Decision Analysis
Decision Under Certainty
Let
- $A$ be a finite set of alternatives (possible decisions)
- $X$ be a set of consequences (usually some financial metrics)
- $c: A \mapsto X$ a consequence function
- $c(a) \in X$ is a consequence of implementing action $a \in A$
Problem:
- to compare alternatives and find the optimal one
- on the basis of their consequences
For these models we make a strong assumption:
- we can quantify the consequences of taking different actions with certainty
However this assumption is not always true
- we often can face situations when consequences $c(a)$ of taking a decision $a$ are not known with certainty
There are two categories of decision analysis tools that help model this:
- we are not able to asses the distribution, but we can list all possible scenarios
Methods
- $c(a)$ is not known with certainty, but we know the probability distribution on the set of $X$
Decision Trees
Links
Sources