Using Behavior Moderators to Influence
CGF Command Entity Effectiveness and Performance
Philip D. Gillis, Ph.D.
US Army Research Institute
Simulator Systems Research Unit
12350 Research Parkway
Orlando, FL 32826-3276
407-384-3985
Philip_Gillis@stricom.army.mil
Steven R. Hursh, Ph.D.
Science Applications International Corporation
626 Town Center Dr.
Joppa, MD 21085
410-538-2901
Steven.R.Hursh@cpmx.saic.com
Keywords:
Behavior Moderators, Cognitive Abilities, Decision Making, Computer Generated Forces
ABSTRACT:. The basic problem addressed by this research concerns the need for more intelligent and realistic
command and human performance influenced unit behaviors for Computer Generated Forces. The variability of
command and unit behaviors due to human performance factors is basically left out of Army models and simulations
leading to too-predictable and unrealistic results. Some constructive and virtual simulations need better methods to
represent human performance variability in response to the stresses of war fighting in models and simulations. The
ARI “Realism in CGF Unit and Command Entities Behaviors Project” is an attempt to address these deficiencies
through the development of cognitive and human performance based models and algorithms that may be utilized by
Advanced Distributed Simulation.
1.0 Introduction
The variability of the performance of human beings in
response to the stressors of battle and as a part of
planning for and reacting to a wide variety of battlefield
events is a crucial area for modeling and simulation
(M&S) research and development. Human performance
in combat is widely acknowledged to be the most
important factor in determining victory or defeat;
however, there is only indirect evidence of this in
current battlefield simulation development practice.
This lack of research, and more importantly, the lack of
simulation developers’ implementations of some good
research that does exist, impacts the accurate behavior
of computer generated forces (CGF) entities, and it also
impacts the human resources required for CGF scenario
development and the exercise control of CGF entities.
These requirements are inappropriately demanding and
may be offloaded by more intelligence and realistic
CGF behaviors.
This paper will report on the authors’ R&D in this
particular area by means of addressing four primary
project thrusts:



Identification of the most relevant research
underlying human performance factors and
cognition that influence appropriate model
behaviors; predict interactions between variables
Development of human performance and cognitive
models from the theory
The development of the evaluation testbed, the
Command, Control, and Communications

Simulation (C3SIM), that is used to evaluate the
outputs of the models in a military simulation
testbed
The evaluation of the ARI Human Performance
Model (HPM) ver. 2.0 in C3SIM and the analysis
of the data.
variables for the production of decision type codes
(DTC), effectiveness, and performance variables that
influence command entity behavior. Most importantly,
HPM 2.0 output variables reflect models based upon
current research on the combined effects of fatigue,
stress, experience, intelligence, and personality type
variables upon performance and effectiveness.
1.1 The Technical Problem
Specific problems with current command entity and
individual unit behaviors in CGF within advanced
distributed simulation (ADS) domains include the
observations that such entities are neither influenced by
human performance variables nor are their behaviors
grounded in valid cognitive constructs during
situational awareness, communications, and course of
action activities. Command entities and otherwise
animate objects frequently act based upon ground truth,
rather than the more realistic perceived truth of a
situation. And when such entities do act upon
information that has been received, frequently the
actions are inappropriately omnipotent. Sometimes a
simulation developer will attempt to “degrade”
command entity/unit performance, but will do so by
means of the application of a random number generator
affecting performance. Such “adjustments” are not
grounded in appropriate and valid cognitive or human
factors research and experimentation; thus, such
attempts at varying performance possess no “construct
validity.”
1.2 Background
For Phase I of this research, Gillis [1] reported on the
effects of sleep deprivation and stress on a CGF
Battalion Command Entity’s (BCE) performance in a
recreated battle, using National Training Center data.
The Phase I BCE possessed limited situational
awareness capabilities and course of action selection.
For Phase II, the scope of a BCE’s actions has been
suitably broadened. In a CGF context, four of the
realistic measurable consequences reflecting the
appropriate variability of human performance behavior
that must be addressed are: correct or incorrect
situational assessment, responsive or unresponsive
communications, correct or incorrect course of action
selection, and the timeliness of actions. The current
phase explores the Battalion Command Entity’s
enhancement or degradation of behavior in all of these
areas, based in a simulated battle, but recreated from
National Training Center data.
2.0 Research Methodology
The research and development methodology for this
project includes efforts to:



Obtain and analyze the most relevant research
underlying human performance and cognition and
predict the likely interactions between variables
Develop human performance variable and
cognitive models with construct validity that are
based upon research, empirical findings, and
predictions
Develop an evaluation testbed, the Command,
Control, and Communications Simulation, that is
used to validate the models
Evaluate the models and algorithms, analyze the
evaluation data, validate the predicted interactions
This Phase II report depicts both the direct and
combined effects of fatigue, stress, time pressure,
confidence-building events, intelligence, experience,
and aggressive, neutral, or risk averse personality type
on a CGF Battalion Command Entity’s effectiveness
and performance in a recreated battle, using National
Training Center data.

