will use the relatively common 
phenomenon of the Warlord or Warlordism. 
Warlords arise and thrive in the power 
vacuum of weak and failed states — just the 
type of conditions into which the future will 
take the US military [see 6], The paper will 
provide a definition of this phenomenon and 
then describe the implications for modelers. 
The paper will conclude by demonstrating 
the impact of incorporating this one rather 


routine complexity into an insurgency 
model, using agent based modeling (ABM). 


2. Describing the Warlord 

Warlords are a common historical 
phenomenon. The specific term was coined 
to label local, militaristic leaders that 
dominated China between the collapse of 
the Ming Empire and the rise of the KMG 
[3], However, all continents have seen 
Warlords. They existed in Europe through 
the Dark and later Ages following the 
collapse of the Roman Empire — Warlords 
and their retainers built many of the castles 
seen today in Europe. In North America, 
Comanche war party leaders represented 
Warlord traits [4], Warlords existed in 
historical Japan. Today, Warlords are found 
in Africa and Southwest Asia, specifically in 
Afghanistan. 

The word “Warlord” is a term applied 
by humans to label a particular social 
phenomenon. As with much from the social 
sciences, this is not a singularly discreet 
phenomenon with easily identifiable 
boundaries. Rather, it is location upon the 
vast spectrum of how humans organize 
themselves and, in the process, deal with 
other humans [3] [4]. Thus, the definition 
and description of a Warlord and of 
Warlordism will always contain flexibility 
relative to the experiences and attitudes of 
the word's user. In the case of the term 
Warlord, an example of this flexibility may 
be found in the aspect as to whether the 
Warlord is or is not financially motivated [4], 

This paper will use a broad definition 
of Warlord. A Warlord is an individual un- 
beholden to an external physical, 
intellectual, or emotional authority such as a 
state or cause; successful in leadership 
through charisma and other motivational 
qualities; possessing military organizational 
traits; and himself internally motivated by 
personal gain, be that gain physical (i.e. 
financial) or emotional (i.e. glory, reputation, 
etc). 

This definition includes those 
elements that make the Warlord both similar 


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and dissimilar from the insurgent leader. 

The Warlord’s personality and military 
organization allow him to collect and 
organize an effective force of retainers, 
personnel loyal first and, perhaps, only to 
him. This could be said of many leaders, 
including insurgent leaders. However, the 
insurgent leader — Mao, Begin, Castro, Pol 
Pot, Noriega, Zarqawi — are in word if not in 
action, subservient to some higher calling, 
such as communism, nationalism, or 
fundamentalism. Through their insurgency, 
they seek initial instability in order to 
weaken the state so they can subsequently 
replace it, whereupon the insurgent imposes 
its own, personally motivated stability. The 
Warlord is interested in only his own gain. 

He seeks instability to create the conditions 
in which he thrives. He has no interest in 
transitioning to some new, stable situation. 
Thus, in the initial phases of an insurgency, 
the Warlord will work with the insurgent if 
not disguise himself as one — the Warlord’s 
interest parallel the insurgent’s. Later in the 
insurgency, when the winning insurgent 
begins to impose his own stability upon the 
state, the Warlord will turn on the insurgent, 
continuing to destabilize the situation. The 
example of this is Charles Taylor who 
entered Sierra Leone allied with the anti- 
government revolutionaries of the 
Revolutionary United Front (RUF) to start an 
insurgency, and then killed off those 
revolutionaries to seize control of RUF for 
his personal profit [3], 

3. Methods and Model Construction 

In order to experiment with the 
concepts presented above we constructed 
an agent based model (ABM) of insurgent 
and warlord instability activity. We 
constructed the model for this experiment 
using NetLogo [7], a software which is 
ideally suited for this type of modeling and 
simulation work. Within NetLogo it is 
possible to create large groups of agents, 
and by assigning them a set of rules to 
follow observe the aggregate results of each 
one's individual actions. 


The model consisted of a population 
of 1 ,000 agents with varying levels of initial 
instability. The model also included a state 
security force trying to maintain stability and 
support for the current ruling group, an 
insurgent influence attempting to create 
instability and subsequently win populace 
support for their views, and a Warlord 
influence seeking to create and maintain 
instability. In order to test our hypothesis 
that Warlord influence will result in greater 
instability by complicating the re- 
stabilization process, we included a switch 
to allow the model to create a baseline by 
running without the Warlord influence and 
then run with the Warlord influence to 
measure the significance. 

