Ground Warfare and Troop Morale:
A System Dynamics Approach
Jordan Bletscher
ESE 499
Fall 2008
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Abstract
Understanding the interplay between ground warfare and troop morale is a difficult task.
War is a complicated affair, with numerous interconnected variables combining to create drastic
effects. The sheer volume of events and factors unique to warfare make it a hard topic to fully
analyze and understand, especially when the analysis seeks to connect warfare with a somewhat
subjective topic like troop morale.
System dynamics modeling is a tool that allows analysts to explore both the quantitative
and qualitative aspects of a problem. Where analytical solutions are reliable and valuable it
provides an elegant way of solving complex systems of differential equations. Additionally, for
problems that are less easily defined in quantitative terms it provides an effective venue through
which to generate discussion on what specific variables are important to a given problem and the
specific interactions between them. My project seeks to apply system dynamics principles, both
quantitative and qualitative, to the complex topic of ground warfare effectiveness and troop
morale. The model I produced is useful for generating basic quantitative analyses of ground
combat scenarios, helping understand the myriad of interconnected factors important to troop
morale, and able to be easily extended to deal with other combat-related questions.
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Introduction
Troop morale is a topic often discussed but seldom diligently analyzed. This may be
because the subject of troop morale encompasses a number of “softer” factors such as emotions,
psyche, and irrational behavior. The effects of troop morale at the extreme ends of the spectrum
are easy observed (mass surrender or desertion versus loyalty to the death), but the reasons
behind the effects are often hard to quantify.
System dynamics provides analysts with a unique tool with which to simultaneously
deliver quantitative results and generate discussion about the interplay between interconnected
factors. Since all models are wrong while some are useful, often the most benefit gained from
modeling comes not simply from the quantitative results but from the discussions between
stakeholders that those results spawn. The graphical nature of system dynamics modeling lends
itself perfectly to generating discussion since the simple and straightforward model structure of
interconnected variables and causality arrows can be understood by those foreign to the model in
a short amount of time. This is one of the major reasons why I chose to use system dynamics in
creating a model to help understand the effects and causes of varying troop morale levels.
That’s not to say, however, that the quantitative aspects of a model are unimportant. They
certainly are. The basic structure of my model is derived from the simple combat model
discussed in a paper by Michael Artelli and Richard Deckro titled “Modeling the Lanchester
Laws with System Dynamics”1. The Lanchester Laws provide a straightforward way to calculate
the relative attrition rates of two opposing forces. The time-step of my model is half an hour, and
as such is set up to model not whole campaigns but rather individual battles that can be given a
wide variety of initial conditions. My version of the simple combat model implements force
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attrition, time-delayed troop reinforcement, and the effect of varying fatigue levels on attrition
rates. The interactions between these three factors comprise the bulk of my core model.
In addition to building the core model I built several examples of ways in which my
model could be extended to deal with other related topics. A study by RAND titled
“Psychological Effects of U.S. Air Operations in Four Wars 1941-1991”2 identified some key
factors proven to be important in driving troop morale conditions. Three of these factors are
included in my model as examples of how, with additional research and extensive quantitative
reasoning, an analyst could ramp up the fidelity of the troop morale portion of the model
independent of changing the core force effectiveness calculations.
A similar purpose was achieved in implementing a basic representation of area fire
effects. The Lanchester Laws yield separate calculations for determining attrition rates based on
whether weapons fire is directly targeted at the enemy or allowed to affect a larger area. My
model implements a method for factoring varying levels of area fire into the attrition rates, as
well as some examples of feedback loops associated with choosing to use area fire versus direct
fire. The basic effects that area fire munitions have on collateral damage, public support, and
changing rules of engagement are included at a low fidelity.
The goal in this was to demonstrate how the system dynamics architecture allows for the
modeling of associated effects in a way that is straightforward and useful for basic
representations of large complex systems. Users of the model can visually understand which
aspects of the model are highly detailed and which are highly abstracted and aggregated. The
analyst can then take any variable block of interest and increase its fidelity by splitting it up into
its subcomponents.
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In this way my basic ground combat model can be used as the jumping off point for an
analyst wishing to drill down into one or more of the variables highly abstracted in my
implementation. He or she can break up the variables of interest into sub-variables to increase the
fidelity of that portion while leaving the rest of the model intact. This results in a much more
extendable and reusable model than traditional models written in basic programming languages,
many of which can only be reliably altered by the original authors.
