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MOR Journal Abstracts
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| Number 4 |
A
Flow Model Social Network Analysis of the Iranian Government (Robert
S. Renfro, II and Richard F. Deckro)
Social
networks describe the complex interrelations, both formal and informal, between
individuals and groups. Modeling and analysis of social networks has many practical
applications across an array of domains. These include government and military
applications. An example is
considered in detail for the Iranian government decision making process where
relevant individuals and groups, their interactions, and their role in the
decision-making process are explicitly modeled. This analysis illustrates a flow model representation of
social networks. Flow modeling is a
robust and powerful tool for social network analysis.
Mapping social network analysis to a flow model representation resolves
many of the problems found in existing Social Network Analysis techniques. (Pg.
5)
‘Structural
Variance’ or ‘Non-Monotonicity’ Effects In Combat Models: A Review (L.R.
Speight)
‘Structural
variance’ is a phenomenon common to many deterministic combat models, in which
output measures of effectiveness may appear as irregular, non-monotonic
functions of the values of selected input variables. It can, of course,
complicate any decision-making process based on model results. This paper is a
review of this topic: its nature; the experimental evidence that has accrued to
date; and the ‘remedies’ that have been suggested to ameliorate its effects.
The causes of this phenomenon appear to lie mainly in the non-linear features
incorporated in most modern models in order to represent the military
decision-making process and the allocation of resources. It is suggested that
many of these features do in fact feature in historical battles. The
experimental evidence suggests that the incorporation of random features within
the model, which themselves are typically a feature of real combat, can do much
to ameliorate the effects of this phenomenon. The penalty lies in the increased
variability of results, and so this puts a premium on experimental design and
modelling strategies that will help reveal unambiguously the effects of
conditions subject to modelling test. Potential candidate strategies are
therefore discussed. Most of the knowledge concerning this topic is unsystematic
and anecdotal, and so the paper outline steps which would help to put it on a
more rigorous footing. (Pg. 17)
Modeling
Knowledge In Combat Models (Walter Perry)
How
to measure the effects of information on combat outcomes remains unclear.
Attempts have been made, but few actually establish the link.
Doing so is important to the military at this time when it is spending a
considerable amount of its scarce investment capital on information technology.
In this paper, we suggest a model of knowledge that uses information
entropy to measure the commander’s current knowledge.
The metric is also used to explain information superiority and the
concept is applied to the Army’s Combat Sample Generator (COSAGE). (Pg. 43)
Assigning
Nuclear Weapons with ReactiveTabu Search (Christopher A. Cullenbine, Mark A.
Gallagher and James T. Moore)
This article examines solving complex weapon-to-target assignment problems. Preprocessing of options is used to account for three nonlinearities: the selection of nuclear aimpoints that affect multiple targets, bomber and cruise missile routes, and feasible target sets for ballistic missile with Multiple Independent Reentry Vehicles (MIRVs). The formulation considers attack strategies that consist of one or two weapons against each target group. In addition, the formulation ensures selected attack strategies are supported by selecting corresponding delivery systems or routes. The resulting formulation considers hundreds of thousands of attack strategies along with corresponding weapon system delivery options. The tabu search quickly obtains high quality solutions with either prioritized or weighted goals.
Since
tabu search finds solutions in less than half the flight-time of attacking
ballistic missiles, this research creates opportunities for flexible U.S.
nuclear war responses that incorporate the latest military intelligence.
Military planners may find similar applications of tabu search provide
solutions for other complex problems. (Pg. 57)
Fusing Statistical and Neural Classification for Screening
Undergraduate Pilot Training Candidates (Ian A. Young, Kenneth W.
Bauer, Stephen P. Chambal and David M. Pugh)
Every year the Air Force spends millions of dollars to send
personnel through Undergraduate Pilot Training (UPT) and other training
programs. Identifying the most
qualified candidates is a difficult, yet critical task.
This study applies multivariate data analysis techniques, including
discriminant analysis and neural networks, to develop a model to predict
candidate success during UPT. An
entire cradle to grave approach is presented from data screening to model
implementation. The model is
validated to establish its predictive accuracy, capabilities, and limits.
The overall study demonstrates the power of fusing statistical and neural
classification techniques for increasing the power of predictive models. (Pg. 5)
Stochastic Simulation Of A Commander’s Decision Cycle
(Eugene P. Paulo and Sergio
Posadas)
Current constructive simulations used by both the U.S. Army
and Marine Corps to conduct mission analysis rely on deterministic methods to
portray combat decision-making. Both
services have expressed great interest in developing tactical decision-making
within constructive simulations that include uncertainty, chance, and
representation of commander attributes. This
study develops a stochastic representation of a tactical commander’s decision
cycle (SSIM CODE) which is being applied to the Combined Arms Analysis Tool for
the 21st Century (CombatXXI), a high-resolution, analytical combat
simulation being developed by the U.S. Army TRADOC Analysis Center-White Sands
Missile Range (TRAC-WSMR) and the Marine Corps Combat Development Command (MCCDC).
(Pg. 21)
A Genetic Algorithm Applied to Planning Search Paths in
Complicated Environments (David P. Kierstead and Donald R. DelBalzo)
Anti-submarine warfare (ASW) has been a major mission
of the US Navy since the days of World War II.
Search Theory, which originated from the need to design and evaluate ASW
search plans, has been the subject of active research ever since, and ASW
remains one of its primary applications. Search
Theory provides guidance for allocating search effort, but there has been very
little progress in translating that guidance into optimal search paths.
This paper describes an implementation of a genetic algorithm for
optimizing sonar searches (actual ships’ paths) in real acoustic environments.
The software has been used to plan ASW searches in multiple fleet
exercises.
