Perfect information - part 1

In this lesson

1. 0:00 Understanding perfect information concept
2. 1:46 Richard's grocery shop scenario setup
3. 2:49 Decision making with perfect forecasts
4. 4:24 Calculating expected value with information
5. 5:28 Comparing value with and without information
6. 6:12 Limitations of expected value analysis

About this lesson

You'll discover why knowing future conditions (like customer demand or market conditions) can eliminate risk and increase expected returns, and how to quantify the maximum you'd be willing to pay for that information. Using Richard's lemonade business as a worked example, the video walks you through comparing expected values with and without perfect information. When a forecasting agency can predict weather with complete accuracy, Richard can order the optimal quantity for each scenario—100 units for cold weather, 300 for moderate, and 500 for hot. This removes uncertainty and increases his expected payoff from 1800 to 2500, meaning the value of perfect information is 700. This tells Richard he should only pay up to 700 for the forecast to break even. The video also explores important limitations of expected value analysis. It assumes risk neutrality and ignores individual attitudes to risk, relies on accurate probability estimates from historical data, and works best for repeated decisions over time rather than one-off choices. Understanding these constraints helps you apply this technique appropriately in exam scenarios and real business decisions.

Duration: 7m