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Decision analysis is a systematic approach to decision
making under conditions of uncertainty. To perform a decision analysis one need
to construct a decision model, or decision tree, that consists of:
- ALTERNATIVES to represent the available choices
- OUTCOMES that depict potential consequences of these
choices
- PROBABILITIES, defining the likelihood of outcomes of
each choice
- UTILITIES, or values, assigned to outcomes
A basic decision tree is created from a decision node
(square) which represents the decision point. Chance nodes (circles) represent
the chance events given that the choice is made. The value at the terminal node
(triangle) represents the utility of ending up in the particular outcome state.
Utilities can represent any type of value, such as survival or preference.
Decision Tree
A decision tree is analyzed by the process of averaging out
and folding back. Probabilities for outcomes are multiplied by their utilities
and summed. The choice with the highest
expected utility
is considered the " best" choice. In the example below, this is Choice
A.
Analyzing the Tree
A decision analysis is completed with a sensitivity analysis
where the important variables are systematically swept trough a range of
plausible values. This helps the decision maker to determine the "robustness"
of the optimal choice.
Important considerations in decision analysis:
- View the problem from the correct perspective
- Model the problem in the context of the decision
- Include the appropriate level of detail / relevance
- Model the problem over the appropriate time horizon
- Obtain correct probabilities
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