Simple Combat Model Implementation
The core of my system dynamics model is the basic combat component modeling the
interplay between force sizes, attrition rates, and reinforcement policies. The time step is set at
half an hour because the model is meant to be used to examine individual battles or localized
confrontations as opposed to full campaigns. An analyst wishing to use my model for other
purposes would be able to change the time step to simulate longer-scale engagements, but doing
so would require alterations to some of the relationship equations.
The two main stocks in my model are “Blue Forces” and “Red Forces”. These stocks
represent the size of each force, and are measured in units of “soldiers.” They are depleted by
their respective “Attrition” flows and refilled by their respective “Add Reserves” flows. The
equation for the Blue Forces stock is: ∫(Add Blue Reserves - Blue Attrition) , and the equation
for the Red Forces stock is: ∫(Add Blue Reserves - Blue Attrition).
The two main flows that affect force size are “Blue Attrition” and “Red Attrition.” These
flows represent the casualties experienced by each force during the conflict. The equations for
these flows implement the Lanchester Square law, which states that the attrition rates for each
side are directly proportional to the size of the opposing force3. The proportionality constant used
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(without the inclusion of troop morale impacts) accounts for the overall weapon effectiveness,
tactical expertise, and territorial advantage or disadvantage for each side. When troop morale is
not included in the model the equation for Blue Attrition is: Red Forces * Constant Red Attrition
Coefficient, and the equation for Red Attrition is: Blue Forces * Constant Blue Attrition
Coefficient. The impact that including troop morale in the model has on this attrition coefficient
will be discussed later.
Figure 1. The Simple Combat Model without Troop Morale Effects
Three of the main variables driven by the red and blue force size stocks are Red/Blue
Force Strength and Combat Ratio. The Red and Blue Force Strength variables are the ratio of the
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current force size to the initial force size. The Combat Ratio variable is the ratio of red force size
to blue force size. These variables are important in determining the winner of the battle. The
model user can define winning conditions for each side based upon the Combat Ratio value or
the Force Strength of either side. In my model I set the winning conditions such that Blue wins if
Combat Ratio < 1.5 or Red Force Strength < 0.7, and Red wins if Combat Ratio > 10 or Blue
Force Strength < 0.7.
In order for reserves to be sent to the battle they have to be requested. My model
simulated troop reinforcement requests with the “Requesting Reserves” variables for each side.
If this variable is equal to 1, it means that reserves have been requested and are being transported
to the battle area. If it is equal to zero, it means that no reinforcement request is currently being
processed. There are additional variables in place to allow the model user to specify the
conditions under which reserves are requested. The user can specify combat ratio and force
strength thresholds for each side that, when exceeded, trigger the request of reserves.
Once reserves are requested there is a time delay before the reserves actually reach the
battle and become a part of the force. When the “Requesting Reserves” variable is set to one, the
“Reserves Waiting Time” stock begins to increase at a constant rate of 0.5 per time step. This
ensures that the stock increases by one every hour. The “Reserves Waiting Time” stock does not
increase when “Requesting Reserves” equals zero. When this stock reaches a multiple of the
time delay value specified in the “Time Delay for Reserves” variable, the “Add Reserves” flow
is triggered and reinforcements are added to the total force stock.
In my model, troops are added to their force in discrete blocks of size specified by the
variable “Reserve Block Size.” Once the appropriate amount of time has elapsed after a troop
request, a whole block of reinforcements is added to the appropriate “Forces” stock at once. With
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this modeling approach, everything associated with preparing, transporting, arming, and
organizing the reinforcements is taken into account when determining the “Time Delay for
Reserves” value.
The “Add Reserves” flow is essentially modeled as an impulse function with a height that
is twice the value of the reinforcement block size. This ensures that all troops in a reinforcement
block are introduced into the “Forces” stock in one time step. Additionally, the “Add Reserves”
flow draws from the stock “Reserves Available.” If this stock is empty, no reinforcements will
be delivered to the battle even if they are requested. The user is able to specify both the
appropriate reserve block size and total number of reserve blocks available with the “Reserve
Block Size” and “Total Reserve Blocks” variable.
The delivery of reserves to the “Forces” stock via the “Add Reserves” flow balances the
“Attrition” flow and drives the combat model’s dynamic behavior. By changing the exogenous
variables (Blue/Red attrition coefficients, initial force sizes, winning conditions, reinforcement
request triggers, reserve delivery delay, reserve block size, and total number of reserve blocks
available) the analyst using the model is able to see the effects of increased or decreased values.
