EIN 6935 Risk & Decision Analysis

Objective: Build foundations for probability-based analytical principles underlying engineering decision making under uncertainty and risk.

References:
Introduction to Statistical Decision Theory, J. W. Pratt et al., 2008.
Decisions with Multiple Objectives, R. L. Keeney et al., 1993.
Decisions under Uncertainty, I. Jordaan, 2005.
Making Hard Decisions, R. T. Clemen et al., 2004.
Dynamic Programming: Models and Applications, E. V. Denardo, 2003.

Topics:

• I. Crunch review of probability concepts
•    Elements of set theory; events & probability spaces
•    Elementary Bayesian concepts; statistical independenc
•    Random variables, vectors, & functions
•    Distribution transformations
• II. Decision making under strict uncertainty
•    Dominated actions
•    Properties of decision criteria
•    Maximin, minimax regret, Hurwicz' alpha criteria
•    Laplace's principle of insuffcient reasoning
• III. Introduction to decision making under risk
•    Expected monetary value (EMV); expected opportunity loss (EOL)
•    Expected value of sample/perfect information (EVSI/EVPI)
•    Uncertain rewards & inaccurate information
• IV. Utility theory
•    Binary relations & preference orderings; risk profiles
•    Stochastic dominance of reward distributions
•    Concept of utility; response functions
•    Utility functions: properties & assessment; common families
•    Certainty equivalence & risk premium; risk attitudes; risk tolerance
•    Underlying axioms of utility theory; behavioral paradoxes
• V. Multi-dimensional utility theory
•    Multi-attribute value functions; marginal rate of substitution
•    Utility independence; additive independence
•    Assessment of multi-attribute utility functions
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