The model consisted of five 
variables of interest: the insurgent influence 
level, the security force influence level, the 
warlord influence level, the initial instability 
within the model, and the threshold for 
agents to become unstable. The influence 
levels represented the amount an agent’s 
instability variable can change based on 
interaction with the security, insurgent, or 
warlord factions. The greater the influence 
the more the faction could change the 
agent’s stability level. The initial instability 
within the model allowed us to seed the 
simulation with some level of instability. 

This allowed us to experiment with relatively 
stable situations or those already in a state 
of relative instability. Finally, the instability 
threshold allowed us to experiment with how 
resilient an agent is to becoming unstable. 
The higher the threshold the more negative 
influence an agent needed to become 
unstable. 

During each run of the model the 
following procedures occurred: populace 
members received influence from the 
security force, the insurgent force acted to 
influence agents, if warlords are present 
they acted to influence agents, agents 
calculated their instability, the model 
calculated the overall instability. During the 
security force interaction, populace agents 
had a 15% chance of receiving influence 
from the security force. Because the force’s 
goal was to maintain stability, they operated 


210 


throughout the entire system. However, to 
ensure they were not omniscient and thus 
able to constantly positively influence all 
agents, the 15% level meant on each run 
roughly 15% of the entire populace, or 150 
agents, would receive positive influence. 

The insurgent would always reach a group 
of agents. However, which group they 
reached was random. This allowed us to 
mimic the way insurgent groups operate, 
not always attacking the same target day 
after day but selecting and attacking 
vulnerable targets. In the model, Warlords 
operated in a similar manner, always 
interacting with a random group of populace 
members. The main difference between the 
groups was that Warlords would only 
instigate instability but the insurgent would 
instigate instability and try to win support for 
their cause. During the instability check 
procedure, all agents calculated whether 
they had crossed the threshold to become 
unstable. The number of agents who were 
unstable was then fed to a master variable 
that determined when 60% of the populace 
had reached instability. Once this occurred, 
the model considered the populace unstable 
and noted the time. This also triggered a 
change in behavior in the model, with initial 
instability resulting in an attempt by 
insurgents to return order and support for 
their goals, while warlords simply continued 
to seek instability. Once this process began 
the model would note when a new stable 
rate occurred, if it ever occurred. The 
second instance of stability represents a 
shift by the populace to support of the 
insurgent. If 60% of the populace never 
reach instability, the model would never 
become unstable, and likewise if the model 
became unstable but never reached a 
second instance of stability this was noted. 

To support our contention that the 
community expand its perspective beyond 
the traditional insurgency conflict model, we 
wanted to illustrate the impact of adding 
complexity to that model, specifically adding 
the rather common phenomenon of 
Warlordism. We thus created an 
experiment. We intentionally kept the 
experiment simple. The design required 


creating a simple insurgency model within 
an ABM. We based this model on a generic 
insurgency scenario. The scenario 
postulated a small group disaffected against 
the government, i.e. the insurgents. That 
group desired to replace the government. 

To do so, they first had to remove the link 
between the existing government and the 
populace, i.e. create instability. Once 
instability reached a critical mass, the group 
then attached the population to themselves, 
i.e. create insurgent sponsored stability. 

The design included creating a 
baseline. This was a scenario without a 
Warlord presence. The next part of the 
design was to include a Warlord and then 
determine if there was an impact, measure 
the impact, determine what that impact was, 
and measure the significance of the impact. 
The Warlord sought instability. While the 
insurgent focused on causing instability, the 
Warlord served as his ally. However, once 
the model reached the tipping point and the 
insurgent started to focus on causing 
stability, the Warlord would work against the 
insurgent. The experiment anticipated that 
in the initial phase, the Warlord would serve 
as an accelerant. In the second phase, the 
Warlord would serve as a modulator. 

To keep the experiment simple, it did not 
include any outside COIN force, i.e. an 
intervening United States. It did, however, 
include a local security force. This force 
worked to maintain stability. The local 
security force acted upon the entire 
population but with reduced effectiveness 
due to being spread out. Additionally, the 
security force lost influence near the tipping 
point when the population became instable. 

