1. What is EVPI Without Information?

EVPI (Expected Value of Perfect Information) calculates the maximum amount a decision maker should be willing to pay for perfect information. It represents the difference between the payoff with perfect information and the payoff without it.

2. How Does the Calculator Work?

The calculator uses the EVPI equation:

Where:

• \( \text{max payoff} \) — Maximum possible payoff in dollars ($)
• \( \text{chosen} \) — Payoff of the chosen alternative in dollars ($)
• \( \text{prob} \) — Probability of the state of nature (unitless, between 0-1)

Explanation: The equation calculates the expected value of having perfect information by summing the differences between maximum possible payoff and chosen payoff, weighted by their probabilities.

3. Importance of EVPI Calculation

Details: EVPI helps decision makers determine the economic value of obtaining additional information before making important decisions. It quantifies the maximum worth of perfect information in uncertain situations.

4. Using the Calculator

Tips: Enter maximum payoff and chosen payoff in dollars, and probability as a decimal between 0-1. All values must be valid (non-negative amounts, probability between 0-1).

5. Frequently Asked Questions (FAQ)

Q1: What does EVPI represent in decision making?
A: EVPI represents the maximum amount a decision maker should pay for perfect information that would eliminate all uncertainty.

Q2: When should EVPI be calculated?
A: EVPI should be calculated when facing decisions under uncertainty, especially when considering whether to invest in additional research or information gathering.

Q3: How is probability determined in EVPI calculations?
A: Probability is typically based on historical data, expert opinion, or subjective assessment of the likelihood of different states of nature.

Q4: Can EVPI be negative?
A: No, EVPI cannot be negative as it represents the value of information, which is always non-negative.

Q5: How does EVPI relate to decision tree analysis?
A: EVPI is often calculated as part of decision tree analysis to determine whether additional information is worth obtaining before making a final decision.