Decision criteria. Influence diagrams, probability trees, decision trees,
probability of causes. Sensitivity analysis on the probabilities and values.
Value of information and control. Utility functions, expected utility and
certainty equivalent. Multi-objective analysis. Using the spreadsheet for
decision analysis.
Syllabus:
1. Decision criteria: max-min, max-max, realism, max-min regret, equiprobability, the expected value.
2. Uncertainty and probability: encoding the probabilities; probability trees and their analysis.
3. Decisions with uncertainty: good decisions and good results; rules for constructing influence diagrams; construction of the decision tree; the decision tree analysis; the value of perfect information; the value of perfect control.
4. Probabilistic dependence: dependence and independence; conditional probabilities and dependent outcomes; the tree of the nature and Bayes' rule; the value of imperfect information; the value of imperfect control.
5. Risk aversion: describing risk aversion and risk attitude; utility functions and their use; exponential function and risk tolerance.
6. Multi-objective decisions: basic concepts; the additive utility function: normalizing and weighting the objectives.
7. Use of spreadsheets for the analysis of cases.