How is the expected value of a Mega Goal decision under uncertainty calculated?

• A. EV = maximum possible value
• B. EV = sum(probability_i x value_i) for possible outcomes; consider risk-adjusted values
• C. EV = minimum possible value
• D. EV is not used in decision making

Answer: (B)

Think of the decision under uncertainty as balancing how likely each possible payoff is with how valuable that payoff is. The key idea is to use a probability-weighted average of all possible outcomes. You multiply each outcome’s value by its probability and add them up: EV = sum over outcomes of (probability × value). In a Mega Goal setting, you might have a range of results from different levels of success and failure; by weighting each result by how likely it is, you get a single number that summarizes the long-run average payoff.

If you want to account for risk, you can use risk-adjusted values or a utility function in place of the raw values, and then apply the same weighted-sum idea. But the core calculation remains the probability-weighted sum.

Why not take the maximum or the minimum? The maximum ignores how often that best outcome occurs, and the minimum ignores any upside entirely. The expected value uses the full distribution to guide choice, often by comparing EVs across options to pick the one with the highest long-run average payoff.