Decision Analysis

Decision Under Certainty


  • $A$ 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 $a \in A$


  • 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:

Decision Under Uncertainty

  • we are not able to asses the distribution, but we can list all possible scenarios


Decision Under Risk

  • $c(a)$ is not known with certainty, but we know the probability distribution on the set of $X$

Decision Trees



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