# Sensitivity Analysis

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Sensitivity Analysis A technique used to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions. This technique is used within specific boundaries that will depend on one or more input variables, such as the effect that changes in interest rates will have on a bond's price. Sensitivity analysis is a way to predict the outcome of a decision if a situation turns out to be different compared to the key prediction(s). Sensitivity analysis is very useful when attempting to determine the impact the actual outcome of a particular variable will have if it differs from what was previously assumed.. For example, an analyst might create a financial model that will value a company's equity (the dependent variable) given the amount of earnings per share (an independent variable) the company reports at the end of the year and the company's price-to-earnings multiple (another independent variable) at that time. The analyst can create a table of predicted price-to-earnings multiples and a corresponding value of the company's equity based on different values for each of the independent variables. Value of Information Value of information (VoI) in decision analysis is the amount a decision maker would be willing to pay for information prior to making a decision. VoI is sometimes distinguished into value of perfect information, also called value of clairvoyance (VoC), and value of imperfect information. They are closely related to the widely known expected value of perfect information and expected value of sample information. Note that VoI is not necessarily equal to "value of decision situation with perfect information" - "value of current decision situation" as commonly understood. The above definition illustrates that the value of imperfect information of any uncertainty can always be framed as the value of perfect information, i.e., VoC, of another uncertainty, hence only the term VoC will be used onwards. There are two extremely important characteristics of VoI that always hold for any decision situation; • Value of information can never be less than zero since the decision-maker can always ignore the additional information and makes decision as if such information is not available. • No other information gathering/sharing activities can be more valuable than that quantified by value of clairvoyance. In decision theory, the expected value of perfect information (EVPI) is the price that one would be willing to pay in order to gain access to perfect information.[1] The problem is modeled with a payoff matrix Rij in which the row index i describes a choice that must be made by the payer, while the column index j describes a random variable that the payer does not yet have knowledge of, that has probability pj of being in state j.