Assessing Transition of Security Operations in Afghanistan

1. Assessing Transition of SecurityOperations in Afghanistan Status Report 18 March 2010

2. Agenda • Problem Statement • Methodology • System Design Update • Values and Metrics Update • Preliminary Results • Friction Points • Earned Value Management

3. Problem Statement • The goal of the research is to develop a value model that assesses the transition of security lead from the International Security Assistance Force (ISAF) and US Forces Afghanistan (USFOR-A) to the Afghani government and Afghanistan National Security Forces (ANSF) • Deputy Director, Force Integration and Training (CJ7) / CSTC-A defined five lines of operation (LOOs) that support the goal of transferring security operations • Accelerate ANSF growth • Achieve security for the Afghan population • Marginalize malign actors • Achieve legitimate, responsive, and accountable governance • Facilitate community development • Develop metrics and an accompanying decision support tool to measure progress against the five LOOs • Stakeholders • Force Integration and Training cell of NTM-A/CSTC-A (sponsor) • NTM-A/CSTC-A • Coalition military leadership • U.S. government leadership

4. Methodology Project group organized into two subgroups • Values and Metrics • Research values and metrics • “Requirements” to win a counter-insurgency conflict • Assessments of ANSF, security, Afghani government, and community • Develop value model with sponsor • System Design • Development of user interface, input forms, storage, usable output • Integrate values, metrics, and value model from other team into the system

5. Technical Approach – System Design System Input: the quantitative portion of the value model in a standardized survey format, completed by military units System Processing and Storage: completed survey templates are configuration controlled and ingested into data storage. User querying capabilities allow the retrieval of data (by unit and/or AOR and/or date range) to research trends Analysis Output: Condensed and easily understood presentation for decision makers 5

6. Concept of Operation • Surveys from 5 main military regions • CJ7 processes surveys and requests status report CJ7 Processes Surveys Requests Status Report Military Regions

7. System Design Update • Past Weeks Progress • Input Form Prototype • Data Compiler Prototype • Query Prototype • Output Prototype • Way Ahead • Obtain weights for Value Model metrics • Refine interface and status report requirements • Expand Compiler Capacity

8. Operational Scenario

9. Technical Approach – Value Model Qualitative Value Model: the identification of an objective hierarchy relating fundamental and means objectives Quantitative Value Model: the articulation of the decision maker’s preferences towards the attributes, and the means of measuring each attribute V(x) = ∑wivi(xi) where wi = weight of attribute i vi = value of attribute i at score xi 9

10. Values and Metrics Update • Near Term • Completing value/metric hierarchy without sponsor input • Completing weight elicitation without sponsor input • Completing input forms • Mid Term • Provide completed hierarchy with weights and input form to system team

11. Weights - Theory (1 of 2) Sports Car .7 .3 Performance Comfort .1 .35 .25 .3 .4 .35 .25 Braking Acceleration Handling Top Speed Head Room Leg Room Shoulder Room (.7)x(.1) (.7)x(.35) (.7)x(.25) (.7)x(.3) .07 .245 .175 (.3)x(.4) (.3)x(.25) .21 (.3)x(.35) .075 .12 .105 Bottom Row Weights • Weights represent the relative importance of each parameter vis-à-vis other parameters at the same node • must sum to one at each level under each node • Bottom Row weights represent lowest level parameter’s importance to overall decision; used for final check with DM • Product of the weights on the branch of the tree • Also must sum to one 11

12. Weights - Theory(2 of 2) • First, define Range of Variation (ROV) as the actual range of possible values (from worst to best) for a parameter • For example, in our sports car example suppose that, for the cars we are examining, the car with the slowest top speed was 140 mph and car with the highest top speed was 190 mph, with all other cars in between: • ROV: 140 – 190 mph • Several methods to elicit weights • Direct weights: simply ask DM to provide • Swing weights: “… thought experiment in which DM compares individual attributes directly by imagining (typically) hypothetical outcomes.” • Robert T. Clemen & Terence Reilly • SMARTER: requires only that DM rank order attributes 1 - n • Rank Reciprocal: requires only that DM rank order attributes 1 - n • Rank Sum: requires only that DM rank order attributes 1 - n 12

13. Swing Weights – Example(1 of 2) • Assume you have a node with 3 parameters, each with the indicated ROV • Annual Income; ROV: $18K - $40K, where higher is better • Income Tax Rate; ROV: 1.3% - 8.5%, where lower is better • Population; ROV: 250,000 – 500,000, where higher is better • Step 1; Select the one attribute you most want to shift from worst to best • Suppose you most prefer to move income from $18K to $40K • Step 2; Select the second most desired attribute to change, from worst to best. How important is this to you compared to your first choice ? • Suppose you would prefer to move the tax rate from 8.5% to 1.3%, but only half as important as shifting annual income • Repeat step 2 for all remaining parameters • In our example income is ten times more important than population

14. Swing Weights – Example (2 of 2) • Summary of DM’s feedback on previous slide with K-weight concept: • Most important attribute to improve is income: Kincome = 1 • Second most important attribute to improve: Tax Rate • Half as important as income therefore Ktax = .5 Kincome • Third most important attribute to improve: Population • One-tenth as important as income therefore Kpopulation = .1 Kincome • Convert to scaled weights that will be inserted the appropriate node of the value model: • Kincome = 1 = 1 / 1.6 = .625 weight income • Ktax = .5 Kincome = .5 / 1.6 = .3125 weight tax • Kpopulation = .1 Kincome = .1 / 1.6 = .0625 weight population Column Sums to 1.6

15. Preliminary Results • Functioning test system using input forms

16. Preliminary Results • Functioning test system using queries

17. Preliminary Results • Functioning test system creating an output from queries

18. Friction Points • Distance and interaction of sponsor • No face-to-face meetings possible • Flow of information is sporadic • Use local point of contact for weight elicitation and fabricate unavailable data

19. Earned Value Management

20. Cost Index

21. Questions?

22. BACKUP SLIDES

23. Work Breakdown Schedule

24. External System Diagrams

25. System Analysis Diagram

26. Functional Architecture

27. Functional Decomposition

28. Value Structure(1 of 5)

29. Value Structure(2 of 5)

30. Value Structure(3 of 5)

31. Value Structure(4 of 5)

32. Value Structure(5 of 5)

33. Metrics and Range of Variation(1 of 7)

34. Metrics and Range of Variation(2 of 7)

35. Metrics and Range of Variation(3 of 7)

36. Metrics and Range of Variation(4 of 7)

37. Metrics and Range of Variation(5 of 7)

38. Metrics and Range of Variation(6 of 7)

39. Metrics and Range of Variation(7 of 7)

40. Range of Variation (ROV)aka “Range of a value measure” • Definition: “The possible variation of the scores of a value measure” -Gregory S. Parnell • Important precursor to determining DM value (or utility) function *OR681, GMU

41. Weights and Utility Curves(3 of 3) • Elicit utility through lottery or certainty equivalence • Weights and utility can be linear, piecewise, exponential, or an S-curve *OR681, GMU

42. Visit Our Project Website http://mason.gmu.edu/~dugarte/index.html