Blue and Red Fatigue
The second major part of my model is the implementation of fatigue effects on combat.
To start, the analyst defines a constant rate of fatigue. The fatigue rate should be specified in
units of percent fatigued per day. For example, if the analyst determines that the troops on the
Blue side would be at 96% of their initial fresh fighting effectiveness after one day, he or she
would set the fatigue rate at 4% per day. This value is inserted in the “Fatigue Rate (% per day)”
variable as the first value in the variable’s equation. Additional calculations are then done within
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the variable to get it into the right units for affecting the “Being Fatigued” flow. The equation for
“Fatigue Rate (% per day)” given a constant fatigue rate (methods for specifying variable fatigue
rates will be discussed later) is: User defined % fatigue per day * 0.01/24. This equation converts
the analyst’s input into units of fatigue/hour which are compatible with the “Being Fatigued”
flow.
The “Being Fatigued” flow adds fatigue to the “Fatigue Percentage” stock at a constant
rate equal to the rate specified in “Fatigue Rate (% per day)”. It implements the effect of soldiers
continually being fatigued throughout the course of the battle. While in reality the individual
fatigue rates of soldiers will vary based on their activity, overall the average fatigue rate of the
force will increase over time and this is the effect that this flow simulates.
When fatigue effects are included in the model, the attrition coefficients are no longer
constant. The “Fatigue Percentage” stock is linearly related to the “Attrition Coefficient”
variable. If a force is ten percent fatigued, then its attrition coefficient (and subsequently overall
fighting effectiveness) will be ten percent of its initial value. When fatigue is taken into account,
the equation for the attrition coefficient becomes: Initial Attrition Coefficient * (1 - Fatigue
Percentage). This adds an additional dynamic layer to the model and allows for more detailed
analysis than could be accomplished using a constant attrition coefficient.
When reserves are inserted into a force stock, the corresponding fatigue stock is
immediately adjusted. This simulates the effect that new reinforcement troops have on the
overall troop morale. When reinforcements arrive it is assumed that they start with zero percent
fatigue. As such, the new overall force morale level will be the weighted average of the existing
fatigue level before the reinforcements arrived and the fresh morale brought by the reinforcement
troops4.
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Figure 2. The Simple Combat Model with Troop Morale Effects
This is done through the “Being Refreshed” flow. The rate of the flow is equal to the
value in the “Refresh Rate” variable, converted to the correct units. The “Refresh Rate” variable
controls both the rate of the “Being Refreshed” flow and when the flow is turned on or off. Since
“Being Refreshed” is a negative flow relative to the “Fatigue Percentage” stock, the “Refresh
Rate” variable must specify the rate at which fatigue is drained due to the arrival of fresh reserve
troops. The equation used to calculate the “Refresh Rate” variable is:
Fatigue Outflow = (Old Fatigue * (1 - (Force level before reinforcements / (New force level with
reinforcements))
And thus, the overall equation for the new fatigue level when reinforcement troops arrive is:
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New Fatigue = Old Fatigue - (Old Fatigue * (1 - (Force level before reinforcements / (New force
level with reinforcements))
For example, say there are 600 soldiers in the force at t=10 with a force fatigue level of
30%. If 300 reserve troops arrive at t=10.5 then the effective force fatigue level at t=10.5 would
be 20% ( = 30% - 30% * (1 – 600/900)). An example graph of Blue Fatigue Level is included
below. Each reinforcement block inserted (indicated by the drops in the graph) is of the same
size. The graph clearly shows the effect that reserve troops have on fatigue level at varying
points in the battle. Since each block of reserve troops enters the battle with 0% fatigue, they
have more of an effect on the overall fatigue when the fatigue percentage they are helping relieve
is higher.
Blue Fatigue Percentage
0.2
0.15
0.1
0.05
0
0
200
400
600
800
1000 1200
Time (Hour)
Blue Fatigue Percentage : Baseline
1400
1600
1800
2000
Fatigue
Figure 3. Example Graph showing Blue Fatigue During the Battle
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The “Blue Being Refreshed” flow is triggered when the “Add Reserves” flow ceases to
equal zero. When “Add Reserves” is not zero, the “Refresh Rate” variable switches from zero to
the value specified in the equation above, and thus triggers the “Being Refreshed” flow to
increase.
