Verification &amp; validation in military simulations

Abstract:In agent based models, the agents are usually platforms (individual soldiers, tanks, helicopters, etc.), not military units. In the Sandis software, the agents can be platoon size units. As there are about 30 soldiers in a platoon, there is a need for strength distribution in simulations. The contribution of this paper is a conceptual model of the platoon level agent, the needed mathematical models and concepts, and references earlier studies of how simulations have been conducted in a data farming environment with platoon/squad size unit agents with strength distribution.

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“…Validation, on the other hand, means that the model and reality are close enough for the purpose of the model [20].…”

Section: Verification and Validation Of The Modelmentioning

confidence: 99%

Tactical Size Unit as Distribution in a Data Farming Environment

Lappi

Åkesson

2016

Axioms

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“…These included early taxonomies such as those from (Hartley, 1997) Since the MIL-STD-3022 is so recently approved as a standard (January, 2008), no ontologies have yet been developed to describe VV&A information in accordance with that standard.…”

Section: For What Types Of Questions the Information In The Ontology mentioning

confidence: 99%

Toward a Verification, Validation, and Accreditation (VV&A) Ontology

Blais¹

2008

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, SPONSORING /MONITORING AGENCY NAME(S) AND ADDRESS(ES)Space and The Department of Defense (DoD) Modeling and Simulation Steering Committee (M&S SC) is sponsoring several Acquisition Modeling and Simulation (M&S) Projects. One of those projects is titled, "Standardized Documentation for Verification, Validation, and Accreditation (VV&A)." Previous efforts defined standardized content and format requirements (i.e., templates) for four core VV&A documents, now codified in a new Military Standard (MIL-STD-3022) and associated Data Item Descriptions. A tool is under development to automate those templates to assist developers in creating standardized VV&A information across the DoD and Military Departments. In support of this effort, the Naval Postgraduate School was tasked to specify data structures and content using the Extensible Markup Language (XML) for metadata and content of VV&A documentation. Based on the initial work, NPS was further tasked to design and develop an ontological foundation for formal description of VV&A information. This document describes preliminary work toward development of an ontology for VV&A that will facilitate future search, discovery, and processing of VV&A information to better meet user needs. of this effort, the Naval Postgraduate School was tasked to specify data structures and content using the Extensible Markup Language (XML) for metadata and content of VV&A documentation. Based on the initial work, NPS was further tasked to design and develop an ontological foundation for formal description of VV&A information. This document describes preliminary work toward development of an ontology for VV&A that will facilitate future search, discovery, and processing of VV&A information to better meet user needs. SUBJECT TERMS THIS PAGE INTENTIONALLY LEFT BLANK

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“…"A model may be valid for one set of experimental conditions and invalid in another" (Sargent, 2003, p. 37). Model validity, then, is a relative concept involving tradeoffs and a balancing of costs and benefits (see note 9; see also Harmon, Gross, & Youngblood, 1999;Hartley, 1997;Logan & Nitta, 2003;MacKenzie, Schulmeyer, & Yilmaz, 2002;Page, Canova, & Tufarolo, 1997). For Balci and Sargent (1981), the two risks inherent in classical statistical hypothesis testing vis-à-vis computer simulation validation-rejecting the model when in fact it is valid and not rejecting the model when in fact it is invalid-"can be decreased at the expense of increasing the sample sizes of observations" (p. 190).…”

Section: Notesmentioning

confidence: 99%

Philosophical foundations of computer simulation validation

Klein

Herskovitz

2005

Simulation & Gaming

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Innovations in information technology (IT) have been responsible for the rapid growth of computer simulation, which in turn has encouraged IT innovation. However, there is a striking contrast between the technological sophistication that drives and is driven by computer simulation and the rudimentary, underdeveloped state of theory underpinning the simulation endeavor. This article attempts to provide computer simulation with a theoretical framework by the application of philosophy of science to simulation validation. The authors discuss the key elements of Popper's theory of falsification and explore how simulation validation is in fact anchored to Popper's theory. Moreover, this article suggests that a Popperian perspective encourages the review of a validated model's continuing validity, thereby potentially leading to the construction of a new, improved model. Finally, we contrast Popper's falsificationist philosophy of science withQuine's philosophical system of holism and early-period Putnam's scientific realism, both of which discourage the building of better models.

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Verification & validation in military simulations

Cited by 17 publications

References 15 publications

Tactical Size Unit as Distribution in a Data Farming Environment

Tactical Size Unit as Distribution in a Data Farming Environment

Toward a Verification, Validation, and Accreditation (VV&A) Ontology

Philosophical foundations of computer simulation validation

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