Introduction to Decision Analysis

This hands-on Introduction to Decision Analysis gives you the tools to make better, more informed and justifiable decisions. The focus is on thinking creatively about issues and then applying structured, formal analysis to spur action. A range of tools are considered, providing a comprehensive toolkit for those willing to enhance the effectiveness of their organizational or personal decision-making.

This course focuses on lightweight, rapid, "agile" modeling ---cycling rapidly between problems, definitions, analyses, conclusions and actions. Emphasis is placed on structuring assumptions, as opposed to mathematical modeling.

The course does not provide a "deep dive" into the techniques presented. Instead, it will give you an overview of the field and form a foundation for a deeper study of the most appropriate techniques for your current and future decision-making needs.

Introduction to Decision Analysis Benefits

• In this Decision Analysis training, you will learn how to:

  • Structure your decision analysis projects.
  • Understand and interpret uncertainty.
  • Recognize and mitigate psychological biases.
  • Uncover cause and effect relationships.
  • Evaluate alternatives using multi-criteria decision analysis.
  • Peer into the future using forecasting.
  • Apply game theory to develop a strategy in the face of competition.
  • Explore complex issues using network analysis and simulation.
  • Leverage the benefits, and avoid the pitfalls, of teams in the decision-making process.

• Prerequisites

  Familiarity with Microsoft Excel and a basic understanding of mathematical concepts at a high-school level are prerequisites for participants.

Decision Analysis Course Outline

Module 1: Introduction to decision analysis

• What makes a good decision?
• History of decision analysis
• Problem solving
• Creative thinking
• Data science vs decision analysis
• Bulletproof Problem Solving
• OODA loop
• Conducting research

Module 2: Uncertainty

• Definition and categorization
• Thinking probabilistically
• Expected value
• Visualizing uncertainty
• Utility curves
• Psychological biases

Module 3: Statistical modelling and inference

• Correlation
• Regression
• Monte Carlo methods

Module 4: Evaluating options

• Forced ranking
• Decision matrices
• Multi-criteria decision analysis Decision trees

Module 5: Forecasting

• Time series
• Expert judgement
• Prediction markets
• Scenario planning

Module 6: Interactive decision-making

• Stakeholder analysis
• Game theory
• Confrontation analysis

Module 7: Systems thinking

• Feedback loops
• Influence diagrams
• System dynamics
• Strategy mapping

Module 8: Group decision making

• Wisdom of crowds
• Delphi method
• Voting