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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 [1] (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 [ clarification needed ] 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. [3] However, AIE itself is not a 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).
Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of surveys and experiments.
Monte Carlo methods, or Monte Carlo experiments, are a broad class of computational algorithms that rely on repeated random sampling to obtain numerical results. The underlying concept is to use randomness to solve problems that might be deterministic in principle. They are often used in physical and mathematical problems and are most useful when it is difficult or impossible to use other approaches. Monte Carlo methods are mainly used in three problem classes: optimization, numerical integration, and generating draws from a probability distribution.
Financial economics is the branch of economics characterized by a "concentration on monetary activities", in which "money of one type or another is likely to appear on both sides of a trade". Its concern is thus the interrelation of financial variables, such as share prices, interest rates and exchange rates, as opposed to those concerning the real economy. It has two main areas of focus: asset pricing and corporate finance; the first being the perspective of providers of capital, i.e. investors, and the second of users of capital. It thus provides the theoretical underpinning for much of finance.
Uncertainty refers to epistemic situations involving imperfect or unknown information. It applies to predictions of future events, to physical measurements that are already made, or to the unknown. Uncertainty arises in partially observable or stochastic environments, as well as due to ignorance, indolence, or both. It arises in any number of fields, including insurance, philosophy, physics, statistics, economics, finance, medicine, psychology, sociology, engineering, metrology, meteorology, ecology and information science.
Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system can be divided and allocated to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; ideally, uncertainty and sensitivity analysis should be run in tandem.
There are two main uses of the term calibration in statistics that denote special types of statistical inference problems. "Calibration" can mean
Decision analysis (DA) is the discipline comprising the philosophy, methodology, and professional practice necessary to address important decisions in a formal manner. Decision analysis includes many procedures, methods, and tools for identifying, clearly representing, and formally assessing important aspects of a decision; for prescribing a recommended course of action by applying the maximum expected-utility axiom to a well-formed representation of the decision; and for translating the formal representation of a decision and its corresponding recommendation into insight for the decision maker, and other corporate and non-corporate stakeholders.
Monte Carlo methods are used in corporate finance and mathematical finance to value and analyze (complex) instruments, portfolios and investments by simulating the various sources of uncertainty affecting their value, and then determining the distribution of their value over the range of resultant outcomes. This is usually done by help of stochastic asset models. The advantage of Monte Carlo methods over other techniques increases as the dimensions of the problem increase.
In mathematical finance, a Monte Carlo option model uses Monte Carlo methods to calculate the value of an option with multiple sources of uncertainty or with complicated features. The first application to option pricing was by Phelim Boyle in 1977. In 1996, M. Broadie and P. Glasserman showed how to price Asian options by Monte Carlo. An important development was the introduction in 1996 by Carriere of Monte Carlo methods for options early exercise features.
In metrology, measurement uncertainty is the expression of the statistical dispersion of the values attributed to a measured quantity. All measurements are subject to uncertainty and a measurement result is complete only when it is accompanied by a statement of the associated uncertainty, such as the standard deviation. By international agreement, this uncertainty has a probabilistic basis and reflects incomplete knowledge of the quantity value. It is a non-negative parameter.
In statistics, resampling is any of a variety of methods for doing one of the following:
Uncertainty quantification (UQ) is the science of quantitative characterization and reduction of uncertainties in both computational and real world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known. An example would be to predict the acceleration of a human body in a head-on crash with another car: even if the speed was exactly known, small differences in the manufacturing of individual cars, how tightly every bolt has been tightened, etc., will lead to different results that can only be predicted in a statistical sense.
The following outline is provided as an overview of and topical guide to finance:
Post-modern portfolio theory is an extension of the traditional modern portfolio theory. Both theories propose how rational investors should use diversification to optimize their portfolios, and how a risky asset should be priced.
Calibrated probability assessments are subjective probabilities assigned by individuals who have been trained to assess probabilities in a way that historically represents their uncertainty. For example, when a person has calibrated a situation and says they are "80% confident" in each of 100 predictions they made, they will get about 80% of them correct. Likewise, they will be right 90% of the time they say they are 90% certain, and so on.
Mathematical finance, also known as quantitative finance and financial mathematics, is a field of applied mathematics, concerned with mathematical modeling of financial markets.
In marketing, Bayesian inference allows for decision making and market research evaluation under uncertainty and with limited data.
Douglas Hubbard is a management consultant, speaker, and author in decision sciences and actuarial science. He is the inventor of the Applied Information Economics (AIE) method and founder of Hubbard Decision Research (HDR). He is the author of How to Measure Anything: Finding the Value of Intangibles in Business, The Failure of Risk Management: Why It’s Broken and How to Fix It, Pulse: The New Science of Harnessing Internet Buzz to Track Threats and Opportunities and his latest book, How to Measure Anything in Cybersecurity Risk. Since 2017, two of his books have been on the required reading list for the Society of Actuaries exam prep. In addition to his books, Hubbard has been published in several periodicals including Nature, the IBM Journal of Research and Development, OR/MS Today, Analytics, CIO, Information Week, and Architecture Boston. His books have been selected for required, recommended, and suggested reading by multiple business schools such as the School of Business and Economics, Jon M. Huntsman School of Business, and Carl H. Lindner College of Business.