Other Factors Affecting Fatigue Rate
The straightforward representation of fatigue in my model can be enhanced by adding
additional factors into the calculation of the fatigue rate. In the discussion above I assumed that
the overall force fatigue rate remains constant throughout the course of the battle. In reality, this
fatigue rate can vary based upon a number of factors. An analyst wishing to study the effects that
other outside events have upon the fatigue rate and overall battle scenario can easily create
separate variables and feedback loops to tie into the “Fatigue Rate” variable.
Figure 4. Combat Model with Troop Morale and External Influencing Factors
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To demonstrate this feature, my model includes two basic examples of outside factors
that affect the overall fatigue rate. These factors were taken from the RAND study titled
“Psychological Effects of U.S. Air Operations in Four Wars 1941-1991”5. The study lists several
factors proven to be of either major or minor importance in affecting the morale level of an
opposing force.
One of the factors included in my model is the representation of PSYOP effects. PSYOP
stands for psychological operations, and are defined as “planned operations to convey selected
information and indicators to audiences to influence their emotions, motives, objective
reasoning, and ultimately the behavior of organizations, groups, and individuals.”6 The RAND
study notes that PSYOP effectiveness plays a minor but important role in degrading opposing
force troop morale. While the effectiveness of PSYOP operations do not often lead directly to
desertion or surrender of opposing forces, they do make the opposing force more prone to the
psychological effects caused by other more major factors. In essence it enhances the
psychological effect that other major war factors have on opposing force troop morale.
PSYOP effectiveness is included in my model as a user defined value. The user can enter
a value between 0 and 100 in the “PSYOP Effectiveness” variable. A value of 0 means that any
attempted PSYOP were completely ineffective and had no effect on the opposing force’s fatigue
rate, while a value of 100 means the operations were completely effective and resulted in a 0.5%
increase in the opposing force’s fatigue rate. Values between 0 and 100 signify varying degrees
of effectiveness and result in a fractional increase of the opposing force’s fatigue rate. The
equation used to include PSYOP effects in the fatigue rate calculation is:
Overall fatigue rate = Base fatigue rate + (0.5 * (Opposing force PSYOP effectiveness / 100))
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An analyst wishing to use my model to study other implementations of PSYOP effects
could easily change this equation, or create additional feedback loops to enhance the calculation
of PSYOP effectiveness. The visual and easily understandable nature of system dynamics
modeling makes this possible.
One factor cited by the RAND study as a major contributor to increased fatigue is
sustained air operations. My model includes a basic representation of this effect to demonstrate
how additional factors can be created and combined dynamically to affect the opposing force’s
fatigue rate. Sustained air operations decrease opposing force troop morale because the constant
threat of bombings or direct air attacks makes troops afraid to exit sheltered areas. It also makes
them more reluctant to use their heavy vehicles and artillery since those are often high priority
targets for offensive air sorties.7
These effects are included in my model through the “Sustained Air Sorties” variable. The
value of the variable is not the number of air sorties conducted throughout the course of the
battle but rather the aggregated overall effectiveness that these sorties have on decreasing
opposing force troop morale. The user is prompted to enter a value between 0 and 100 in the
“Sustained Air Sorties” variable. Similar to the PSYOP effectiveness variable, a value of 0
signifies either that there are no air sorties undertaken or that those executed were completely
ineffective in decreasing the opposing force troop morale, while a value of 100 means the sorties
were completely effective and resulted in a 1% increase of the opposing force’s base fatigue rate.
Values between 0 and 100 signify varying degrees of effectiveness and result in a fractional
increase of the opposing force’s fatigue rate. The equation used to include sustained air sortie
effects in the fatigue rate calculation is:
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Overall fatigue rate = Base fatigue rate + (1 * (Opposing force PSYOP effectiveness / 100))
Another factor cited by the RAND study as being of major importance in affecting
opposing force troop morale is food shortages. When troops experience food shortages their will
to fight is drastically reduced. This makes them much more prone to desertion or surrender than
they would be normally.
I included a basic representation of food shortage effects in my model to demonstrate
how external factors can be linked to increase the fidelity of the overall fatigue rate calculation.
Not only do food shortages lead to decreased opposing force troop morale, but the shortages
themselves are often caused by sustained air sorties. When sustained air sorties target lines of
communication (LOCs) they often disrupt the flow of food to soldiers and cause food shortages.
These shortages then combine with the psychological effects of the sustained air sorties
themselves and cause a greater overall impact upon the opposing force’s fatigue rate.