The implementation of these behavior moderators in
the Human Performance Model, ver. 2.0. allows a
much more realistic observation of the effects of human
performance variables on CGF command entities
behaviors than that reported for phase I. The current
HPM software prototype uses Walter Reed Army
Institute of Research data to model the effects of sleep
deprivation on performance. HPM ver 2.0 also uses
select experimental findings on other independent
Models and algorithms reflecting human performance
variability are primarily gleamed from the extant
research data in the field on human performance
variable and training effectiveness, and appropriate
cognitive models are implemented that apply to
simulated entity acts of cognition.
The structure of this report will reflect research and
development in these four areas in sequential order.
2.1 Research and empirical findings from the field
One of the most important precepts that a researcher
encounters during an attempt to build valid models of
human performance on the battlefield is that humans
are generally stressed under battlefield conditions.
Therefore, several recent and extensive reviews of the
stress literature form the empirical basis for the HPM
ver. 2.0 “effectiveness” and “performance” outputs.
Driskell, et. al. [2] and Driskell, Hughes, Willis,
Cannon-Bowers, and Salas [3] have conducted two
exhaustive meta-analysis reviews of the stress
literature. These were conducted specifically to support
the Air Force and Navy in preparing for studies to
simulate the stressful environment for training. These
reports were supplemented by an analysis of material
and findings from three recent collections of reviews
edited by Driskell and Salas [4]; Klein, Orasanu,
Calderwood, and Zsambok [5]; and Flin, Salas, Strub,
and Martin [6].
Mullins, Fatkin, Modrow, & Rice [7] found that
participants with less experience reported higher ratings
of overall stress. Also, several other studies have
documented the benefits of experience for cognitive
performance under stress in military-context
evaluations (Kirschenbaum, [8]; Stokes, A. F. [9];
Klein, Calderwood, & Clinton-Cirocco, [10]; Klein &
Calderwood, [11] ). Still other studies have studied the
effects of intelligence on decision-making and found
cognitive performance to be positively correlated with
performance (Whitmarsh & Sulzen [12] ).
All together, these reviews summarize the results of
over 1,350 studies of stress, many sponsored by
branches of the Armed Forces. ARI contracted Dr.
Steve Hursh of Science Applications International
Corporation (SAIC) in Joppa, MD to compile and
analyze these studies and develop a decision making
under stress (DMUS) model that could be used to
influence a CGF command entity’s behaviors in a
battlefield simulation. Hursh [13] considered a variety
of factors that have been found to degrade effectiveness
and have been defined as stressors.
3.0 The Human Performance Model
While HPM ver. 1.0 was a decision-making under
stress model used solely to compute human
effectiveness, ver. 2.0 draws upon other ARI human
performance variable findings from the field in an
attempt to predict a CGF BCE’s performance in
activities in addition to decision making and also not
under stressful conditions.
The development of the HPM is thus proceeding in
stages, with the incorporation of the DMUS model first,
with ARI expanding the outputs based upon stress and
non-stress related performance research resulting in
HPM ver. 2.0.
For the Phase II completion of the project, the Human
Performance Model ver 2.0 utilizes eight types of
inputs:








Sleep Deprivation/accumulation
Variable Time Pressure
Performance Use
Stress Effects
Confidence Building Effects
Experience Effects
Intelligence Effects
Aggression/Risk Aversion Tendencies
And the HPM ver 2.0 produces four types of outputs:




