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Validation Challenges in Battle Model Development

This article explores validation issues in developing battle models and the importance of ensuring credibility in military decision-making processes. It delves into the validation activities, methodologies, and findings, emphasizing the iterative and collaborative nature required for effective validation. The text highlights the significance of validation in study processes and the impact of data adjustment and extraneous effects on model outcomes.

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Validation Challenges in Battle Model Development

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  1. Issues in the Validation of Battle Models Presented at 19 ISMOR David Frankis ‘The Barbican’, East Street, Farnham, Surrey GU9 7TB 01252 738500 www.Advantage-Business.co.uk August 2002

  2. Acknowledgements • Dstl • This work was carried out under contract to Dstl by Advantage Technical Consulting • RMCS

  3. Today’s Presentation • Why validation • CLARION • What was done • Issues raised • Questions

  4. Why Validation? • UK Government decision-making must pass the test of independent scrutiny • Making a logical case based on credible information is key to this • OA claims to be able to support this by quantifying key aspects, objectively • The validity of this quantification is therefore crucial • A new version of CLARION required an update to its validation status

  5. CLARION General • A Land-Air campaign model • Object Oriented C++ implementation • Functionality is based on the concept of missions: • Each entity (e.g. a division) has a mission • Subordinate units are tasked with missions based on the superior’s mission • Defined set of mission types • Generally Brigade level and above

  6. CLARION Functionality • Movement and Attrition • Command • Communications • Sensing • Close combat, Arty, Recce, Helo • Some Air aspects • CBW • EW • No logistics in version examined (V3.0)

  7. What is Validation? • The model is realistic? • The representation of internal processes is correct? • Known effects are covered? • Sufficient detail is included? • The results are plausible? • Conclusions drawn are substantiated?

  8. Other Validation Issues • Scope of validation • Model only, or ancillary tools • Status of any comparison • Danger of mutually-supporting invalid models

  9. Validation Activities • Prioritisation of Requirement • Selection of Comparison Method • Generation of Scenario • Comparison Activity • Analysis and Reporting

  10. Prioritisation • CLARION has wide scope of functions and contexts • Key stakeholders were consulted for their views • Formal method used to prioritise • Main outcome: focus on mainstream uses, not functions less used (Air, EW, CBW)

  11. Selection of method • Possible comparison approaches • Historical Analysis • Trials and Exercises • Other models • Military (and analytical) Judgement • Wargame • These are not mutually exclusive • Wargame was selected as best approach at a workshop • Dstl staff selected most appropriate (commercial) game

  12. Scenario Generation • Workshop held with scientific and military analysts • Fictitious scenario overlaid on a map • Outline scheme of manoeuvre developed

  13. Comparison and Analysis • Scenario entered in CLARION • Adjusted with military input • Then into wargame and played • Further CLARION adjustment to reflect military intentions in wargame • Comparison of outputs • Some practical difficulties arising from wargame limitations

  14. Findings • Validation as part of study process • Data adjustment • User interface issues • Extraneous effects

  15. Validation as Part of Study Process • Ideally, the data and the way the model is used requires (re-)validation on each study • Validation is an iterative process • How much? • What if the iteration doesn’t converge? • In exceptional cases, could have independent teams of analysts

  16. validate Wargame Process Elements Selection of Scenario Scheme of manoeuvre CLARION input Interpret outputs Exercise in CLARION Study conclusions

  17. User Interface Issues • If the user interface is unfriendly or unintuitive, analysts will lack confidence • Longer learning curve for new analysts and scrutineers • Resulting loss of confidence in results through uncertainty and reduced effective validation effort

  18. Data Adjustment • In order to capture effects not explicit in the model, analysts adjust the input data • Acceptable as long as analysts doing the adjustment are doing the reporting • Legacy effects • Unpredictable interactions when done more than once

  19. Extraneous Effects • CLARION scenarios are acknowledged to develop much more quickly than reality • As long as all processes (movement, attrition, communication) are accelerated the same for both sides, does not matter for many study purposes • BUT study results are easy to rubbish because they seem to have low credibility

  20. Conclusions: General • Model unlikely to be the limiting factor on confidence in study results • The use of a good model cannot compensate for a poor process or the use of insufficiently skilled analysts • Where studies focus on scenarios, they, and their data, should be validated for that study • Consider use of a wargame tool to support the development of a scheme of manoeuvre in campaign studies

  21. Conclusions: Process Elements • Treat input data collection and refinement as integral to the study, not a necessary evil • Iterate the review of input data, output results, and the use of adjunct tools to converge on a ‘valid enough’ solution • Ensure the military plan remains valid when conducting sensitivity excursions • For major studies, consider some parallel working • Use different experts at different stages to ensure freshness of perspective

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