In my model, food shortages are modeled by the variable “Percent Food Shortage.” The
model user is able to specify two external variables, “Fraction of LOC Traffic Transporting
Food” and “% Sorties Targeting LOCs”. From these two variables the “Percent Food Shortage”
variable is calculated based on the equation below:
Percent Food Shortage = (Opposing Force Sustained Air Sorties Intensity * (Opposing Force %
Sorties Targeting LOCs/100)) * Fraction of LOC Traffic Transporting Food
This “Percent Food Shortage” variable is then combined with “Sustained Air Sorties
Intensity” and “PSYOP Effectiveness” to produce the overall calculation for the opposing force’s
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fatigue rate. When all factors mentioned above are taken into account, the equation for
calculating fatigue rate becomes:
Fatigue Rate (% per day) = (Base Fatigue Rate + (0.5 * PSYOP Effectiveness/100)) + ( 1 *
Opposing Sustained Air Sorties Intensity/100) + ( 2 * Blue Percent Food Shortage/100)
This value is then converted in to the correct units of fatigue/hour (since the time step of
the model is half an hour) and connected to the “Being Fatigued” flow.
Clearly, using this method of system dynamics to model troop morale effects allows
analysts to easily alter the model and either ramp up fidelity on factors of interest or conduct
sensitivity analyses on the included basic factor representations. By implementing additional
feedback loops between contributing factors, an analyst can create an effective model that
produces both numerical results and a graphical representation of the relationships between the
important factors. This graphical representation is especially valuable for analysts when they are
briefing their analysis results to customers who may be unfamiliar with the model. Because the
customers are able to look at the model and understand how relationships are being represented,
they can easily offer valuable feedback and help the analyst change the model to better reflect
reality. Getting this feedback from experts is invaluable for analysts wishing to better understand
complex systems.
Area Fire Effects
Often during warfare weapons are used that cause damage throughout an area and not just
on one specific target. Troops equipped with these weapons can fire at an area rather than a
specific opposing soldier to possibly cause increased damage to the opposing force. By using an
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appropriate weapon to execute area fire rather than direct fire, a soldier gains the possibility of
injuring more than one soldier with one volley. A side effect of this, however, is that often
collateral damage is caused by choosing to use area fire. Since area fire damages everything
within a given area, often neutral or civilian casualties are caused by soldiers executing area fire.
The Lanchester Laws provide a basic way to model area fire’s effect on overall force
attrition. The Lanchester Linear Law can be used when the fire coming from a force is spread
over an area as opposed to being aimed directly at intended targets. When the Lanchester Linear
Laws are factored into my attrition coefficient calculations, the equation for determining the
“Attrition” flow becomes:
Attrition = Opposing Forces * Opposing Attrition Coefficient *
((Combat Ratio)^(1-Weiss Parameter))8
The Weiss Parameter simulates how much of the fire coming from the opposing force is
area fire as opposed to direct fire. The Weiss Parameter has a range of [0.5, 1]. A Weiss
Parameter value of 1 means that all fire coming from the opposing force is direct fire, while a
value of 0.5 means that all fire coming from the opposing force is area fire.
In my model, the Weiss Parameter for each side is driven by the variable “Percent Area
Fire”. This variable specifies, at any given time, what percentage of the fire coming from a
particular force is area fire. This value can be set as a constant by the model user or connected
with other feedback loops to simulate how different area fire policies might affect the outcome of
the battle.
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Figure 5. The Full Combat Model
As can be discerned from the Lanchester Linear Law implementation equation above,
using area fire helps a force if its size is smaller than its opponent’s and hinders it otherwise. The
extent to which the area fire increases or decreases the opposing force’s attrition rate is
dependent upon how much larger or smaller a force is than its opponent. A force that is larger
than its opponent will want to use primarily direct fire since it is able to target at a greater than
1:1 ratio and should not waste ammunition trying to use area fire when it is not needed.
Conversely, if a force is smaller than its opponent it will want to use area fire because it enables
the force to cause a greater total amount of damage than it could using direct fire alone. Since the
force has a less than 1:1 soldier ratio when compared with its opponent it would not be able to
target every opposing soldier using direct fire. Therefore, area fire is appropriate and yields the
possibility of being a force multiplier.
In my model implementation, I have attached several variables to the “Percent Area Fire”
variable to demonstrate how the parameter might be used to examine other effects of warfare.