3.1. Experimental Protocol 

We conducted 17,280 runs of the 
model under varying conditions. The main 
purpose of the experiment was to measure 
the impact of adding a Warlord to a conflict 
by determining if the impact of the inclusion 
on the speed that a population would both 
become initially unstable and subsequently 
re-stabilizing under the insurgent influence. 
While this was the main thrust of the 


211 


experiment, we also examined the impact 
upon results of the variables governing 
insurgent influence, Warlord influence, 
security influence, instability thresholds, and 
initial instability within the model. 


4. RESULTS 

The results of the experiment 
demonstrate the significance of adding 
complexity into an insurgent model. The 
inclusion of a Warlord presence in the 
model supported our hypothesis that 
including the Warlord presence would result 
in faster times to initial instability but a much 
longer run to reach a new level of stability. 
Tables 1 and 2 below outline the number of 
steps it took the model to reach instability 
and then re-stabilize (ticks), the number of 
turns it took the model to reach initial 
instability (un-stable-turn), and the number 
of times out of 8,640 trials that the model 
reached a level of initial instability. As the 
tables show, when the Warlord was not 
present, it took the model almost 300 
additional turns to complete. Completion 
meaning the model reached an initial 
unstable level and then returned to stability. 
In addition, it took over 40 more turns to 
reach the initial instability. These numbers 
represent a 36% increase in steps to 
complete and 26% increase in time taken to 
reach initial instability when there is no 
Warlord presence. The number of unstable 
trials also demonstrates that Warlord 
presence greatly increased the likelihood of 
instability as there was a 59% increase in 
trials where instability occurred when the 
warlord was present. 


Table 1 : No Warlord present 
Warlord Off 


ticks 


un-stable-turn 


803.75 


177.06 


Number of trials unstable 

1928 


Table 2: Warlord present in the model 
Warlord On 


ticks 


un-stable-turn 


512.89 


130.49 


Number of trials unstable 


4747 


In order to determine if the results 
we observed in these experiments were 
significant we conducted an Independent 
Samples T-Test on the data. The results of 
the test appear in Table 3 and demonstrate 
that there was a significant difference 
between the two sets of experiments. This 
confirms our observation that including a 
Warlord in the model will cause quicker time 
to initial instability (p-value < .01), but result 
in a longer amount of time until a new level 
of stability occurs (p-value < .01). 


212 


Table 3: Two Sample T-Test 


Group Statistics 


WarLord 

N 

Mean 

Std. Deviation 

Std. Error Mean 

.00 

8640 

803.7503 

423.77172 

4.55906 

Ticks 





1.00 

8640 

512.8897 

421 .80117 

4.53786 

.00 

8640 

39.5109 

109.62365 

1.17936 

UnstableTurn 





1.00 

8640 

71.6947 

131 .61067 

1.41591 


Independent Samples Test 



Levene's Test for Equality of Variances 

t-test for Equality of Means 

F 

Sig. 

t 

df 

Equal variances assumed 

11.919 

.001 

45.217 

17278 

Ticks 





Equal variances not assumed 



45.217 

17277.625 

Equal variances assumed 

235.898 

.000 

-17.465 

17278 

UnstableTurn 





^^^cjua^ainances not assumec * 



-17.465 

16731.166 


Independent Samples Test 



t-test for Equality of Means 

Sig. (2- tailed) 

Mean Difference 

Std. Error Difference 

Equal variances assumed 

.000 

290.86065 

6.43251 

Ticks 




Equal variances not assumed 

.000 

290.86065 

6.43251 

Equal variances assumed 

.000 

-32.18380 

1.84274 

UnstableTurn 




^^^cjual variances not assumed 

.000 

-32.18380 

1.84274 


Independent Samples Test 



t-test for Equality of Means 


95% Confidence Inte 

rval of the Difference 


Lower 

^^Uppei^^ 

Equal variances assumed 

278.25228 

303.46902 

Ticks 



Equal variances not assumed 

278.25228 

303.46902 

Equal variances assumed 

-35.79575 

-28.57184 

UnstableTurn 



^^Equa^anances not assumed 

-35.79576 

-28.57183 


model to reach instability. Tables 3 and 4 
In addition to the findings regarding below display the regression statistics, 

the presence of Warlord impact on 
instability, we examined how the other 
variables related to the time taken to reach 
instability. In order to determine what 
relationships existed and if they matched 
our hypothesis’, we employed regression 
analysis. Our hypothesis was that insurgent 
influence, stabile influence, initial instability, 
and the warlord influence would all have 
negative relationships with the time taken to 
reach instability. By this we mean that 
where these variables increase, time to 
instability would likely decrease. In addition, 
we included the instability threshold in the 
regression, hypothesizing that the higher 
the threshold the longer it would take the 