Effectiveness, a DMUS quantitative measure
Decision Response Time, a quantitative measure
Performance, a quantitative measure that has the
option of using DMUS research
Decision Type Code, DMUS qualitative outputs,
consisting of correct, random, aggressive, and risk
adverse codes.
Since the effectiveness and performance output
variables were the primary areas for concentration for
Phase II of this study, they will be examined in more
detail for this paper.
3.1 Decision making under stress
The HPM ver 1.0 and refined HPM ver 2.0 DMUS
stress model inputs related to the effectiveness output
variable were implemented based primarily on the
literature reviews by Driskell and colleagues [2] and
Driskell, Hughes Guy, Willis, Cannon-Bowers, and
Salas [3]. This research suggests the two most salient
input stressors, useful for the confines of this study and
the development of the effectiveness variable, are time
pressure and fatigue brought on by continuous
operations.
Thus it was necessary that HPM ver 2.0 outputs
primarily reflect the effects of time pressure, fatigue
and stress and furthermore, that the HPM reflect the
findings that the effects of stress on decision making
are strongly dependent on experience. When
confronted with time pressure and work overload, the
low experience CGF BCE should emulate the less
experienced human decision maker, who is subject to a
variety of errors that can degrade the quality of
decisions in a variety of ways, as summarized by
Orasanu and Backer [14]: “decision makers use a small
number of heuristics (rules) in making their decisions
(Tversky & Kahneman [15] ), fail to consider all
possible decisions and outcome options (Slovic,
Fischhoff, & Lichtenstein, [16] ), are inconsistent in
dealing with risk (Lopes [17] ), ....[are] likely to display
premature closure - terminating the decisional dilemma
without generating all the alternatives and without
seeking all available information about the outcomes
(Janis [18] ).”
In contrast, studies of experienced decision-makers
under stress suggest that a more streamlined decision
strategy is used-- Naturalistic Decision-Making (Klein,
Orasanu, Calderwood, and Zsambok [5]; Orasanu &
Connolly [19]; Klein [20], in press; Klein & Crandall
[21]. This strategy is best suited for settings where the
decision task is unclear, the available information is
incomplete, unreliable, or continuously changing, and
stressors such as time pressure and high stakes are
present (Orasanu & Connolly [19] ). Under such
situations, it is impractical to adopt an exhaustive
prescriptive decision strategy that requires complete
data and is time-consuming. Klein [22] has proposed
that experienced decision-makers faced with this
situation use the Recognition-Primed Decision (RPD)
model.
According to this model (Klein [23] ), the experienced
decision-maker can make rapid but effective decisions
by using experience to size up the situation and to
generate and evaluate COAs one at a time (as opposed
to comparatively). In the simple case, the situation is
recognized as typical of ones encountered before, and a
typical COA can be immediately selected. The product
of naturalistic decision-making is a decision that is
adequate and resistant to time pressure, if not
absolutely optimal. Alternative, more exhaustive
strategies are disrupted by time pressure and,
consequently, yield decisions that are flawed or not
timely applied (Hursh [13] ). The studies of naturalistic
decision-making “show that experienced decisionmakers are able to generate reasonable options as the
first ones they consider, and select these options to
carry out when performing a stressful task such as
flying a complex mission in a simulator (Klein [22];
Klein, Wolf, Militello, & Zsambok [24]; Stokes,
Kemper, & Marsh [25] and Yates [26] ).”
Naturalistic decision-making critically depends on a
high level of training and experience, and project goals
mandate the observation of a simulated, poorly-trained
and inexperienced BCE moving teams around on a
battlefield as well. The need for the CGF BCE to act
based upon variable experience and training levels
prompted the development of a decision type code
(DTC) as one of the HPM outputs.
3.2 Fatigue and sleep deprivation effects
Modern combat augmented by night vision devices and
electronic means of navigation and communication is
not constrained by time of day and the cloak of
darkness. The flow of battle may be relatively
continuous with few breaks for sleep and recuperation.
Under these conditions of continuous or sustained
operations, sleep deprivation and fatigue may be a
natural human hazard. Moreover, studies of sleep
patterns in simulated combat at the National Training
Center indicate that commanders (Lieutenant Colonels
and Colonels) in force on force operations average just
over four hours of sleep per day, about half the normal
requirement for fully effective performance (Belenky,
Balkin, Thomas, Redmond, Kant, Thorne, Sing,
Wesensten, & Bliese [27] ).
Furthermore, laboratory studies of sleep deprivation
indicate that the most sensitive indicators of sleep
deprivation are cognitive operations, such as logical
reasoning, mathematical operations, short term
memory, and decision making (Thorne, Genser, Sing &
Hegge [28]; Banderet, Stokes, Francesconi, Kowal, &
Naitoh [29]; Horne [30]; Angus & Heslegrave [31] ).
Hence, fatigue can be a strong disrupter of command
level performance.
For the implementation of the effectiveness variable
and DMUS Decision Type Code outputs used in HPM
2.0, the Sleep and Performance Model , based upon a
previous model SAIC had developed in conjunction
with the Division of Neuropsychiatry of the Walter
Reed Army Institute of Research (WRAIR), was
maintained. A comparison of the algorithm, however,
with both Air Force and U.K. Ministry of Defence
algorithms is ongoing.
3.3 Calculation of the effectiveness variable
At the heart of the ver. 2.0 model is a cognitive
reservoir that maintains a balance of effective
performance units. During sleep, units are added to the
cognitive reservoir according to the sleep accumulation
function, which specifies how many units of effective
performance are credited for each minute of sleep. The
rate of accumulation is responsive to the sleep deficit,
the difference between the current level of the cognitive
reservoir and its maximum capacity. During time
awake, units are subtracted from the cognitive reservoir
according to the performance use function, which
specifies a linear decrease in the cognitive reservoir
with each minute awake (Hursh [13] ).
The resulting effectiveness variable is the sum of three
variables: the level of the cognitive reservoir, expressed
as a percent of its maximum capacity; the performance
circadian rhythm, and the general stress effects.
is phasic, depending on time pressure at each stage of
the event or battle (Crego & Spinks [32] ). Periods of
time-constrained decision pressure are interspersed
with periods of time-rich decision opportunity. The
first level of the structure in Figure 1 represents the
oscillation between these two general states. The
outcome of this first branch is based on the level of E
and is probabilistic; as E varies based on changes in
time pressure, stress, and fatigue, the likelihood of
branching to the left or right varies continuously.
3.4 Interactions and final DTC outputs
The effects of stress on performance are strongly
conditioned by individual factors such as levels of
training , experience, and type of personality. The
importance of these factors is strongly dependent on the
level of stress, fatigue, and time pressure that a human
or simulated CE is operates under. The interactions of
these factors in HPM 2.0 result in the output DTC’s:
correct, random, or situation primed, as diagrammed in
Figure 1. Situation primed DTC’s were further
categorized as aggressive, neutral, or risk averse. The
production of the codes and consequent selection of
COA’S by the BCE proceeds in the following manner:
The overall level of effectiveness, E, resulting from the
sleep and fatigue model and the stress process
determines the first level of control. Based on detailed
studies of decision making in stressful emergency
situations, it is clear that the nature of decision making
The left hand branch is selected most often when E is
high based on low time pressure, stress and fatigue.
This is a time rich decision opportunity and the greater
the level of training, experience and intelligence, the
more likely the CE will select the optimal or correct
COA. As training, experience, and I.Q. decline, the
greater the likelihood that the decision will simply be a
random selection from those courses of action that are
reasonable for this situation. Note, however, that this is
a probabilistic process and only under the conditions of
little experience and training will course of action
selection actually be random. In practice, given the
normal requirements for command, no command entity
would ever approach this extreme case.
The right hand branch is selected most often when E is
low based on high time pressure, stress and fatigue.
This represents time constrained decision pressure and
results in situation primed decision making after the
Naturalistic Decision Making
Effectiveness (E) decreases with
Increasing Stress, Fatigue, and Time Pressure
What is the value of E?
(v
ber
um
n
m
ndo
Ra

E
)
Ra
nd
om
(v
Random Course of Action
)
(v
Low Experience

d
an
<
X
)
X
Low training and
Low experience
Correct Course of Action
nu
mb
er(
v)
Level of Training(Tr) and
Experience(X)?
)
(v
High training or
High experience
< v)
Tr < (
X
Tr 
X  (v) or
(v)
Level of Training (Tr) and
Experience (X)?
E<
High Experience
Situation Primed
Course of Action
Figure 1. Naturalistic decision making under stress: decision tree showing effects of stress, fatigue, time
pressure, training and experience.
concept of recognition primed decision making
described by Klein [22]. When a BCE is influenced by
variables reflecting these conditions, it will act on prior
experience with similar situations, if the BCE
experience variable reflects good to high experience.




