Multiple Criteria Optimization and Analysis in the Planning of Effects-Based Operations (EBO)

1. Jouni Pousi, Kai Virtanen and Raimo P. Hämäläinen Systems Analysis Laboratory Helsinki University of Technology jouni.pousi@hut.fi, kai.virtanen@hut.fi, raimo@hut.fi Multiple Criteria Optimization and Analysis in the Planning of Effects-Based Operations (EBO)

2. Effects-based operations (EBO) • Concept for planning and executing military operations(e.g., Davis, 2001) • Complex military operations, systems perspective • How to produce effects in a system? • Single action produces multiple effects • CONTENTS • Planning of EBO = MCDM problem • Multiple criteria influence diagrams in EBO

3. Steps in EBO planning System Threateningmilitary buildupin a country • Identify higher level objective • Describe operation as a system • Derive effects from thehigher-level objective • First described qualitatively • Find actions which contribute to the fulfillment of effects • How to measure the fulfillmentof effects? • Criteria • Economic sanctions • Missile strike • Etc. • Public unrest • Etc. Actions Effects

4. Description of the system Country • Functionally related elements • Elements have states • E.g. works / out of order Element Car factory Dependency Car factory goes out of business if steel mill doesn’t produce steel Element Steel mill

5. Effects described by one or multiple criteria Criteria defined in terms of system elements Multiple elements related to single criterion Criteria make effects measurable Qualitative modeling Country Car factory Criterion Unemployment Effect Publicunrest Criterion Media coverage

6. System model Elements = System variables Dependencies between elements Actions : Element states Criteria The EBO problem Planning EBO as an MCDM problem

7. Planning EBO as an MCDM problem Country System • Economic sanctions • Missile strike • Etc. • Public unrest • Etc. Actions Effects Actions Criteria

8. Previous literature • Probabilistic modeling (Davis, 2001) • System dynamics (Bakken et al., 2004) • Bayesian networks (Tu et al., 2004) • Single criterion • Combination of Bayesian networks and Petri nets (Wagenhals & Levis, 2002; Haider & Levis, 2007) • Effects over time • Efficient set not determined • Agent-based modeling (Wallenius & Suzic, 2005) • Calculates criteria given an action • Efficient set not determined • Outranking methods (Guitouni et al., 2008) • No system model

9. Multiple criteria influence diagram (MCID) System • Bayesian network used as a system model • Elements: chance nodes / random variables • Dependencies: arcs /conditional probabilities • MCID (Diehl & Haimes, 2004) • Actions represented by decision nodes • Criteria represented by utility nodes ... ... Actions Criteria

10. EBOLATOR - Decision support tool • Implementation utilizing MCID • Construction of system model(GeNIe, 2009)

11. EBOLATOR - Graphical user interface • Visualization of actions • Calculation of efficient set • Criteria weights  Single action

12. EBOLATOR - Sensitivity analysis • Weights • MCID probabilities

13. EBOLATOR - Example analysis • Defensive air operation • System • Civil and military infrastructure • Actions • Aircraft positioning andair combat tactics • MCID • 12000 probabilities • 729 actions • Analysis • 13 efficient actions • Sensitivity analysis

14. Conclusions • Multiple criteria and systems perspectiveessential in planning EBO • Similar philosophy applicable in other application areas (e.g., hospital, marketing) • Previous modeling techniques improved by MCDM • Successful implementation: EBOLATOR • Multiple criteria influence diagram is an interesting modeling approach in MCDM

15. References 1/2 • B. T. Bakken, M. Ruud and S. Johannessen, “The System Dynamics Approach to Network Centric Warfare and Effects-Based Operations - Designing a ``Learning Lab'' for Tomorrow's Military Operations”, Proceedings of the 22nd International Conference of the System Dynamics Society, Oxford, England, July 25-29, 2004 • P. K. Davis, “Effects-Based Operations: A Grand Challenge for the Analytical Community”, RAND, 2001 • M. Diehl and Y. Y. Haimes, “Influence Diagram with Multiple Objectives and Tradeoff Analysis” , IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans, vol. 34, no. 3, 2004 • A. Guitouni, J. Martel, M. Bélanger and C. Hunter, “Multiple Criteria Courses of Action Selection”, MOR Journal, vol. 13, no. 1, 2008 • Decision Systems Laboratory of the University of Pittsburgh, “Graphical Network Interface”, http://dsl.sis.pitt.edu, 2009

16. References 2/2 • S. Haider and A. H. Levis, ”Effective Course-of-Action Determination to Achieve Desired Effects”, IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans, vol. 37, no. 2, 2007 • H. Tu, Y. N. Levchuk and K. R. Pattipati, “Robust Action Strategies to Induce Desired Effects”,IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans, vol. 34, no. 5, 2004 • L. W. Wagenhals and A. H. Levis, “Modeling Support of Effects-Based Operations in War Games”, Proceedings of the Command and Control Research and Technology Symposium, Monterey, California, USA, June 11-13, 2002 • K. Wallenius and R. Suzic, “Effects Based Decision Support For Riot Control: Employing Influence Diagrams and Embedded Simulation”, Proceedings of the Military Communications Conference, Atlantic City, New Jersey, USA, October 17-20, 2005