What Does a Probability Tree Have That a Regular Decision Tree Does Not?

Probability trees and decision trees are graphical representations of expected outcomes. While they look similar, a probability tree has a number of features a decision tree does not, ranging from the kind of data involved to how both are used. Probability trees are generally used to teach probability principles or to help visual thinkers understand probability. Decision trees are used to make decisions.
  1. Fixed Purpose

    • A probability tree is only used to find probabilities. Once you are done with it, you can't change it into something else. Once you map out a decision using a decision tree, you can assign probability values to various consequences of the decision you mapped, effectively turning it into a probability tree. A decision tree can become a probability tree, but a probability tree can't become a decision tree.

    Immediate Values

    • A probability tree has its values already written on it. A decision tree has its values assigned later, once the entire decision process has been mapped out.

    Formulae

    • A probability tree has a fixed formula: when you move across a probability tree, you multiply the values, and when you move down the probability tree you add the values. A decision tree, on the other hand, doesn't necessarily have a formula. Rather, it's a way of displaying information; you don't have to follow any specific mathematical function or even include numbers at all.

    Multiple Outcomes

    • A probability tree can compare multiple outcomes. You can use a probability tree to compare the odds of one thing happening in spite of the other or the odds of both things happening. A decision tree, on the other hand, tends to make things a little more specific. Since you are calculating decisions in a decision tree, the implication is that by choosing one thing you are choosing not to do another. In this way, probability trees give more information.

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