According to this mode of decision making, the
experienced commander, when confronted with
extreme time pressure or stress, does not attempt an
exhaustive utility analysis of all available options.
Rather, the commander looks for features of the
situation that resemble prior experiences and recalls
successful decisions from those prior occasions. Once
the situation is “type identified”, the commander can
apply a “typical” course of action. Obviously, the
likelihood of engaging in situation primed decision
making will strongly depend on the level of prior
experience required to build this catalog of typical
courses of action.
TRW SMEs validated the correct actions prescribed
within the DST, and within situational contexts, further
categorized potential COAs as indicative of the
following types:
For Phase II, situation primed COA’s were largely
coded similar to “correct” COA’s, taken from the
decision support template and SME analyses of the
situations in the context of the mission itself. Situation
primed COA’s were further subcategorized as being
typical of aggressive, neutral, or a risk averse
personality type.
Time constraints did not allow the implementation for a
full range of COA’s reflecting a broad range of
experience, hence a broad range of RPD’s that a BCE
could enact. During Phase III, these distinctions will
be integrated.
For its production of DTC outputs, HPM ver. 2.0
substantially factors in the effects of experience and
intelligence in a manner that was consistent with RPD
theory and with other findings from the field.
Experience and intelligence effects influence the
magnitude of a random variable, which is compared to
E; the resultant comparison controls the top-level
branch selection in Figure 1. The magnitude of the IQ
and experience effects for the calculation of the random
variable was based upon empirical findings from the
literature in the area.
3.4.1 Transforming DTC’s to COA’s
For Phase II a contract was awarded to TRW, in
Orlando, FL, for purposes of analyzing a NTC mission
for elements useful for evaluating BCE effectiveness
and performance. Subject matter experts (SMEs)
analyzed one Movement to Contact mission and
produced for the mission:





The Decision Support Template
Revised five paragraph OPORDS
Validation of Bluefor and Opfor units
Matrices for the Decision Support Template (DST)
actions
Correct
Highly to Poorly Trained
Aggressive
Risk Averse
High to Low Experience
SMEs also analyzed the mission for potentially
stressful and confidence building events that could
occur during the course of the mission. In addition,
stress types and amplitudes were correlated with
potential events.
3.5 The effects of time pressure
The important finding from the SAIC review of
decision making under time pressure is that much larger
effects should be expected with inexperienced
commanders/CGF command entities as compared to
experienced commanders/CGF command entities. For
inexperienced decision makers who cannot rely on a
recognition-primed decision strategy or who attempt to
use an exhaustive prescriptive strategy, the effects of
time pressure will be to seriously degrade decision
making (Crego & Spinks [32] ).
Driskell [2] has summarized the literature on time
pressure and have found that a relatively simple linear
equation relates the magnitude of time pressure to the
size of the stress effect. Hursh advised that the
magnitude of time pressure (MAG) be defined as:
MAG = longer time period/(longer period + shorter
period).
Hence, a task that normally is performed with high
accuracy in 60 seconds that is required in 42 seconds
would have a MAG value of .587 and would predict a
correlation coefficient with accuracy (r) of -.3.
For the implementation of the effects of time pressure
in HPM, ver. 2.0, Hursh proposed the utilization of
three a priori levels of potential time pressures. The
levels were categorized, but only two: high and none,
were actually implemented and tested for HPM and
C3SIM, ver. 2.0:



low (.481)
moderate (.587)
high (.707)
The corresponding changes in accuracy were defined
as :
 low (r = -.1)
 moderate (r = -.3)
 high (r = -.5).
Versions 3.0 of HPM and C3SIM will see all three
levels implemented.
Driskell did not consider the modulating effects of
experience on the magnitude of the time pressure effect
[2]. Based on the review of naturalistic decision
making (Klein [22 and 33] ), time pressure tends to
increase the likelihood that the model will attempt to
provide a situation primed decision, and it was this
latter finding that was implemented in HPM 2.0.
3.6 Calculation of the stress effect
The effects of stress can degrade cognitive
performance as represented in a lower computation of
the effectiveness variable. The stress effect (SE), as
represented in this simplified model, is designed to
reflect the influence of stressful events and time
pressure on effectiveness in making decisions.
One key factor in this model of stress is the occurrence
of significant events in the battle scenario that may
either advance the mission (positive or confidence
building events) or hinder the mission (negative or
stressful events). The computation of the stress effect
depends, in part, on the frequency of those events and
their value or severity. For purposes of computing the
stress effect, mission advancing or confidence building
events ranged in value from 0 to +1; hindering or
stressful events ranged in value from 0 to -1. The
overall stress effect at any moment in time considers
the sum of these values over the preceding time
interval. The actual calculation of the effectiveness and
stress effect variables are reported in Gillis, 1998.
The ability to process and react to events is modulated
by the time available. During a slowly developing
operation with events occurring infrequently in time
there is plenty of time for a human or simulated
command entity to react to events and take appropriate
action. This tends to diminish the effects of stressful
events. Hence, the value of battle events is multiplied
by a factor that represents time pressure. For example,
a time pressure value of .5 would reflect a magnitude of
time pressure of .707, while a time pressure of .1 would
reflect a magnitude of time pressure of .481. Since it is
not possible at present to actually measure the
magnitude of time pressure in a C3SIM mission
scenario, the model was exercised with a range of time
pressure values from 0 to 1, that represent a range of
time pressure magnitudes.
3.7 The decay of the stress effect over time
The overall value of the stress effect is subject to the
decay of memory over time. As time elapses since an
event, the value of that event in contributing to the total
value of SE declines according to the double
exponential shown below, based on classic memory
experiments (Ebbinghaus [34] ). The initial term of the
expression represents short-term memory and the
second term represents long-term memory:
CurrentValue  136.5e 10t  31e 0.195t  19.44
The above equation is computed given that Current
Value is the percent of the original value at time t since
the original value, and time is in hours.
3.8 The performance output variable
The current implementation of the model underlying
the performance output variable can be viewed as a
modified version of a study by Locklear, Powell, and
Fiedler (1988) which examined the impact of individual
experience and intelligence of military leaders on
decision performance under varying degrees of stress
(Guest, 1998, in Gillis, P.D., Hursh, S., Guest, M., &
Sweetman, B. [35].) Their experiment incorporated
two levels of intelligence (low, high) and two levels of
experience (low, high) based on median splits. Three
levels of stress were included (low, medium, high).
Results indicated that:




