Applied Information Economics

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:

Practitioners of AIE claim that if something affects an organization, it must be observable and, therefore, measurable.

AIE differs in several ways from other popular methods of decision analysis:

The unique values AIE offers for businesses are (1) a disciplined quantification of the variability in financial projections and (2) the information necessary to systematically reduce that variability.

AIE does tend to be somewhat more elaborate than these alternatives. But practitioners argue that it is no more complicated than analysis methods used in many other fields, as long as trained specialists are used. It also becomes more important to choose rigor over simplicity when the decisions being analyzed are much larger and riskier.

Because the AIE methodology requires an analytical background to understand, articulate to business stakeholders and deliver, its takeup is likely to be gradual.

While there are multiple articles in industry periodicals and government sources (see below) referencing applied information economics, there are few in academic literature. In addition, the following limitations apply:

AIE as a whole, like many decision analysis and risk analysis methods, has little or no research showing the long term benefits of the method. However, AIE itself is not new method and is based on previously-developed components that have a sound theoretical basis and/or have strong empirical evidence of improving on unaided intuition or other popular decision analysis methods. Among these components are Monte Carlo simulations, calibration training, information value calculations from decision theory, and widely accepted empirical methods used for scientific measurement (see references above).

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