For example, since increased area fire leads to increased collateral damage I included a variable
called “Collateral Damage” for each side. With more in-depth study into the connection between
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area fire and specific amounts of collateral damage, an analyst could use this variable to track the
amount of collateral damage caused by the battle. My model assumes that the amount of
collateral damage caused is equal to the product of the opposing force’s attrition rate and the
percent of total fire coming as area fire, although different collateral damage equations could be
implemented if desired.
Connected to the “Collateral Damage” variable in my model is a small stock and flow
diagram portraying a variable level of public support for the conflict on both the red and blue
sides. This small model is not intended to be numerically rigorous but rather is included to show
how other factors of warfare can be analyzed in conjunction with running the central combat
model. Public support for a conflict grows and wanes throughout the course of warfare and this
often has implications for what happens in the conflict. If an analyst wanted to study how an
outside influence (such as public support) affected the conflict itself, he or she could build a
separate model structure like the basic public support structure portrayed in my model and then
connect it to the combat model at the appropriate points.
The “Public Support” stocks in my model are affected by the “Gaining Support” and
“Losing Support” flows. The “Gaining Support” flow increases when collateral damage is
caused by the opposing force, simulating the fact that often increased collateral damage caused
by the enemy can help convince the public that the opposing force is worth fighting and
attempting to bring to account for their actions. The “Losing Support” flow increases based on
opposing force PSYOP effectiveness and also increased collateral damage caused by that side’s
own force. A major purpose of PSYOP is to convince the opposing side’s population that the war
is unjustified and not worth fighting, and collateral damage caused by friendly forces often
angers war activists at home and causes protests against the conflict.
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While there are many effects that stem from varying levels of public support for a
conflict, the main one portrayed in my model is the effect that varying public support levels have
on the rules of engagement for a force. In this case, the rules of engagement variable is defined
as the maximum percent of area fire allowed by a force’s commander. My model shows that the
rules of engagement are affected by both public support for the conflict as well as the strength of
the opposing force, representing the tension that exists between needing to get the job done on
the battlefield while simultaneously retaining support for the conflict back home. The “Rules of
Engagement” variable is linked to the “Percent Area Fire” variable on each side and limits its
maximum value. This is the crucial link that connects the public support stock and flow model to
the overall combat model.
Conclusion
The combat model I created demonstrates how System Dynamics can be used for both
quantitative and qualitative analysis. Because quantifying the plethora of factors that impact
troop morale can be difficult, having a visual way to generate discussion on the topic is crucial.
An analyst wishing to drill down deeply into one or more factors associated with troop morale
and accurately quantify its effects would have to spend significant time with subject matter
experts understanding the complex interactions between external influences and human
psychological responses. The system dynamics model provides the analyst a tool with which to
drive and organize his discussions with experts as well as produce numerical analytical results.
Because of the extendable nature of my model, it can be used as a basis for the study of
interactions between of a wide variety of warfare-related topics. Through conducting systems
engineering analysis and continually improving the model calculations in conjunction with
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subject matter experts, analysts can gradually ramp up the model’s fidelity in areas of interest. In
concert with the numerical results achieved, this process of refinement and continual model
improvement is extremely beneficial and is an excellent method to use when trying to define and
understand complex systems.
“Modeling the Lanchester Laws with System Dynamics”; Artelli and Deckro; The Journal of Defense Modeling
and Simulation, January 2008, Volume 5 Number 1.
2
“Psychological Effects of U.S. Air Operations in Four Wars 1941-1991”; Hosmer, Stephen T; RAND, 1996.
3
“Aggregation, Disaggregation, and the 3:1 Rules in Ground Combat”; Davis, Paul K; RAND, 1995.
4
“Modeling the Lanchester Laws with System Dynamics”; Artelli and Deckro; The Journal of Defense Modeling
and Simulation, January 2008, Volume 5 Number 1.
5
“Psychological Effects of U.S. Air Operations in Four Wars 1941-1991”; Hosmer, Stephen T; RAND, 1996.
6
“Psychological Operations/Warfare”; Major Ed Rouse; http://www.psywarrior.com/psyhist.html .
7
Psychological Effects of U.S. Air Operations in Four Wars 1941-1991”; Hosmer, Stephen T; RAND, 1996.
8
“Modeling the Lanchester Laws with System Dynamics”; Artelli and Deckro; The Journal of Defense Modeling
and Simulation, January 2008, Volume 5 Number 1.
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