Structuring Decision

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Chapter 3

• Structuring Decision

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Structuring DecisionsLearning 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

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Structuring Decisions

• Three fundamental steps to create a decision
• model
• Identify and structure the values and objectives
• Structure the decision elements into a logical
  framework
• Refine and precisely define all elements of the
  model

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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

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Fundamental Objectives

• General and reflect values
• Organized in hierarchies
• Paste figure 3.1

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Means Objectives

• Identify how to accomplish fundamental objectives
• Organized in networks
• Paste figure 3.2

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Decision Context

• Three criteria for consistency of values-based
• objectives and decision context
• Properly reflective of decision situation
• Decision owner has authority to make decision
• Feasible to conduct analysis within resources

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Structure Elements into Framework

• Two graphical methods
• Influence diagrams
• Decision trees

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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

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Influence Diagrams

• Geometric shapes are called nodes
• Rectangles decision nodes
• Ovals chance nodes
• Diamonds payoff nodes
• Round-cornered rectangles consequence or
  calculation nodes

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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

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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

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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

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Influence Diagrams Basic Risky Decision

• Copy the figure 3.9
• Is the potential gain from choice A worth the
• risk that must be taken?

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Influence DiagramsImperfect 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

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Influence DiagramsSequential Decisions

• Copy the figure 3.12
• Sequential decisions reveal time sequence

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Influence DiagramsIntermediate Calculations

• Copy the figure 3.16
• Calculation nodes emphasize diagram
• structure

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Creating Influence Diagrams

• No single strategy for creation
• Identify decision context and objectives
• Create simple version, then add details
• Unique representation rare

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Creating Influence Diagrams

• Three common mistakes
• Confusion with flow charts
• Misuse of sequence arcs
• Inclusion of cycles

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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

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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

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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

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Decision TreesBasic Risky Decision

• Copy figure 3.24

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Decision TreesDouble-Risk Dilemma

• Copy figure 3.26

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Decision TreesRange-of-Risk Dilemma

• Copy figure 3.27

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Decision TreesImperfect Information

• Copy figure 3.28

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Decision TreesSequential Decisions

• Copy figure 3.29
• Alternatives at second decision do not change
• as a result of outcome A or B

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Decision Trees vs. Influence Diagrams

• Decision trees
• Display more information
• Can become cumbersome
• Influence Diagrams
• Graphical presentation relatively simple
• Easier for some to understand

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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

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Decision Details

• Defining elements
• Elements must be measurable
• Element definitions must require no judgment or
  interpretation

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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

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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

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Summary

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