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Decision-Making and Investment Appraisal

Evaluating decision tree analysis

  • 3 things to know
  • Component 2

Overview

What a tree adds and what it leaves out — estimated probabilities, no qualitative factors, and an expected value that is an average over repetitions a one-off decision never gets.

What you need to know

Answer each one before you reveal it.

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Key term

How can bias enter a decision tree?

Key term

Give two benefits of decision tree analysis.

Key term

How does a decision tree help a business compare risk?

Questions from this subtopic

Each one with the model answer underneath.

  1. 1

    Give two benefits of decision tree analysis.

    Model answer

    A decision tree forces managers to set out every option and outcome explicitly. Probabilities and payoffs then convert a vague comparison into a single figure for each option.

  2. 2

    How does a decision tree help a business compare risk?

    Model answer

    A tree shows the range of outcomes attached to each option, not only the average. A manager can see that one option risks a large loss while another does not.

  3. 3

    Give two limitations of decision tree analysis.

    Model answer

    The probabilities are estimates and a small change in one can reverse the recommendation. The technique also ignores qualitative factors entirely.

  4. 4

    Why is the accuracy of a decision tree limited by its estimates?

    Model answer

    Every figure on the tree is a forecast of probability or payoff. The calculation is exact but the inputs are not, so the precision of the answer is misleading.

  5. 5

    What qualitative factors does a decision tree omit?

    Model answer

    A tree omits the effect on staff morale, on the firm's reputation and on relationships with suppliers. Ethical considerations and the objectives of the owners are also outside it.

  6. 6

    What is meant by sensitivity analysis in this context?

    Model answer

    Sensitivity analysis reworks the tree with different probabilities to see whether the recommendation changes. A recommendation that survives a wide range of assumptions is far more reliable than one that does not.

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