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Tensor-Centric Warfare II: Entropic Uncertainty Modeling

DOI: 10.4236/ica.2018.92003, PP. 30-51

Keywords: Tensor-Centric Warfare, Non-Equilibrium Entropy, Uncertainty and Symmetry of Warfare, Lie-Derivative Machinery

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Abstract:

In the first paper of the tensor-centric warfare (TCW) series [1], we proposed a tensor model of combat generalizing earlier Lanchester-type systems with a particular emphasis on contemporary military thinking, including the distributed C4ISR system (Command, Control, Communications, Computing, Intelligence, Surveillance and Reconnaissance). In the present paper, we extend this initial tensor combat model with entropic Lie-derivative machinery in order to capture some aspects of this deep uncertainty, while, in the process, formalizing into our model military notion of symmetry and asymmetry in warfare as a commutator, also known as a Lie bracket. In doing so, we have sought to shift the question from the prediction of outcomes of combat, upon which previous combat models such as the Lanchester-type equations have been typically constructed, towards determining the uncertainty outcomes, using a rigorous analytical basis.

References

[1]  Ivancevic, V., Pourbeik, P. and Reid, D. Tensor-Centric Warfare I: Tensor Lanchester Equations. ICA.
[2]  Alberts, D., Garstka, J. and Stein, F. (1999) Network Centric Warfare: Developing and Leveraging Information Superiority. CCRP.
[3]  Reid, D.J., Goodman, G., Johnson, W. and Giffin, R.E. (2005) All That Glisters: Is Network-Centric Warfare Really Scientific? Defense and Security Analysis, 21, 335-367.
https://doi.org/10.1080/1475179052000345403
[4]  Reid, D.J. (2017) An Autonomy Interrogative. In: Abbass, H.A., Scholz, J. and Reid, D.J., Eds., Foundations of Trusted Autonomy, Springer, New York, Chapter 21, 365-391.
[5]  Rittel, H. (1973) Webber: Dilemmas in a General Theory of Planning. Policy Sciences, 4, 155-169.
[6]  Jomini, A.H. (2007) Baron de: The Art of War. Dover Edition, Dover Publications, New York.
[7]  Von Clausewitz, C. (1832) On War. Princeton Univ. Press, Princeton.
[8]  Lanchester, F.W. (1916) Aircraft in Warfare: The Dawn of the Fourth Arm. Constable, London.
[9]  Lanchester, F.W. (2000) Mathematics in Warfare. In: Newman, J., Ed., The World of Mathematics, Vol. 4, Simon and Schuster, New York, Dover, 2138-2157.
[10]  Osipov, M. (1995) The Influence of the Numerical Strength of Engaged Forces on Their Casualties. In: Military Operations Research Society, Eds., Warfare Modeling, Helmbold, R.L. and Rehm, A.S. Trans., John Wiley & Sons, Hoboken, 290-343.
[11]  McLemore, C., Gaver, D. and Jacobs, P. (2016) Model for Geographically Distributed Combat Interactions of Swarming Naval and Air Forces. Naval Research Logistics, 63, 562-576.
https://doi.org/10.1002/nav.21720
[12]  Ivancevic, V. and Ivancevic, T. (2006) Geometrical Dynamics of Complex Systems. Springer, Dordrecht.
https://doi.org/10.1007/1-4020-4545-X
[13]  Ivancevic, V. and Ivancevic, T. (2008) Complex Nonlinearity: Chaos, Phase Transitions, Topology Change and Path Integrals. Springer, Berlin.
[14]  Ivancevic, V. and Reid, D. (2015) Complexity and Control: Towards a Rigorous Behavioral Theory of Complex Dynamical Systems. World Scientific, Singapore.
[15]  Ivancevic, V., Reid, D. and Pilling, M. (2017) Mathematics of Autonomy: Mathematical Methods for Cyber-Physical-Cognitive Systems. World Scientific, Singapore.
https://doi.org/10.1142/10716
[16]  Wikipedia (2017) Battlespace.
[17]  Scholz, J.B., Calbert, G.J. and Smith, G.A. (2011) Unravelling Bueno De Mesquita’s Group Decision Model. Journal of Theoretical Politics, 23, 510-531.
https://doi.org/10.1177/0951629811418142
[18]  Ivancevic, V. and Ivancevic, T. (2007) Applied Differential Geometry: A Modern Introduction. World Scientific, Singapore.
https://doi.org/10.1142/6420
[19]  Ivancevic, V. and Ivancevic, T. (2007) Neuro-Fuzzy Associative Machinery for Comprehensive Brain and Cognition Modelling. Springer, Berlin.
https://doi.org/10.1007/978-3-540-48396-0
[20]  xTensor (2015) Fast Abstract Tensor Computer Algebra.
http://xact.es/xTensor/
[21]  Woszczyna, A., et al. (2014) The Symbolic Tensor Analysis Package, with Tools for General Relativity.
http://library.wolfram.com/infocenter/MathSource/8848/
[22]  Ivancevic, V., Reid, D., Boswell, S. and Pourbeik, P. Evolution of Combat Models: From Linear Lanchester Equations to Nonlinear Schrodinger Equations.
[23]  Penrose, R. (2004) The Road to Reality. Jonathan Cape, London.
[24]  Nicolis, G. and Prigogine, I. (1977) Self-Organization in Nonequilibrium Systems: From Dissipative Structures to Order through Fluctuations. Wiley, Hoboken.
[25]  Ivancevic, V., Reid, D. and Scholz, J. (2014) Action-Amplitude Approach to Controlled Entropic Self-Organization. Entropy, 16, 2699-2712.
https://doi.org/10.3390/e16052699
[26]  Ruppeiner, G. (1995) Riemannian Geometry in Thermodynamic Fluctuation Theory. Reviews of Modern Physics, 67, 605-659.
https://doi.org/10.1103/RevModPhys.67.605
[27]  EurekAlert! (2017) Blue Brain Team Discovers a Multi-Dimensional Universe in Brain Networks.
[28]  Reimann, M., et al. (2017) Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function. Frontiers in Computational Neuroscience, 11, 48.
https://doi.org/10.3389/fncom.2017.00048
[29]  Bassett, D. and Sporns, O. (2017) Network Neuroscience. Nature Neuroscience, 20, 353-364.
https://doi.org/10.1038/nn.4502
[30]  Bauer, U., Kerber, M., Reininghaus, J. and Wagner, H. (2017) PHAT—Persistent Homology Algorithms Toolbox. Journal of Symbolic Computation, 78, 76-90.
https://doi.org/10.1016/j.jsc.2016.03.008
[31]  Hackage (2017) Ad: Automatic Differentiation.
https://hackage.haskell.org/package/ad
[32]  DiffSharp (2017) Differentiable Functional Programming.
http://diffsharp.github.io/DiffSharp/

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