Attrition formulas for deterministic models of large-scale combat

Anderson, Lowell Bruce

doi:10.1002/1520-6750(199504)42:3<345::aid-nav3220420304>3.0.co;2-c

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…19 Lanchester and Osipov independently developed deterministic attrition formulas early in the twentieth century that focused on relative force sizes and their corresponding effectiveness and firepower. 17,20 These attrition formulas have been discussed extensively by a diversity of authors including Hartley and Helmbold, 21 Anderson, 22 and Ancker Jr and Gafarian. 23 Hartley and Helmbold 21 considered Lanchester's square law and its effect on attrition.…”

Section: Attritionmentioning

confidence: 99%

“…LFORRAT is the log function of the force ratio, whereas LHELMRAT is the log function of the fraction describing the change in the two forces respective force ratios. Anderson 22 has provided a taxonomy for attrition equations based on two characteristics; the level of coordination and the type of fire being used (point or area fire). Ancker and Gafarian 23 analyzed the validity of the underlying assumptions in Lanchester attrition rates and found that they did not apply for large numbers of combatants, either in the deterministic or stochastic Lanchester forms.…”

Section: Attritionmentioning

confidence: 99%

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Attrition rates and maneuver in agent-based simulation models

Ormrod

Turnbull

2017

Journal of Defense Modeling & Simulation

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Agent-based military combat simulation models make use of a variety of methods to determine individual engagement and casualty effects. The agent-based approach differs to the deterministic approach of Lanchester and Osipov, whose combat attrition calculations pre-dated computationally intensive physics-based simulation models. Deterministic approaches to analyze force attrition rates and the outcomes of combat have been noted by many authors to lack realism and to ignore many fundamental aspects of modern combat. However, there is little available evidence to suggest that modern simulations provide a more realistic attrition rate, despite their complexity. This paper seeks to explain the importance of understanding the implications of the tactical aspects of multiple simulation packages and to ensure these are considered within the underlying combat model. The principle factor this paper will discuss is attrition and its relationship to maneuver. This work analyzes the macro-attrition rates emergent within a number of unclassified agent-based simulations using the Helmbold space. The results demonstrate insufficient sensitivity to the influence of maneuver and the inconsistent influence of force ratios on combat outcomes. Simulation supported by well-documented testing and validation is necessary for analysts to support the military training imperative and provide realistic experimental engagement results.

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Section: Attritionmentioning

confidence: 99%

Section: Attritionmentioning

confidence: 99%

Attrition rates and maneuver in agent-based simulation models

Ormrod

Turnbull

2017

Journal of Defense Modeling & Simulation

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“…If R c (0) ú 0 but i ca Å i aa we also have a new and somewhat different square law. Anderson [1] has also considered the influence of decoys or false targets in a more elaborate setting. His formulation differs from ours in that discrimination capability is not represented by functions i aa , i ca (and their respective complements).…”

Section: Blue Possesses Discriminatory Powersmentioning

confidence: 99%

Attrition modeling in the presence of decoys: An operations‐other‐than‐war motivation

Gaver

Jacobs

1997

Naval Research Logistics

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Under various operational conditions, in particular in operations other than war (OOTW) or peacekeeping, an intervening force, here Blue, must occasionally engage in attrition warfare with an opposing force, here Red, that is intermingled with noncombatants. Desirably, Red armed actives are targeted, and not the unarmed noncombatants. This article describes some simple Lanchesterian attrition models that reflect a certain capacity of Blue to discriminate noncombatants from armed and active Red opponents. An explicit extension of the Lanchester square law results: Blue's abstinence concerning the indiscriminate shooting of civilians mixed with Red's is essentially reflected in a lower Blue rate of fire and less advantageous exchange rate. The model applies to other situations involving decoys, and reflects the value of a discrimination capability.

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“…Such alternative functions could be obtained by taking partial derivatives with respect to the number of shooting weapons (of type i on side s) of an attrition equation that calculates the number of target weapons (of type j on side s') killed as a function of W, E , A , P , and then evaluating those partial derivatives at the force sizes in question. Using different attrition equations would yield different definitions for K ; several different attrition equations that could be considered are presented in Anderson [4,51. Future research could examine the impact of alternative definitions for K . See Anderson [2] for a discussion of some initial work in this area.…”

Section: Notationmentioning

confidence: 99%

On weapons scores and force strengths

Anderson¹,

Miercort²

1995

Naval Research Logistics

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Quantitative comparisons of the combat forces on two opposing sides are virtually always formed as follows. First, all of the resources on each of the two sides are grouped into a set of categories. Each of the resources in each category is assigned a (nonnegative) value or score, where these scores are constant within categories, can vary across categories, and can be functions of the numbers and effectiveness parameters of the resources on both sides. These categories need not be the same for the two sides. Force strengths are then formed for each side by summing, over all of its categories, the product of the number of resources in each category times the score given to the resources in that category. Quantitative comparisons of the two forces are then calculated using (real‐valued) functions of these strengths and, perhaps, of exogenous input parameters. Thus, such force comparisons depend on the number and effectiveness of the weapons in the forces only through these force strengths. Accordingly, if this type of quantitative comparison of the forces on two opposing sides is desired, then it is reasonable to consider the questions of how to calculate scores for the resources of each type, and of how to combine the resulting scores to form force strengths that can be used to calculate measures of the relative effectiveness of the forces involved. This article addresses these questions. © 1995 John Wiley & Sons, Inc.

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Attrition formulas for deterministic models of large-scale combat

Cited by 6 publications

References 5 publications

Attrition rates and maneuver in agent-based simulation models

Attrition rates and maneuver in agent-based simulation models

Attrition modeling in the presence of decoys: An operations‐other‐than‐war motivation

On weapons scores and force strengths

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