Abstract
There is a need to represent military command and control in closed-form simulation models of conflict, in order to compare investment in such capability with alternative defence investments. This paper considers such representation of military command and control in the context of embodied cognitive science. This means that we represent such processes in terms of both decision-making and resultant behaviour. Previous work leads to the view that such a representation can be captured by a combination of deliberate (top down) planning and rapid (bottom up) planning. We have developed an approach on these lines as a way of representing human decision-making and behaviour in conflict. Here we show, by comparing simulation model results with real conflict situations, that our approach yields emergent force behaviour which is valid and representative. This thus increases our confidence that our representation of command and control in such simulation models is sufficient for our requirements.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Moffat J (2000). Representing the command and control process in simulation models of conflict. J Opl Res Soc 51: 431–439.
Moffat J (2002). Command and Control in the Information Age; Representing Its Impact, The Stationery Office: London, UK.
Pfeifer R and Scheier C (1999). Understanding Intelligence, The MIT Press: Cambridge, MA, USA.
Muller JP (1996). The Design of Intelligent Agents, Springer-Verlag: Berlin, Heidelberg, New York.
Newell A and Simon HA (1976). Computer science as empirical enquiry: symbols and search. Commun of the ACM 19(3): 113–126.
Mason CR and Moffat J (2001). An agent architecture for implementing command and control in military simulations. In: Proceedings of 2001 Winter Simulation Conference IEEE, Piscataway, NJ.
Bratman ME, Israel DJ and Pollack ME (1987). Towards an architecture for resource bounded agents, Technical Report CSLI-87-104, Centre for the Studyof Language and Information, SRI and Stanford University, USA.
Klein GA (1989). Recognition primed decisions. Adv Man Mach Sys. Res 5: 47–92.
McFarland D and Bosser M (1993). Intelligent Behaviour in Animals and Robots, The MIT Press: Cambridge, MA, USA.
Pidd M (2001). Computer Simulation in Management Science, 4th Ed. John Wiley and Sons: Chichester, UK.
Popper KR (1968). The Logic of Scientific Discovery, Fourth Impression (Revised). Hutchinson: London, UK.
Balci O (1994). Validation, verification and testing techniques through the life cycle of a simulation study. In: Balci O (ed). Annals of Operations Research, Vol. 23: Simulation and Modelling, J.C. Balzer, Basel.
Balci O (1997). Principles of simulation model validation, verification and testing. Trans Soc Comput Simulation Int 14: 13–12.
Robinson SW (1996). Service quality management in the process of delivering a simulation study. Paper Presented to the 14th Triennial Conference of the International Federation of OR Societies, 8–12 July, Vancouver, BC.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Copyright information
© 2015 British Crown copyright
About this chapter
Cite this chapter
Moffat, J., Campbell, I., Glover, P. (2015). Validation of the Mission-based Approach to Representing Command and Control in Simulation Models of Conflict. In: Forder, R.A. (eds) OR, Defence and Security. The OR Essentials series. Palgrave Macmillan, London. https://doi.org/10.1057/9781137454072_3
Download citation
DOI: https://doi.org/10.1057/9781137454072_3
Publisher Name: Palgrave Macmillan, London
Print ISBN: 978-1-349-49785-0
Online ISBN: 978-1-137-45407-2
eBook Packages: Palgrave Business & Management CollectionBusiness and Management (R0)