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Agent Based Modelling to demonstrate self-synchronisation at the Battle of Trafalgar: a demonstration of computational methods for teaching Command and Control�Topic 5 (Modelling and simulation)

Dr. Matt Offord

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Purpose of this presentation

  • Agent Based Models to teach Edge C2
  • Mission Command in the Information Age
  • Battle of Trafalgar

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Trafalgar in the 21st Century

  • Why is transformation in C2 ‘the road less travelled’?
  • Why are there so few examples of self-synchronization?
    • Command Intent
    • Trust
    • Shared Situational Awareness
    • Competence
  • Increased complexity in the Information Age

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Teaching Edge C2

  • Pedagogy
  • Simulation
    • Wargames
    • Tabletop Tactics
    • Simulators
    • ABM?

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Agent Based Models

  • Agents
  • Levels
  • Complexity

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Rules

Red (Control Free)

Blue

  • Select target
  • Close target (point move forward)
  • Fire when in range
  • Move forward
  • Fire when in range

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Demonstrations

  • Self-synchronization
    • Emergence
    • Self organisation
  • Self organizing criticality
    • Thresholds

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Demonstration of self-synchronization

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Demonstration of self-organizing criticality

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Features

Settings

Red

Blue

Health

4

5

Number

27

33

Accuracy

75

50

Speed

1.5

1

  • Bounded realism
  • Control free
  • Threshold behaviours
  • Customisation

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Teaching with the model

  • Acquisition
  • Demonstration
  • Experimentation

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Using the open resource

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Conclusion���matt.offord@glasgow.ac.uk

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References

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  2. Alberts, D. S., and Richard E. Hayes. Understanding command and control. Assistant secretary of defense (C3I/Command Control Research Program) Washington DC, 2006
  3. Alberts, D. S. The Agility Advantage : A Survival Guide for Complex Enterprises and Endeavors. 2011. 50.
  4. Bosio, Nick. "Moulding war's thinking: Using wargaming to broaden military minds." Australian Army Journal 16, no. 2 (2020): 25-48.
  5. Eisenberg, D. A., Alderson, D. L., Kitsak, M., Ganin, A., & Linkov, I. Network Foundation for Command and Control (C2) Systems: Literature Review. IEEE Access, 6, 68782–68794, 2018. https://doi.org/10.1109/ACCESS.2018.2873328
  6. Frigg, R. Self-organised criticality - What it is and what it isn’t. Studies in History and Philosophy of Science Part C :Studies in History and Philosophy of Biological and Biomedical Sciences, 34(3), 613–632, 2003: https://doi.org/10.1016/S0039-3681(03)00046-3
  7. Gilbert, G. Nigel, and Klaus G. Troitzsch. 1999. Simulation for the social scientist. Buckingham: Open University Press. 208
  8. Gu, X., and K. L. Blackmore. "A systematic review of agent-based modelling and simulation applications in the higher education domain." Higher Education Research & Development 34, no. 5 (2015): 883-898.
  9. Hofstede, Gert Jan, Léon De Caluwé, and Vincent Peters. "Why simulation games work-in search of the active substance: A synthesis." Simulation & Gaming 41, no. 6 (2010): 824-843.
  10. Moffat, James. Adapting modeling & simulation for Network enabled operations. OFFICE OF THE ASSISTANT SECRETARY OF DEFENSE FOR NETWORKS AND INFORMATION INTEGRATION WASHINGTON DC, 2011
  11. Offord, Matt, Roger Gill, and Jeremy Kendal. "The effects of prestige on collective performance and information flow in a strictly hierarchical institution." Palgrave Communications 5, no. 1 (2019): 1-11.
  12. Wilensky, U. NetLogo. (1999) https://ccl.northwestern.edu/netlogo/index.shtml
  13. Wilensky, Uri, and William Rand. An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. The MIT Press, 2015. http://www.jstor.org/stable/j.ctt17kk851. 12