intelligence was a benefit at all stress levels;
experienced leaders outperformed less experienced
leaders in the high stress condition
at low and moderate stress levels, intelligence
resulted in better performance than experience
at high levels of stress, experience resulted in
better performance than intelligence.
This study is in agreement with other studies that
examined more specific effects of various factors on
cognitive performance under stress. Importantly,
though, very few studies incorporated aspects of
experience and intelligence with the consideration of
stress. Therefore, the Locklear, Fiedler, & Powell [36]
study is chosen as a representative basis for the current
model development. The current model attempts to
capture the impact of expertise on cognitive
performance under stress, in addition to benefits of
intelligence (Guest in Gillis, P.D., Hursh, S., Guest, M.,
& Sweetman, B. [35] ).
The key characteristics of the cognitive performance
model related to individual experience, intelligence,
and stress can be summarized as follows:




Intelligence positively affects performance at all
levels but is subject to performance decrement, at
high stress levels, depending on experience;
At high levels of stress, experience positively
affects performance more than intelligence;
Experienced individuals have little or no
performance decrement due to high stress;
Novices have a significant performance decrement
under high stress.
The current model implements:




a three unit performance decrement for novice
individuals from low to high stress, when
intelligence remains constant
a 1.5 unit performance decrement for middleexperienced individuals from low to high stress,
when intelligence remains constant
no performance decrement for experts from low to
high stress
a 1.0 unit increase in performance with a 1.0 unit
increase in intelligence, when experience and stress
remain constant.
Guest recommended the above findings results in the
following regression equation, which was used to
compute performance in HPM, ver. 2.0, under high
stress conditions:
y’ = 2.25(EXPERIENCE) + 1.0(INTELLIGENCE) 1.5(STRESS) + 1.5
This equation is currently undergoing minor revision
based upon the incorporation of new findings.
4.0 The C3SIM Testbed
The evaluation software requirements for this project
are extensive, and essentially call for specialized
software to be developed. Early on during the
conception phase for the project, the question related to
the appropriateness of command echelon necessary for
observing the effects of HPM clearly dictated that the
most significant effects of the HPM variables would be
observed at battalion level or higher, because decision
making at such a level possesses a formidable.
The effects of fatigue are, of course, a problem at any
level of operations on the battlefield; however, at
higher levels of command errors in judgement have
great impact on the success or failure of the mission
and command experience can moderate the effects of
stress and fatigue.
Given that sleep deprivation, accumulation, and
performance use were three important variables
operating during the study, it was obvious that
simulated missions used had to be fairly lengthy. The
resulting mission requirements for the project included
the necessity to observe a battalion command entity,
commanding three or four teams in a simulated battle
over a period of time of at least eight hours.
Considering the various study requirements, several
constructive simulations were initially considered for
purposes of the evaluation of the Human Performance
Model developed for this project. MODSAF was, at
first, the primary candidate; however, based upon two
cost estimates for using MODSAF to test the full range
of outputs of HPM at the battalion level of command,
MODSAF was quickly dismissed. It was considered
too complex and difficult to modify, given the
monetary resources available for the current project.
Thus, it was deemed more advisable to develop a
software evaluation testbed whereby the effects of the
human performance variables acting on simulated
command entities at higher echelon levels could be
observed. Command actions related to situational
awareness, communications, and course of action
activities at the battalion level clearly have a potentially
greater impact than at squad level.
4.1 C3SIM ver. 2.0 functionality
The evaluation testbed requirements for this project
are extensive, and essentially call for specialized
software to be developed. The C3SIM evaluation
testbed has been in various stages of development for
the past five years. The initial functionality of C3SIM
was developed by the author, but was turned over to
Systems Engineering Associates in San Diego for
enhancement and refinement, which resulted in C3SIM
ver. 1.0. For Phase II, the Institute for Simulation and
Training continued to enhance those portions of the
C3SIM functionality that would be used to test HPM.
upon the influences of chance, as are events on the
battlefield.
C3SIM at the end of Phase II is a Win32 application
that, in replay mode, is capable of reading temporal and
positional data from National Training Center data sets,
and consequently replaying the battle. In this mode,
C3SIM employs probability of hit and kill algorithms
from both the Close Combat Tactical Trainer and the
Joint Research Training Center to assess attrition for
both Bluefor and Opfor. Results from C3SIM mission
replay mode and the actual NTC mission results are
published in Gillis [1].
The actual values of effectiveness and performance are
also sent over to C3SIM. These values are also used in
a similar stochastic manner in the following situations:
C3SIM, ver. 2.0, is currently configured to run on two,
or preferably three, Win32 platforms. It uses Windows
Distributed Component technology to allow the three
separate applications residing on separate machines to
communicate. The three separate applications consist
of the C3SIM simulation itself, the HPM, and the
Knowledge Base Server (KBS). The KBS provides
output in the form of a temporally based knowledge
base to C3SIM consisting of battalion level team
courses of action (COA); these COA’s are based upon
the decision support template (DST) that was
developed by the actual Battalion Commander’s staff
for the movement to contact mission.
4.1.1 The interaction of HPM variables within
HPM, C3SIM, and KBS
HPM ver. 2.0 currently sends the previously discussed
effectiveness and performance variables to C3SIM, and
these variables affect a variety of conditions and
actions within C3SIM.
Effectiveness and performance variables predominantly
influence courses of action returned to C3SIM from
HPM. Figure 1 demonstrated possible Decision Type
Codes that could be returned, consisting of: Correct
(aggressive, neutral, or passive); Random (aggressive,
neutral, or passive); and Situation primed (aggressive,
neutral, or passive
The relative effects of effectiveness and performance
variables upon the BCE’s situational awareness,
communications, and course of action selections are
being investigated during Phase III of this project.
All DTC’s are processed within HPM, and all HPM
outputs are subject to stochastic influences. For
example, although effectiveness and performance are
the result of mathematical equations resulting in real
numbers, the actual output produced is partially based