213 


Table 4: Regression without Warlord 


Regression 

Multiple R 

0.61 

R Square 

0.37 

Adjusted 


R2 

0.37 

Stand 


Error 

104.14 

Trials 

8640 



Coefficients 

Standard 

Error 

tStat 

P 

Intercept 

157.72 

5.59 

28.21 

0.00 

ins-inf 

-43.48 

0.67 

-64.97 

0.00 

stab-inf 

32.01 

0.71 

44.83 

0.00 

initial 

-0.63 

0.07 

-9.01 

0.00 

war-inf 

-7.03 

0.53 

-13.20 

0.00 

threshold 

1.59 

0.07 

23.15 

0.00 

Table 5: Regression with Warlord 


Regression 





Multiple R 

0.61 




R Square 

0.37 




Adjusted 





R2 

0.37 




Stand 





Error 

104.14 




Trials 

8640 






Standard 




Coefficients 

Error 

f Stef 

p 

Intercept 

157.72 

5.59 

28.21 

0.00 

ins-inf 

-43.48 

0.67 

-64.97 

0.00 

stab-inf 

32.01 

0.71 

44.83 

0.00 

initial 

-0.63 

0.07 

-9.01 

0.00 

war-inf 

-7.03 

0.53 

-13.20 

0.00 

threshold 

1.59 

0.07 

23.15 

0.00 


For almost all of our hypotheses, we 
were able to confirm our initial projections. 
However, we did discover one interesting 
note. When the Warlord presence did not 
exist, the relationships between insurgent 
influence and stable influence were 
opposite of what we expected, i.e. in these 
runs, an increase in insurgent influence 
would result in a longer time to instability 
and increased stable influence would result 
in quicker time to instability. This may be a 
result of the way we ran our experiment. 

Due to computational limitations, we choose 
only to use a strong security force and a 


weak security force. Because we did not 
sample across more variable values, this 
may be a demonstration of sensitivity within 
the model. In future runs, we would 
experiment across more variable values and 
examine whether the results remain the 
same. Despite this finding, the model 
appears to function properly and we were 
able to confirm our main hypothesis that the 
presence of a Warlord influence will result in 
quicker time to initial instability, but a 
prolonged time to re-stabilize. 

5. CONCLUSION 

The results of this experiment support 
proposal of the requirement to move beyond 
simple applications and incorporate 
complexity in our appreciation of 
contemporary conflict. Our experiment 
confirmed our initial hypothesis that the 
inclusion of a complicating factor, such as a 
Warlord, will significantly affect an 
insurgency model. This research does not 
approach addressing all of the variables that 
would inevitably be present in an actual 
insurgency. Thus, it is not a beginning to 
better understanding but, hopefully, a 
beginning to a beginning. 

6. REFERENCES 

[1 ]. FM 3-24, (2009), Field Manual 3-24, 
Tactics in Counterinsurgency, 
Headquarters, United States Army, 
Washington, DC. 

[2] , JP 1 -02, (201 0;, Join Publication 1-02, 

Department of Defense Dictionary of 
Military and Associated Terms, Joint 
Staff, Washington, DC 

[3] . Lezhnev, S., (2005), Crafting Peace: 

Strategies To Deal With Warlords In 
Collapsing States, New York, NY, 
Lexington Books. 

[4] , Giustozzi, A., (2009), Empires of Mud: 

wars and warlords in Afghanistan, New 
York, NY, Columbia University Press. 

[5] , Hamalainen, P., (2008), The Comanche 

Empire, New Haven CT, Yale University 
Press. 

[6] , United States Joint Forces Command 

Joint Futures Group, (2010), The Joint 


214 


Operating Environment (JOE), US Joint 
Forces Command, Suffolk, VA. 

Wilensky, U., (1999), 

NetLogo. h ttp ://ccl.northwes tern . ed u/netl 
ogo/, Center for Connected Learning 
and Computer-Based Modeling, 
Northwestern University, Evanston, IL.