1. 1 Chapter 3 Structuring Decision

2. 2 Structuring Decisions Learning Objectives Fundamental steps in model creation Identify and structure values and objectives –Fundamental objectives and hierarchies –Means objectives and networks Graphical methods for decision frameworks –Influence diagrams –Decision trees Concepts for model details –Elements –Probabilities –Cash flows –Objectives measurements

3. 3 Structuring Decisions Three fundamental steps to create a decision model: 1.Identify and structure the values and objectives 2.Structure the decision elements into a logical framework 3.Refine and precisely define all elements of the model

4. 4 Identifying and Structuring Values and Objectives Identify important issues consistent with values Identify and define relevant objectives Organize objectives –Fundamental objectives and hierarchies –Means objectives and networks Ensure consistency with context

5. 5 Fundamental Objectives General and reflect values Organized in hierarchies Paste figure 3.1

6. 6 Means Objectives Identify how to accomplish fundamental objectives Organized in networks Paste figure 3.2

7. 7 Decision Context Three criteria for consistency of values-based objectives and decision context: 1.Properly reflective of decision situation 2.Decision owner has authority to make decision 3.Feasible to conduct analysis within resources

8. 8 Structure Elements into Framework Two graphical methods: Influence diagrams Decision trees

9. 9 Influence Diagrams Geometric representation of decision elements: –Rectangles: represent decisions –Ovals: represent chance events –Diamonds: represent payoffs –Round-cornered rectangles: represent intermediate consequences or mathematical calculations

10. 10 Influence Diagrams Geometric shapes are called nodes –Rectangles: decision nodes –Ovals: chance nodes –Diamonds: payoff nodes –Round-cornered rectangles: consequence or calculation nodes

11. 11 Influence Diagrams Nodes connected by arcs to represent relationships Nodes are named –Predecessor: if at beginning of arc –Successor: if at termination of arc Copy figure 3.7

12. 12 Influence Diagrams Arcs represent two types of relationships, defined by the successor node Arcs can represent sequence or relevance –Sequence: successor node is decision node –Relevance: successor node is any non-decision node Paste figure 3.8

13. 13 Influence Diagrams Basic influence diagrams –Basic risky decision: One decision and one uncertainty –Imperfect information: Imperfect information about an uncertainty influences payoff –Sequential decision: Result from one decision determines if another decision is to be made –Intermediate calculation: Compiles predecessors information

14. 14 Influence Diagrams Basic Risky Decision Copy the figure 3.9 Is the potential gain from choice A worth the risk that must be taken?

15. 15 Influence Diagrams Imperfect Information Copy the figure 3.10 Imperfect information about uncertain event received and decision made; uncertain event is then resolved. Both decision and event affect payoff

16. 16 Influence Diagrams Sequential Decisions Copy the figure 3.12 Sequential decisions reveal time sequence

17. 17 Influence Diagrams Intermediate Calculations Copy the figure 3.16 Calculation nodes emphasize diagram structure

18. 18 Creating Influence Diagrams No single strategy for creation Identify decision context and objectives Create simple version, then add details Unique representation rare

19. 19 Creating Influence Diagrams Three common mistakes Confusion with flow charts Misuse of sequence arcs Inclusion of cycles

20. 20 Decision Trees More details; sequential and chronological flows Representation of events: –Square: decisions to be made –Circles: chance events Branches from squares represent alternatives available Branches from circles represent the possible outcomes of chance events Final consequences or payoffs at branch ends Decision trees flow from left to right

21. 21 Decision Trees Copy the figure 3.21 –Alternatives: mutually exclusive; collectively exhaustive; select only one –Outcomes: mutually exclusive; collectively exhaustive; only one can occur –Complete tree includes all possible decision paths, alternatives and outcomes

22. 22 Decision Trees Multiple objectives: –List payoffs at branch ends –Can be cumbersome and bulky Basic decision tree forms: –Basic risky decision –Double- risk dilemma –Range-of-risk dilemma –Imperfect information –Sequential decisions

23. 23 Decision Trees Basic Risky Decision Copy figure 3.24

24. 24 Decision Trees Double-Risk Dilemma Copy figure 3.26

25. 25 Decision Trees Range-of-Risk Dilemma Copy figure 3.27

26. 26 Decision Trees Imperfect Information Copy figure 3.28

27. 27 Decision Trees Sequential Decisions Copy figure 3.29 Alternatives at second decision do not change as a result of outcome A or B

28. 28 Decision Trees vs. Influence Diagrams Decision trees –Display more information –Can become cumbersome Influence Diagrams –Graphical presentation relatively simple –Easier for some to understand

29. 29 Decision Trees vs. Influence Diagrams Decision trees and influence diagrams are complementary Strategy for use: –Start with influence diagram to understand major elements –Convert to decision tree to document details

30. 30 Decision Details Defining elements Elements must be measurable Element definitions must require no judgment or interpretation

31. 31 Decision Details Probabilities and Cash flows Chance events require probability assignments –Only one outcome can occur –Probability of an outcome must be between 0 and 1 –Probabilities at chance node must sum to 1.00 Cash flows specified on branches –Cash flows compiled at branch ends to show consequences –Net present values used to reflect timing effects

32. 32 Decision Details Measuring fundamental objectives Measurement is crucial Measure lowest level objectives in hierarchy Measurement scales identified by attributes –Natural attribute scales –Proxy (surrogate) attribute scales –Constructed attribute scales

33. 33 Summary Fundamental steps of model structuring Identify and structure values and objectives Graphical methods for structuring models Concepts for model details