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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''〔D. Hubbard, ''How to Measure Anything: Finding the Value of Intangibles in Business'', 2nd ed., John Wiley & Sons, 2010.〕 (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.〔B. Fischhoff, L. D. Phillips, and S. Lichtenstein, “Calibration of Probabilities: The State of the Art to 1980,” in ''Judgement under Uncertainty: Heuristics and Biases'', ed. D. Kahneman and A. Tversky (Cambridge University Press, 1982)〕 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 has an impact on an organization, it must be observable and, therefore, measurable. ==Comparison to other methods== AIE differs in several ways from other popular methods of decision analysis: *Unlike the accounting-style business case or cost benefit analysis, it does not rely entirely on point estimates of uncertain values. Since it uses the Monte Carlo method, uncertainty can be modeled explicitly. *Unlike most decision-theory analysis of uncertain decisions, the AIE model starts with "calibrating" the estimators. *The calculation of the value of information is used to guide further measurement efforts. *AIE converts all values to economic terms so that powerful financial and economic optimization methods can be employed. This is unlike some methods that rely mostly on subjective scores. 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. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「applied information economics」の詳細全文を読む スポンサード リンク
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