Scenario Set as a Representative of Possible Futures

  • Peter DobiasEmail author
  • Cheryl Eisler
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)


Because of the inherent disconnect between the length of the procurement cycle and the dynamic time scales of the security environment, future military capability planning presents a unique challenge. The security environment can change very rapidly, and the extent of the changes, and thus the future environment, is potentially unpredictable. In contrast, the procurement cycles responsible for the renewal of the capabilities are typically on the order of decades. The traditional approach to capability-based acquisition consists in the development of a scenario set representing possible futures, and then plan capabilities to address the challenges presented by the scenario set. This approach is based on an implicit assumption that the chosen scenario set is sufficiently representative of all possible futures. Intuitively, if the scenarios are selected somewhat randomly (i.e., there is no significant selection bias that would eliminate certain futures), then a solution that works for the set of planning scenarios, whether in optimization or capability scheduling sense, is more likely to work for any feasible future. In this paper we examine this assumption in a more systematic way and attempt to express likelihood that a solution to a limited set of vignettes is a solution for all possible futures, at least for some special cases.


Planning scenarios Modeling and simulation Scenario selection 


  1. 1.
    Hobson, B.: Obsolescence challenges, Part 3: identifying future capability requirements. Can. Naval Rev. 4(4), 10–14 (2009)Google Scholar
  2. 2.
    Bowden, F.D.J., Pincombe, B., Williams, P.B.: Feasible scenario spaces: a new way of measuring capability impacts. In: 21st International Congress on Modelling and Simulation, Gold Coast, Australia (2015). Accessed 11 Apr 2018
  3. 3.
    Taylor, B., Wood, D.: Guide to capability-based planning, TR-JSA-TP3-2-2004 (2004)Google Scholar
  4. 4.
    Taylor, B.: Toward an Enhanced Capability Based Planning Approach, DRDC-RDDC-2017-D063 (2017)Google Scholar
  5. 5.
    Williams, P.B., Bowden, F.D.J.: Dynamic morphological exploration. In: 22nd National Conference of the Australian Society for Operations Research, Adelaide, Australia (2013). Accessed 11 Apr 2018
  6. 6.
    Chief of Force Development: The Future Security Environment 2013 – 2040, NDID # A-FD-005-001/AF-003 (2014)Google Scholar
  7. 7.
    Chuka, N., Friesen, S.K.: Divining the Force Planning Scenarios: Methodology, Experiences and Lessons from the Canadian Department of National Defence, RTO-MP-SAS-088, RTO System Analysis and Studies Panel Specialists’ Meeting, Sweden (2011)Google Scholar
  8. 8.
    Davis, P.K., Bankes, S.C., Egner, M.: Enhancing Strategic Planning with Massive Scenario Generation Theory and Experiments, RAND Corporation (2007). Accessed 18 Apr 2018
  9. 9.
    Wesolkowski, S., Eisler, C.: Capability-based models for force structure computation and evaluation, NATO Workshop on Integrating Modelling & Simulation in the Defence Acquisition Lifecycle and Military Training Curriculum, STO-MP-MSG-126 (2015)Google Scholar
  10. 10.
    Taleb, N.N., The Black Swan: The Impact of Highly Improbable, Random House Trade Paperbacks, 2 edn. (2010)Google Scholar
  11. 11.
    Yao, W.-M., et al.: (Particle Data Group): Statistics. J. Phys. G33(1) (2006). Accessed 16 Apr 2018
  12. 12.
    Kotz, S., van Dorp, J.R.: Beyond Beta: Other Continuous Families of Distributions with Bounded Support and Applications, pp. 1–28. World Scientific Publishing Co. Pte. Ltd., Singapore (2004). Accessed 16 Apr 2018CrossRefGoogle Scholar
  13. 13.
    Dobias, P., Eisler, C.: Modeling a naval force protection scenario in MANA. Oper. Res. Manag. Sci. Lett. 1(1), 2–7 (2017)Google Scholar
  14. 14.
    Lauren, M.K., Stephen, R.T.: Map-aware non-uniform automata (MANA) – a New Zealand approach to scenario modelling. J. Battlefield Technol. 5, 1–13 (2002)Google Scholar
  15. 15.
    McIntosh, G.C., Galligan, D.P., Anderson, M.A., Lauren, M.K.: MANA (Map Aware Non-Uniform Automata) Version 4 User Manual, DTA TN 2007/3 NR 1465 (2007)Google Scholar
  16. 16.
    Dobias, P., Bouayed, Z., Woodill, G., Bassindale, S.: Optimal number of non-lethal launchers study - Nickel Abeyance II, DRDC CORA TR 2006-18 (2016)Google Scholar
  17. 17.
    Shapiro, S.S., Wilk, M.B.: Analysis of variance test for normality. Biometrika 52(3), 591–611 (1965). Accessed 16 Apr 2018MathSciNetCrossRefGoogle Scholar
  18. 18.
    Triefenbach, F.: Design of Experiments: The D-Optimal Approach and Its Implementation as a Computer Algorithm, Thesis, Umea University (2008). Accessed 20 Apr 2018

Copyright information

© Crown 2018

Authors and Affiliations

  1. 1.DRDC CORAOttawaCanada

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