Applied information economics (AIE) is a decision analysis method developed by Douglas W. Hubbard and partially described in his book How to Measure Anything: Finding the Value of Intangibles in Business (2007; 2nd ed. 2010; 3rd ed. 2014). AIE is a method for the practical application of several proven methods from decision theory and risk analysis including the use of Monte Carlo methods. However, unlike some other modeling approaches with simulations, AIE incorporates the following: Calibrated probability assessment. This is a method for training estimators and experts (who are relied on for the inputs in Monte Carlo methods) to be neutrally confident about their assigned probabilities. That is, their probabilities are neither overconfident (too high) nor underconfident (too low). Computing the value of additional information. AIE uses information value calculations from decision theory such as the expected value of perfect information and the value of imperfect (partial) information. Often, this is done for a large number of uncertain variables in some type of decision model or business case. The result will reveal where efforts to reduce uncertainty by making further measurements are best spent. Empirical methods applied according to the information value of the measurement. This step is, in fact, the reason for the name of the method. Most Monte Carlo modeling experts stop modeling after the first (uncalibrated) probability estimates from experts and there is usually little emphasis on further measurements with empirical methods. Since AIE computes the value of additional information, measurement can be selective and focused. This step often results in a very different set of measurement priorities than would otherwise have been used. Various optimization methods including modern portfolio theory. MPT and other methods are applied to determine ideal risk and return positions for a set of alternatives. Practitioners of AIE claim that if something affects an organization, it must be observable and, therefore, measurable.