1. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Babes in the Woods: How Naïve Analysts Clashed with Trained Killers to the Mutual Benefit of All Fred Cameron Operational Research Advisor to the Director General Land Capability Development

2. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Defence Research and Development Canada Kingston, Ontario Directorate of Land Synthetic Environments

3. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Land Capability Development Operational Research Team Mr Fred Cameron (Team Leader) Mr Roger Roy Ms Eugenia (Jenny) Kalantzis Mr Ian Chapman Mr François Cazzolato Maj Bruce Chapman (to join July 2006) The Army Experimentation Centre –Part of the Directorate of Land Synthetic Environments (Army’s home for Computer-based Wargames, Models, and Simulations for Training and Experimentation) Strategic Analyst – Mr Peter Gizewski Science Advisor – Mr Regan Reshke

4. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Deciding Which Method to Use – A Meta-Decision A meta-model of the meta-decision process An analysis: –Description What happened? –Ascription Were there causes and effects? –Prescription What should we do in future? –Proscription What should we avoid in future?

5. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Cameron’s Rubbish Bin Model of Decision Making Born of many years of observation Employed for organizations that are “organized anarchies” Basis for workshop on “Ambiguity and Command” in 1986 Used provocatively: –Is it really that bad? Sources: Cohen, March, and Olsen (1972), March and Weissenger-Baylon (1986), and March (1994) March’s Garbage Can Model

6. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle March’s Garbage Can Model of Decision Making Complex interactions between: Actors (decision makers) Problems Choice opportunities Potential solutions

7. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle March’s Garbage Can Model of Decision Making Some ways to “improve” garbage-can decision making –Increase the heat and pressure Force more frequent interactions –Put more garbage in the can Add more potential solutions Add more problems, more actors, more decision opportunities too? –Make actors “stickier” Hope that they will carry problems or potential solutions with them long enough to get a better fit

8. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Description “Deciding Which Method to Use” –OR analysts make great critics of the decision- making of others –But, turn the lens into a mirror The Analyst’s Toolbox… Our Experience Canadian Army Modelling and Simulation Specific Examples and Lessons

9. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle The Operational Research Tool Box Some Examples Structured brain storming Scenarios Analysis of historical data Options analysis, Decision analysis Group ranking Seminar war games Analytical tools – mathematical analysis Computer-based war games (JANUS, OneSAF) Trials, exercises, and evaluations – Source: Future Army Development Plan, 1998 More: time resources credibility Less: time resources credibility Soft OR/OA Methods Hard

10. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Our Early Meta-decision Rules for “Deciding Which Method to Use” If time and resources are short, and credibility is not an issue, then use soft OR methods If credibility is paramount, and time and resources are available, then use hard OR methods Our experience supports: “hard and soft methods can be used together at appropriate stages of a study (usually soft for problem formulation, hard for resolution)”

11. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Simulation Types People Equipment Real Live Real Simulated VirtualConstructive Simulated

12. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Goal Synthetic Environment Live Constructive Virtual Joint War Game Simulators ArmyNavyAir Force Operational ArmyNavyAir Force Tactical Link C2 Systems Real World Link

13. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Constructive Simulations Command and Staff Trainer UK’s ABACUS

14. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Constructive Simulations

15. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Lesson 1: AE-7B * Armed Griffon Helicopter Aim. To provide insights into differences in effectiveness and survivability between an armed and an unarmed helicopter Purpose. To validate the incorporation of aerial firepower requirements in the CH-146 Griffon Helicopter mid-life upgrade Simulation Structure. Constructive and virtual computer- based simulation supported by OneSAF Testbed Baseline (OTB), with virtual workstations for Griffon helicopters and Pointer-type Unmanned Air Vehicles (UAV) * AE = Army Experiment

16. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Lesson 1: AE-7B Armed Griffon Helicopter Constructive Virtual Tactical Griffon DIS OTBSAF Pointer DIS

17. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle AE-7B – The Textbook Case Jun 02 Jul 02 Aug 02Sep 02 Oct 02 Nov 02 Jan 03 Feb 03 Mar 03 Apr 03 Dec 02 Define Develop Conduct Analyze Report Weak Synthetic Environment Specification Late Terrain Generation Late Griffon Simulation Delivery Late Scenario Build Strong Sponsor Involvement Excellent Participant Base Automated Small Sample Statistics Corrective Action Implemented Synthetic Environment Statement of Requirement

18. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Lesson 2: LOE* 0301 – TUAV in Controlled Airspace Aim. To investigate possible reduced airspace restrictions for UAVs if Air Traffic Controllers have better situational awareness Objective. To identify any differences in situational awareness enabled by various improvements to ATC situational awareness (in the vicinity of Kabul) Simulation Structure. Constructive and virtual computer- based simulation supported by OneSAF Testbed Baseline (OTB), with virtual workstations for ATC Tower, Air Defence and ATC radars, and UAV * LOE = Limited Objective Experiment

19. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Lesson 2: LOE 0301 – TUAV in Controlled Airspace Constructive Virtual Tactical Tower Ops DIS OTBSAF TUAV DIS AD Rdrs/ EOs DIS ATC Rdr DIS

20. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle LOE 0301 – The Short Circuit May 03 Jun 03 Jul 03 Aug 03Sep 03 Oct 03 Nov 03 Define Develop Conduct Analyze Report Staff Check Sponsor Identification Problem Identification Terrain Incomplete Iterative Prototyping Sponsor Participant Access Automated Situation Awareness Analysis Tools Report Pre-Preparation Corrective Action Create Terrain/Visualization Cell

21. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Ascription Does the Garbage Can Model fit? Is it really that bad? Decision outcomes driven by –Temporal Confluence: Decision makers, Choice opportunities, Problems, Potential solutions Is the method we use for “deciding which method to use” any better that that?

22. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Toolbox Structured brain storming Scenarios Analysis of historical data Options analysis, Decision analysis Group ranking Seminar war games Analytical tools – mathematical analysis Computer-based war games (JANUS, OneSAF) Trials, exercises, and evaluations The Operational Research Skill Set Skill set Facilitator Scribe Co-author of scenarios Enumerator of votes Analyst of preferences Modeller Mathematician/Statistician Experimenter in simulation space Experimenter in live trials Soft OR/OA Methods Hard

23. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Roles of Soft OR Methods To support thinking and planning by an individual analyst or decision maker – problem articulation To support discussion between consultant and decision maker – problem negotiation To support debate and conclusion among decision makers – group decision support To initiate or strengthen organisational capabilities – organisational development – Source: Steve Cropper presentation in Holt and Pickburn (2001)

24. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Prescription Consider the full spectrum in the tool box Prepare for the full range of required skills Implement a “lessons” process, and strive to improve

25. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Application of the Spectrum of Methods Soft and hard OA should be viewed as equally useful and part of the analyst’s toolkit. These should be viewed as a spectrum of options within the analyst’s toolkit which should be selected and used as appropriate. It was recognised that hard and soft methods can be used together at appropriate stages of a study (usually soft for problem formulation, hard for resolution). Analysts need to be aware of all OA techniques and, broadly where they should and should not be employed. Better education may help to achieve this. An expert practitioner (or team) is needed with expertise in the range of methods appropriate to the problem. – Extracts from Holt and Pickburn (2001), pp. 15, 17 and 19

26. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Proscription Woe, woe, and thrice woe Beware of complexity

27. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Error Complexity Specification Error Minimize Model Error Error Complexity Measurement Error Models, Complexity and Error Error Complexity Model Error

28. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Error Complexity Specification Error Complexity Model Error Error Complexity Measurement Error If we can increase the accuracy of performance characteristics, we can accommodate greater complexity. Models, Complexity and Error Overall error drops.

29. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Error Complexity Specification Error Complexity Model Error Error Complexity Measurement Error Minimize Model Error by Decreasing Complexity Models, Complexity and Error

30. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Main Recommendation Make the actors “stickier”: –Analysts need to be aware of all OA techniques and, broadly where they should and should not be employed. Better education may help to achieve this –An expert practitioner (or team) is needed with expertise in the range of methods appropriate to the problem Source: Holt and Pickburn (2001)

31. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle References Michael D. Cohen, James G. March, Johan P. Olsen “A Garbage Can Model of Organizational Choice” Administrative Science Quarterly, Vol. 17, No. 1. (Mar., 1972), pp. 1-25 James G. March and Roger Weissenger-Baylon eds. (1986) Ambiguity and Command: Organizational Perspectives on Military Decision- Making. Marshfield, MA Pitman Publishing Inc. James G. March (1994) A Primer on Decision Making: How Decisions Happen. New York: The Free Press Future Army Development Plan (1998) Kingston, Ontario: Directorate of Land Strategic Concepts John Holt and George Pickburn (2001) OA Techniques for the Future. Farnborough, UK: Defence Evaluation and Research Agency. 30 March 2001

32. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Questions – Discussion Fred Cameron Tel: +1.613.541.5010 ext 2470 Email: Cameron.FWP2@forces.gc.ca

33. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle The Collective Estimate The Formal Estimate Receipt of Task List Assumptions Mission Analysis Consideration of Factors COA Development COA Comparison Decision Execution Receipt of Task List Assumptions Mission Analysis Consideration of Factors COA Development COA Comparison Decision Execution Receipt of Task List Assumptions Mission Analysis Consideration of Factors COA Development COA Comparison Decision Execution Source: Teaching material from the Canadian Land Force Command and Staff College

34. One Army, One Team, One Vision Operational Research Une Armée, une équipe, une vision Recherche opérationnelle Intuitive Methods “Thinking in Time” by Neustadt and May –Define the situation, and the decision-maker’s concerns –List analogues from the past, then K-U-P/L-D –Known-Unclear-Presumed and Likenesses-Differences Klein’s Recognition Primed Decision Model –Involve Commander to greater degree –Determine a single “preferred” COA early –“Skilled decision makers usually generate a good COA on their first try” –Improve on the first COA Sources: Neustadt and May (1986) and Ross, et al (2004)