Portfolio Tools & Techniques: Financial Management and Decision Support: Decision Making Methods

Workshop Overview

Decisions that matter are usually hard to make if not only to endure the consequences of. Due to various levels of structure issuing a decision demands breaking it down to its elemental constituents to represent it in a solvable manner while incorporating values, assumptions, risks, strategies and alternatives. Once resolved the consequences of a decision made are subject to what-if analysis to proof it against fluttering changes then the value of that decision is compared to the best case scenario to evaluate any expense made to improve the quality of information whether by experimenting or incorporating new insights or even rumor. This means that not only is the decision evaluated but the decision maker as well. 

Learning Objectives  

  • Initiate decisions from strategies and goals.
  • Break decisions into elements of alternatives, risks, strategy, and value.
  • Recognize the different decision structures in terms of representation and context.
  • Resolve the decision to issue an optimal strategy.
  • Produce a sensitivity report mapping the extent of change in decision in terms of uncertainty change.
  • Evaluate the information from consultancy against the status quo.
  • Deal with conflicting decisions. 

Workshop Outline  


  • Introduction to Decision Analysis
    • Why Are Decisions Hard?
    • Why Study Decision Analysis?
    • Subjective Judgments and Decision Making
    • The Decision-Analysis Process 
      • Requisite Decision Models
    • Where Is Decision Analysis Used?
    • Where Does the Software Fit In?
    • Where Are We Going from Here? 
  • Elements of Decision Problems 
    • Values and Objectives
    • Making Money: A Special Objective
    • Values and the Current Decision Context
    • Decisions to Make
    • Sequential Decisions
    • Uncertain Events
    • Consequences
    • The Time Value of Money: A Special Kind of Trade-Off
  • Structuring Decisions 
    • Structuring Values
    • Fundamental and Means Objectives
    • Getting the Decision Context Right 
    • Structuring Decisions: Influence Diagrams
    • Influence Diagrams and the Fundamental-Objectives Hierarchy
    • Using Arcs to Represent Relationships
    • Some Basic Influence Diagrams
      • The Basic Risky Decision
      • Imperfect Information
      • Sequential Decisions
      • Intermediate Calculations 
    • Constructing an Influence Diagram
      • Some Common Mistakes
      • Multiple Representations and Requisite Models 
    • Structuring Decisions: Decision Trees
    • Decision Trees and the Objectives Hierarchy
    • Some Basic Decision Trees 
      • The Basic Risky Decision
      • Imperfect Information
      • Sequential Decisions
    • Decision Trees and Influence Diagrams Compared
    • Decision Details: Defining Elements of the Decision
    • More Decision Details: Cash Flows and Probabilities
    • Defining Measurement Scales for Fundamental Objectives 
  • Making choices
    • Decision Trees and Expected Monetary Value
    • Solving Influence Diagrams: Overview
    • Solving Influence Diagrams: The Details
    • Solving Influence Diagrams
    • Risk Profiles
      • Cumulative Risk Profiles 
    • Dominance: An Alternative to EMV
    • Making Decisions with Multiple Objectives
    • Analysis: One Objective at a Time
    • Subjective Ratings for Constructed Attribute Scales
    • Assessing Trade-Off Weights
    • Analysis: Expected Values and Risk Profiles for Two Objectives 


  • Sensitivity Analysis
    • A Modeling Approach
    • Problem Identification and Structure
    • One-Way Sensitivity Analysis
    • Tornado Diagrams
    • Dominance Considerations
    • Two-Way Sensitivity Analysis
    • Sensitivity to Probabilities
    • Two-Way Sensitivity Analysis for Three Alternatives 
  • Modeling Uncertainty
    • Venn Diagrams
    • Uncertain Quantities
      • Discrete Probability Distributions 
    • Expected Value
    • Variance and Standard Deviation 
      • Covariance and Correlation for Measuring Dependence
      • Continuous Probability Distributions
      • Expected Value, Variance, and Standard Deviation: The Continuous Case
      • Covariance and Correlation: The Continuous Case 
    • Bayes' Theorem 
  • Using Data 
    • Using Data to Construct Distributions 
      • Histograms
      • Empirical CDFs
    • Fitting Distributions to Data
    • Using Data to Model Relationships
    • The Regression Approach
      • Estimation
      • Regression Modeling


  • Value of Information
    • Some Basic Ideas 
      • Probability and Perfect Information
      • The Expected Value of Information
    • Expected Value of Perfect Information
    • Expected Value of Imperfect Information
    • Value of Information in Complex Problems
    • Value of Information, Sensitivity Analysis, and Structuring
    • Value of Information and Nonmonetary Objectives
    • Value of Information and Expert
  • Risk Attitudes 
    • Expected Utility, Certainty Equivalents, and Risk Premiums 
    • Keeping Terms Straight
    • Utility Function Assessment 
      • Assessment Using Certainty Equivalents
      • Assessment Using Probabilities
      • Gambles, Lotteries, and Investments 
    • Risk Tolerance and the Exponential Utility Function
    • Decreasing and Constant Risk Aversion
      • Decreasing Risk Aversion
      • Constant Risk Aversion 
  • Utility Axioms, Paradoxes, and Implications 
    • Axioms for Expected Utility
    • Paradoxes
    • Implications
      • Implications for Utility Assessment
      • Managerial and Policy Implications
  • Conflicting Objectives 
    • Fundamental Objectives and the Additive Utility Function
    • Trading Off Conflicting Objectives
    • Multi-attribute Utility Models with Interactions
    • Independence Conditions 

Course Duration

24 hours  
Workshop format

Interactive sessions matched with exercises, mini-cases and discussions. 
Intended audience

Decision makers, decision owners, Monitoring stakeholders, Sponsors, Business leaders, General managers, Strategic consultants, Functional managers, Claim evaluators, Risk profilers. 

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