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Exercise: Building Decision Trees

2022

This sample exercise and solution set supports the teaching pack on Building Decision Trees, in which students learn how to structure the elements (e.g., objectives, alternatives, probabilities, and outcomes) of a problem into a decision tree model, conduct a baseline analysis of the expected value of different alternatives, assess the value of perfect information, and perform sensitivity analyses. In addition to the sample exercise, material include an instructor's note, videos, companion slides, a glossary, and an annotated bibliography.

Learning Objectives

  • Integrate the core elements (alternatives, chances, outcomes) of a decision problem into a decision tree model.
  • Calculate the expected value of each alternative using a process of averaging and folding back the decision tree.
  • Calculate the expected value of perfect information.
  • Perform a basic sensitivity analysis and threshold analysis.

The exercise provides just 2-3 simple examples of the types of short questions that can be useful for practicing some of the skills covered in this teaching pack. These should be modified, tailored, and expanded upon to meet the goals of specific classes.

This teaching pack was developed by Sue J. Goldie at the Center for Health Decision Science, Harvard T.H. Chan School of Public Health. The multimedia components were developed as part of a series of pilots in the CHDS Media Hub, led by Jake Waxman, where media-based pedagogy experiments contribute to new ways of thinking about short form content.

 

Related Files:

Source:

Exercise. Teaching Pack: Building Decision Trees. Center for Health Decision Science, Harvard T.H. Chan School of Public Health 2022. https://repository.chds.hsph.harvard.edu/repository/2722