During the BCE’s situational awareness events,
such as type and depth of situational awareness
activities.

During the BCE’s communication events, such as
whether or not it responded to a team situation
report (SITREP) or whether or not it issued a
fragmentary order (FRAGO) based upon the
SITREP.

During select BCE’s course of action, in addition
to those returned by the KB server. FRAGO’s
consisting of Movement and Fire orders are two
examples of COA’s within this category of
processing
Two of the most important variables, time pressure on
performance and decision response time, have not been
fully implemented within C3SIM ver 2.0. Since time
pressure has such an important impact upon human
performance, much of the movement to contact mission
is run with the time pressure variable set to a
moderately high setting.
Decision response time is currently computed within
HPM and is sent over to C3SIM, yet the variable is not
fully implemented with the BCE’s situational
awareness, communications, and course of action
selection activities, but will be fully implemented
during Phase III of the project.
5.0 Data Analysis
A very strong emphasis was placed upon data
collection and analysis for Phase II. To date
approximately 200 simulation runs have been
completed and results analyzed over time.
The data was collected from the simulation runs with
the simulations in a time-skewed mode. A comparison
of the skewed mode with real time mode demonstrated
an approximate 10% difference in attrition levels for
both sides. Attrition levels were higher in real time
mode because all unit’s fire cycles represented more
realistic levels. However, due to the time constraints
imposed on this study, it was not possible to complete
all simulation runs in real time mode. Neither was it
possible to complete multiple runs of each tested
permutation of independent variables.
5.0.1 Findings from multiple runs of one
permutation
For purposes of the current analysis, it was necessary to
demonstrate that a single run at each permutation
setting was adequate, i.e., that the stochastic nature of
the HPM and C3SIM models used for the
determination of actions was negligible; that is,
negligible for purposes of the statistical analysis of the
data.
Therefore, multiple runs of one permutation of the
independent variables were completed and results were
randomly divided into two samples. Linear associations
between dependent variables were observed, with
results indicating high correlations and linear
associations between pairings for the key study
dependent variables.
Thus, it was deemed reasonable to proceed with the
current analysis, based upon the assumption that the
stochastic influences of HPM and C3SIM does not
produce statistically significant different outcomes
between simulation runs for key dependent variables.
5.0.2 Assumptions for the statistical analysis for the
single runs of the current set of data
For the approximate 200 simulation runs that have been
completed, the Shapiro Wilks’ test for normality was
applied to the dependent variables, with results
indicating normal distributions for all key variables.
Based upon this finding, levels of statistical
significance are reported for the Pearson’s Product
Moment Correlation Coefficients and for the Student’s
t test associated with the linear regression analyses. It
should be noted, however, that such significant
differences are reported, at this time, for purposes of
comparison, only, and cannot be construed as
completely valid evidence of significant differences
until multiple runs of each permutation of independent
variables are completed during Phase III of this project.
5.1 The independent variables (I.V.) for the analysis
of HPM, ver 2.0.
The following independent variables have been run in
the C3SIM evaluation testbed and analyzed:



Sleep, range 2 to 7 hours/night over 5 nights
Experience, 3 different levels
Aggressive, Neutral, or Risk Averse Personality



Intelligence (I.Q.), 3 different levels
Time Pressure
Experience level of OpFor BCE
648 permutations of the above independent variables
are possible. It was not possible to analyze the varying
experience levels of the Opfor BCE for this study.
Therefore, all simulations were run with the Opfor BCE
experience level set to “highly experienced.”
5.2 The dependent variables (D.V.), their means (X),
and standard deviations (s’) for the analysis of
HPM.
The set of dependent variables that have been used for
the current analysis are collected at the end of each
simulation run, which constitutes the end of the mission
(EOM).
They are:
Dependent Variable
X
s’
Bluefor Survivability Index
Opfor Survivability Index
TF4-64 Firepower Damage
TF4-64 Mobility Damage
TF4-64 FP and Mob Damage
TF4-64 Fully Mission Capable
TF4-64 Survivability Index
TF4-64 in Objective FORD
TF4-64 Mission Finish
Opfor in Furlong Ridge
Bluefor Attacks
Opfor Attacks
HPM Resource Balance EOM
HPM Effectiveness at EOM
HPM Performance at EOM
BCE Communications Failures
Stressors Received by BCE
78.2
56.9
25.4
29.2
18.8
31.6
80.1
22.4
13:06
8.8
3706
1110
1266
38.6
30.3
28.5
175
4.0
81.2
6.4
7.3
6.1
9.6
8.9
30.4
10.6
729
190
172
17.6
22.7
57.4
192.4
Table 1. The dependent variables, their means (X), and
standard deviations (s’) for the analysis of HPM
5.3 The analysis of the results
Four of the dependent variables were considered to be
the strongest indicators of Bluefor/Opfor mission
success and/or failure and were considered key
variables for the purposes of this analysis:



