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Value of information

This text explores the concept of quantifying the value of information, highlighting the significance of both perfect and imperfect information. The objective is to understand how information influences decision-making by comparing the expected monetary values (EMV) of decisions made with and without information. The accuracy of information plays a critical role, defined by conditional probabilities. Through illustrative examples, we assess the EMV derived from different choices and demonstrate the value gained from having perfect information, ultimately emphasizing its essential role in informed decision-making.

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Value of information

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Presentation Transcript

  1. Value of information • Objective: Quantify value of information --both perfect and imperfect • Principle: Information should help a decision maker make decisions that are better than decisions without information • Expected value of information=expected monetary value (EMV) with information-expected monetary value (EMV) without information

  2. Measuring accuracy of information • Value of information depends on decision we want to make and accuracy of information • Accuracy of information: P(expert says “A”/A occurs), P(expert says “A will not occur”/A does not occur). The higher these conditional probabilities, the more accurate is the information. • Perfect information: P(expert says “A”/A occurs)=1, P(expert says “no A”/A does not occur)=1.

  3. Example of measuring accuracy of information

  4. Example I (0.1) 20 (0.2) 10 EMV=7 (0.6) 5 A (0.1) 0 B 6 If we have no information we will chose A; EMV with no information=7

  5. Example: continued (0.1) A20 Choose A, payoff 20 (0.2) A  10 Choose A, payoff 10 A  5 (0.6) Choose B, payoff 6 A  0 (0.1) Choose B, payoff 6 EMV with perfect information=8.2 Value of perfect information=8.2-7=1.2

  6. Example II (0.1) 20 (0.2) 10 (0.6) 8 A (0.1) 7 B 6 Option A dominates option B. Value of perfect information =0.

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