Military Operations Research Society Oral History Project
Interview of Richard I Wiles, FS (Robert Sheldon)
No Abstract. (Pg. 61)
Comparison of Agent Based Distillation Movement Algorithms
(Andrew Gill and Dion Grieger)
Agent based distillations are a new class of low-resolution
simulations, used principally to explore Army operations. The Australian Defence
Science and Technology Organisation is examining these simulations to support
traditional operations analyses. Agent movement within two simulations, called
EINSTein and MANA, is based on an attraction-repulsion weighting system and a
numerical penalty function. Andrew Gill and Dion Grieger analyzed these movement
algorithms to reveal examples of unexpected behaviour and deduce their
underlying causes, suggesting a mismatch between the developer’s concept and
its implementation. An enhancement based on relative distances, a cumulative
functional and simulated annealing was then proposed and tested. (Pg. 5)
Some Experiments with Agent-Based Combat Models (Raymond
Hill, Greg McIntyre, Thomas R. Tighe and Richard K. Bullock)
The DoD has become increasingly reliant on models and their
outputs. Given this reliance on
models and their outputs, one might assume the models are accurate and
faithfully represent the particular system of interest.
Unfortunately, this is not the case, particularly when the systems of
interest involve key elements of combat uncertainty. Agent-based simulation however potentially provides a means
to capture and model the goal-directed behavior of combatants provided one can
first build the simulations and then interpret the output.
This paper presents two agent-based combat simulations, each focused on
examining strategic effects. Both
models and accompanying experiments are described.
The emergent behavior of these agent models is then examined from a
combat analysis perspective with extremely interesting results.
This work breaks new ground in how to use agent-based simulations to gain
insight into warfare dynamics. (Pg. 17)
‘Structural Variance’, Randomisation Strategies and
the Design of Experiments Using Combat Models (L.R. Speight)
This paper is concerned with a feature of many deterministic
combat models, commonly referred to as ‘structural variance’. Due mainly to
the effects of differential tasking of battlefield elements, the output may
appear as an irregular and non-monotonic function of any input variables. This
can make it difficult to generalise from any particular set of results,
complicating any decision-making process based on them. It is generally agreed
that the addition of stochastic features to such models can assist in
alleviating this problem. By building a dedicated experimental combat model,
allied to a very extensive experimental programme, this paper gives provisional
guidance on: the varieties of ‘structural variance’ and their likely
resistance to remedial measures; the choice of parameters for stochastic
treatment and the range over which they should be varied; the effectiveness of
different randomisation strategies; the impact of different sampling
distributions; and ‘variance reduction’ methods which may increase the
precision and information-value of combat modelling experiments. (Pg. 29)
Diffuse Gaussian Multiple-Shot Patterns (Alan Washburn)
A Simulation Study of Military Cargo Clearance Times (Michael F. Cochrane)
Discounting
Effectiveness (Kent D. Wall and James C. Felli)
Defense
planners continually face decision problems in which timeliness as well as
opportunity cost must be considered and carefully weighed to make intelligent
choices. When the timing of
delivered capabilities is important discounting only cost, and not
effectiveness, leads to an inconsistency. This
paper presents a remedy. After
motivating the need to discount effectiveness by considering the temporal nature
of a threat and the operational profiles of alternatives to counter it, we
employ an economic utility function approach to model system effectiveness as a
time series of marginal contributions to an overall measure of effectiveness.
We then provide a method for discounting the marginal effectiveness
series into a single measure and discuss the descriptive and prescriptive value
of our approach. (Pg. 5)
Stochastic
Models of a Cooperative Autonomous UAV Search Problem (Matthew Flint,
Emmanuel Fernandez and Marios Polycarpou)
Directing
Unmanned Aerial Vehicles (UAV's) to behave in an “intelligent” manner
constitutes a very interesting and challenging problem. Such vehicles have
steadily increased in importance in military and other applications, where they
have several advantages over manned aircraft. This paper presents a flexible
model that allows multiple UAV's to cooperatively search for targets in a given
environment or area, using a method to efficiently store dynamic target location
probability distributions and a dynamic programming implementation. Several key
approximations are also given that produce a feasible and effective solution in
the presence of constraints on communication and computational power, as
demonstrated via comprehensive simulation studies.
(Pg. 13)
A
Justification of a Negative Binomial Model For Target Sightings (Brian
McCue)
Using an
example drawn from the analysis of a campaign of aerial search for U-boats
during WW II, this paper presents a heuristic argument that the number of
sightings anticipated in a given period, e.g., the coming month, will be
negative-binomially distributed. This result—normally found by assuming that
the U-boat density is, for some reason, gamma-distributed—is then formally
re-derived from the starting point of a reciprocal, or “Jeffreys,” prior
distribution for the U-boat density and one or more months’ worth of
Poisson-distributed U-boat sightings, and heretofore distinct lines of reasoning
regarding Bayesian updating and the fact that if the density of a Poisson
distribution is itself gamma-distributed then the resulting distribution is the
negative-binomial. (Pg. 33)
An
Examination of Some Artillery Firing Strategies to Maximize Coverage of a
Circular Target Area (Dennis O. Rintjema and William J. Hurley)
This
paper examines whether the algorithms embedded in the fire control computers for
Canadian Forces artillery units are reasonable under certain operational
conditions. Specifically we examine single-volley battery aiming strategies
against circular target areas. The general finding is that these algorithms are
quite robust. However there are
conditions where area coverage is greater if these aiming strategies are
altered. (Pg. 43)
Military
Operations Research Society Oral History Project Interview of John Honig (John Honig and Robert Sheldon)
No Executive Summary (Pg. 53)
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