Modeling Knowledge In Combat Models

Information has a far-reaching effect in military operations. What is unclear is just how to measure that effect. Some attempts have been made at quantifying the effects of information through the development of measures of performance, but few actually measure the effects of improved (or degraded)...

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Bibliographic Details
Published in:Military operations research (Alexandria, Va.) Va.), 2003-01, Vol.8 (1), p.43-55
Main Author: Perry, Walter
Format: Article
Language:English
Subjects:
Artillery
Information entropy
Knowledge representation
Military operations
Modeling
Operations research
Sensors
Warfare
Weapon systems
Weapons
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Summary:Information has a far-reaching effect in military operations. What is unclear is just how to measure that effect. Some attempts have been made at quantifying the effects of information through the development of measures of performance, but few actually measure the effects of improved (or degraded) C4ISR systems on outcomes of combat. Developing links between C4ISR systems and combat outcomes is important to the military, particularly at a time when it is spending a considerable amount of its scarce investment capital on establishing information age links across its forces. Analyzing the impact of new operating concepts, such as Network Centric Warfare, requires information-age analytic tools to help it make the best choices possible. Chief among these are measures of effectiveness (MOEs) that reflect the effects of knowledge, produced through C4ISR systems, on military outcomes. In this paper, we suggest a probability model of knowledge that uses information entropy to measure the amount of uncertainty in the commander's current knowledge of the battlespace. The knowledge metric developed is also used to explain relative information superiority. The concept is then applied to the Army's Combat Sample Generator (COSAGE), a legacy model used to support Army force structure analyses.
ISSN:1082-5983
2163-2758
DOI:10.5711/morj.8.1.43