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Revising Judgments in the Light of New Information

This guide explores the role of Bayes' theorem in revising judgments with new information. It covers topics such as prior and posterior probabilities, the components problem, vague priors, reliability of information, retailer's decision-making, EVPI calculation, and the value of imperfect information. Learn how to apply Bayesian principles to enhance decision-making processes effectively.

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Revising Judgments in the Light of New Information

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Presentation Transcript

  1. Revising Judgments in the Light of New Information

  2. Bayes’ theorem Prior probability New information Posterior probability

  3. The components problem

  4. Applying Bayes’ theorem to the components problem

  5. Vague priors and very reliable information

  6. The effect of the reliability of information on the modification of prior probabilities

  7. The retailer’s problem with prior probabilities

  8. Applying Bayes’ theorem to the retailer’s problem

  9. Applying posterior probabilities to the retailer’s problem

  10. Determining the EVPI

  11. Calculating the EVPI

  12. Deciding whether to buy imperfect information

  13. If test indicates virus is present

  14. If test indicates virus is absent

  15. Determining the EVII

  16. Expected profit with imperfect information = $62 155 Expected profit without the information = $57 000 Expected value of imperfect information (EVII) = $5 155

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