TF4-64 Survivability Index
TF4-64 in Objective FORD
TF4-64 Mission Completion Time

Opfor in Furlong Ridge
This consideration was based upon both Bluefor and
Opfor commanders’ intents for the missions, see Gillis
[1].
The Commander’s intent for the BLUFOR was
“Destroy the Advance MRD in zone and seize key
terrain for follow-on defense. The end state is the
destruction of the advance guard (destroy Opfor in
Furlong Ridge) without penetration of PL FLORIDA,
occupy OBJECTIVE FORD with sufficient combat
power to establish a defense in sector to defeat followon enemy Motorized Rifle Regiments.”
The Commander’s Intent for the OpFor was “Find the
enemy and destroy his reconnaissance and lead
elements with the Motorized Rifle battalion (MRB)
Advance Guard battalions. Success is destroying enemy
in zone while maintaining sufficient combat power to
seize the regimental subsequent objectives.”
NTC data sets used for the first mission were complete
with the exception of HINDs, eight T-72, and ten BMP
units for the Opfor and all artillery units for both
BLUFOR and Opfor. Therefore, CGF operatorcontrolled HINDs, missing T-72s, BMPs, Opfor and
Bluefor artillery were utilized in order to make up for
the missing NTC data. For this particular set of
simulation runs, Opfor artillery fires and other Opfor
CGF attacks were all similar and orchestrated based
upon the Opfor Operations Order for the mission and
the Opfor Doctrinal Support Package. Bluefor artillery
fires were consistent with Bluefor OPORD fire support
matrix for the mission. Time limits for all missions
were consistent with actual NTC mission data.
significantly different linear relations between
dependent and independent variables will be reported.
The statistic used to test this hypothesis is Student’s t
distribution. The significance of this test will also be
reported.
A commonly used measure of how well the linear
model between dependent and independent variables
actually fits, or the goodness of fit of a linear model, is
r2, or the coefficient of determination. The combined r2
for all independent variables effects upon a single
dependent variable will be reported in the discussion of
each dependent variable analysis.
5.3.3.1 Dependent Variable TF4-64 in Objective
FORD
Per the Bluefor commander’s intent, the success of the
BCE at moving the four teams up into Objective Ford
was a critical mission element. TF4-64 in Objective
FORD is reported as a percentage of the task force that
actually made it into Objective FORD at the end of the
mission.
The Pearson Product Moment Correlation Coefficients
between the five BCE/HPM relevant independent
variables (all except “Experience level of Opfor BCE”)
and TF4-64 in Objective FORD all demonstrated
positive r’s, with the r values for I.V. sleep of .29 and
for experience of .39 being significant at the .01 level
of significance.
5.3.2 Significant Pearson Product Moment
correlation coefficients for dependent variables
demonstrating linear association
The multiple regression analysis for the same
combination of variables demonstrated an r2 of .29,
thus indicating 29% of the observed variability for the
dependent variable TF4-64 in Objective FORD can be
accounted for by the five I.V.s. The student’s t for the
slopes (B1) for the independent variables sleep, and
time pressure, and experience were all significant at
the .01 level of significance for this analysis.
An observation of the linear correlations, the Pearson
Product Moment Correlation Coefficients (r), between
variables points toward a useful study of the relations
among select variables. This value will be reported in
the discussion of each dependent variable analysis.
This particular analysis constitutes one of the strongest
findings for the study; it demonstrates the effects of
increased levels of sleep and experience, and decreased
amounts of time pressure on the probability for the
BCE’s mission success.
5.3.3 Multiple linear regression analysis of
dependent variables demonstrating significant
correlation.
The positive, though not significant, correlation
between aggression and the dependent variable (r =
.07) indicates that the more aggressive BCE in C3SIM
was more successful in getting its units to the final
objective, though as shall be noted in the analysis of the
TF4-64 Survivability Index, a more aggressive BCE
lost more of its units doing so.
A frequently tested hypothesis for variable associations
of this type is that there is no linear relationship
between an independent and dependent variable—that
the slope of the regression line is 0. Slopes (B1) for
5.3.2 Dependent Variable Opfor in Furlong Ridge
Again, per the Bluefor commander’s intent, the success
of the Bluefor BCE at “destroying the Advance MRD
in zone” was a critical mission element. Opfor in
Furlong Ridge is reported as a percentage of the Opfor
MRD that remained in the main battle zone at the end
of the mission.
The Pearson Product Moment Correlation Coefficients
between the independent variables of aggression,
sleep, experience, and I.Q. all demonstrated negative
correlations with the dependent variable, as they
should. As the levels of sleep, experience, and I.Q.
increased and the BCE became more aggressive, the
percentage of the Opfor in the battle zone should, and
did, reflect higher attrition rates. The r value for I.V.
sleep of -.73 demonstrated significance at the .01 level
of significance.
The multiple regression analysis for the same
combination of variables demonstrated an r2 of .56,
thus indicating 56% of the observed variability for the
dependent variable “Opfor in Furlong Ridge” could be
accounted for by the five I.V.s. The student’s t for the
slopes (B1) for the independent variables sleep and
time pressure were again significant at the .01 level of
significance for this analysis.
The significance of this particular analysis lies in the
observation that the effects of the five I.V.s primarily
accounted for the absence of the enemy in Furlong
Ridge at the end of the mission. As the appropriate
