Advanced Decision Analysis with Portfolio and Project Modeling - ADA

Quality forecasts and evaluations depend upon well-designed project and portfolio models, clear decision policy, sound professional judgments, and a good decision process. In this course, participants learn to build good decision models.

We use Microsoft Excel as the calculation platform for risk assessment, project evaluation, and simple portfolio models. Add-in software provides Monte Carlo and decision tree capabilities. The course emphasizes evaluation concepts and techniques rather than particular software programs. Participants work in pairs using a shared personal computer.

Target Audience

Evaluation engineers, project planners, business analysts, economists, and
managers

This course is for professionals involved with project evaluation, portfolio, and other forecasting and assessment models. Participants should have a prior background in decision analysis through work experience or attending a DA course such as Petroleum Risk and Decision Analysis.

Before registering, please visit http://www.decisionapplications.com/ada-pre-read/ to review a course prerequisites list and to take a short self-assessment quiz. Use ‘ada’ (no quotes) as the password.

You Will Learn

• Frame, build, and evaluate decision models and extract key insights
• Apply a utility function for risk policy
• Design investment portfolio optimization models that include constraints, requirements, and typical interrelationships between projects
• Use decision tree software for value of imperfect information analysis
• Use Monte Carlo simulation software with optimization
• Develop quality Excel models for evaluating projects and portfolios

Course Content

• Decision Modeling: application of DA process for modeling; influence diagrams; judgments and biases; sampling error bias; sensitivity analysis; documentation  and good modeling practices; real options overview
• Monte Carlo Simulation: multi-pay prospect risking (similar to play analysis); calculating probabilities and distributions with simulation; modeling and optimizing investment portfolios; valuing added control and flexibility; stopping rules; ways to model correlation
• Decision Tree Analysis: value of information review; sensitivity analysis; solving with a utility for decision policy.
• Decision Policy: portfolio optimization to maximize economic value; efficient frontiers; multi-criteria decisions; risk policy as a utility function; calculating expected utility and certainty equivalent; insurance and hedging; optimizing working interests
• Implementation: eliciting a decision maker’s or organization’s preferences for trade-offs among objectives, time value, and risk attitude; decision analysis presentation agendas and formats; special topics from the instructor’s research and experience.

Product Details