Decision Analysis Levels of Proficiency

The days of training required depends greatly upon the experience and capabilities of the participants. Here are four proficiency levels and approximate days of training:

AWARENESS 1 to 4 hours

Ten-step decision analysis process
Understanding that cash flow drives business value
Using present value discounting for time value of money
Expected value concept and solving simple decision trees
Expected Monetary Value decision rule

Note: Executive Workshops range from 1 hour to 1 day in length. Depending upon the interests of the participants, the discussion ranges from how to interpret and use the results of a decision analysis and the calculation fundamentals -to- risk policy and measuring shareholder value creation.

WORKING KNOWLEDGE 0.5-3 days

Decision policy; why risk aversion usually can be neglected
Traditional decision criteria and strengths/weaknesses
Complement, addition and multiplication rules
Probability distributions; mean and standard deviation
How Monte Carlo simulation works
Simple spreadsheet modeling and using a decision analysis tool (Crystal Ball, @RISK, DATA, PrecisionTree or other decision analysis software)
Designing decision models for Monte Carlo simulation and decision tree analysis
Multi-discipline team analyses

COMPETENCE 2 to 7 days

Modeling inflation and escalation
Bayes’ Theorem
Value of information problems
Proficient in spreadsheet modeling: layout, names, functions, debugging, documenting
Competence in a decision analysis computer tool (see above)
Modeling dependencies; representing correlation
Diagramming techniques (e.g., influence, tornado, sensitivity and spider diagrams; thought maps)
Judgments and biases; feedback; post-audits
Cost of capital; capital constraints; simple optimization
Project schedule models and project risk management

PROFICIENCY experience with substantial real-world problems

Experience in constructing medium-sized decision models (over 50 variables)
Competitive bidding
Portfolio theory; CAPM alternative
Using a utility function to represent risk policy
Traditional optimization techniques: LP and mathematical programming
Emerging technologies: simulated annealing, expert systems, fuzzy logic, chaos theory, analytic hierarchy process, multi-criteria decision-making
Evaluation project control, review and documentation
Analysis write-up and presentation