variables increased and time pressure decreased, more
of the enemy was attrited in Furlong Ridge.
Notable in analysis of this dependent variable is the
lack of statistical significance for the effects of the
experience variable for increased attrition on the Opfor
in Furlong Ridge. However, the lack of statistical
significance does not likely point to a deficiency in the
HPM algorithms incorporating the experience effects,
but rather a problem in incorporating the effects of
greater experience in the BCE in the simulation itself, a
known shortcoming, as pointed out in section 3.4 of
this paper. This deficiency is being corrected during
Phase III of this project by means of the incorporation
of refinements to the COA’s, reflecting a greater range
or experience levels possible for the BCE.
effectiveness of the TF4-64 BCE. Since another TF
(TF1-33) was operating in the arena in support of TF464, the survivability index of all the Blueforces was
also a consequence of the effectiveness of TF1-33 in
attriting the enemy in support of TF4-64. TF1-33 unit
movements were based upon NTC data for the actual
mission. TF1-33 units fired opportunistically.
The Pearson Product Moment Correlation Coefficients
for the independent variable of sleep, only,
demonstrated a positive correlation with the dependent
variable, with the r value of .47 being significant at the
.01 level of significance. Noteworthy however was a
negative r value for the aggression I.V.(-.06),
reflecting an inverse correlation between aggression
and TF4-64 survivability, i.e., a more risk averse BCE
in C3SIM was more successful at lowering the attrition
level of its own units.
The multiple regression analysis for the same
combination of variables demonstrating an r2 of .26,
thus indicating 26% of the observed variability for the
Bluefor survivability index could be accounted for by
the five I.V.s. The student’s t for the slope (B1) for the
independent variable sleep again was significant at the
.01 level of significance for this analysis.
TF4-64 survivability was also strongly influenced by
the communications capability of the BCE; an inverse r
of -.69 demonstrates that the more the BCE
communicated, the less his TF was attrited.
5.3.3.4 Dependent Variable TF4-64 Mission
Completion Time
The outcome for this dependent variable was consistent
with expected results. The movement to contact
mission was scheduled to begin at 0500, and the last
event on the DST was scheduled for 0950, with a
possible EOM between 1015 and 1030. A simulation
cut off time of 1400 was imposed on BCE’s for all of
the simulation runs; if the BCE had not successfully
moved its teams to Objective Ford by that time, it was
apparent it would not meet this mission element.
All five independent variables demonstrated negative
inverse r’s in relation to the EOM dependent variable,
except for Time Pressure, which is supposed to show a
positive r, or linear relation to the I.V. The r and level
of significance is presented in Table 2:
5.3.3.3 Dependent Variable TF4-64 Survivability
Index
Dependent Variable
r
significance
The survivability index of all the Blueforces was only
in part a consequence of the performance and
Sleep
Experience
-.30
-.40
.01
.01
Time Pressure
IQ
Aggression
.21
-.23
-.12
.01
.01
.06
Table 2. Pearson Product Moment r’s and level of
significance for dependent variables and EOM.
The multiple regression analysis for the same
combination of variables demonstrating an r2 of .33,
thus indicating 33% of the observed variability for the
dependent variable “mission completion time” could be
accounted for by the five I.V.s. The student’s t for the
slopes (B1) for the independent variables sleep, time
pressure, and experience were all significant at the .01
level of significance for this analysis.
Forces and Behavioral Representation, Orlando,
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6.0 Next Steps
[6] Flin, R., Salas, E., Strub, M., and Martin, L.
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and Applications, Brookfiled: Ashgate, 1997.
The current phase of this project, Phase III, deals in
part with the addition to a training variable, and its
interactions with the other extant variables in HPM,
ver. 2.0. In addition, HPM ver. 3.0 will be evaluated
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mission scenario.
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Author Biographies
Philip D. Gillis, Ph.D., is a senior research psychologist
and program manager at the U.S. Army Research
Institute, Simulator Systems Research Unit at
STRICOM in Orlando, FL. He currently sits as a
voting member on the Army Modeling and Simulation
Policy and Technology Working Group. Dr. Gillis has
been active in constructive simulation development for
ten years and coded the initial architecture for the
Command, Control, and Communications Simulation.
He has published articles in the areas of training,
simulation, and artificial intelligence for the past 17
years. His research interests are in the areas of: the
utilization and effectiveness of simulation for training
purposes, human performance and cognitive modeling
on the battlefield, and intelligent tutorial systems.
Steven R. Hursh, Ph.D., was the senior research
psychologist for the Army and consultant to the Army
Surgeon General for research in the behavioral
sciences. Upon retirement from the Army, he became
the Program Area Manager for Biomedical Modeling
and Analysis at Science Applications International
Corporation. Dr. Hursh holds a joint appointment as
Professor of Behavioral Biology at the Johns Hopkins
University School of Medicine and directs NIH funded
basic research. Recently, Dr. Hursh developed the first
comprehensive model of the effects of sleep
deprivation, work schedule and circadian rhythms on
performance, and he is extending this model for use by
the Air Force to predict pilot performance for military
and civilian applications. This combined experience
was applied to assist ARI develop the Human
Performance Model described in this paper.