Sensitivity AnalysisA technique used to determine how different values ​​of an independent variable will affect a particular dependent variable under a given set of hypotheses. This technique is used within specific limits that will depend on one or more input variables, such as the effect that changes in interest rates will have on the price of a bond. Sensitivity analysis is a way to predict the outcome of a decision if a situation resolves itself. be different from key predictions. Sensitivity analysis is very useful when trying to determine the impact that 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 evaluate a company's equity (the dependent variable) given the amount of earnings per share (an independent variable) that the company reports at the end of the year and the price/earnings multiple. company earnings (another independent variable) at that point in time. The analyst can create a table of expected price-earnings multiples and a corresponding value of the company's equity based on different values ​​for each of the independent variables. Value of Information The value of information (VoI) in decision analysis is the amount a decision maker would be willing to pay for information before making a decision. VoI is sometimes divided into perfect information value, also called clairvoyance value (VoC), and imperfect information value. They are closely related to the widely known expected value of perfect information and the expected value of sample information. Note that VoI is not necessarily equal to the "value of the decision situation with perfect information" - "value of the current decision situation" as commonly understood. The above definition illustrates that the imperfect information value of any uncertainty can always be framed as the perfect information value, i.e. VoC, of ​​another uncertainty, so only the term VoC will be used in the following. There are two extremely important features of VoI that always hold for any decision situation;• The value of the information can never be less than zero since the decision maker can always ignore additional information and make decisions as if that information were not available. • No other information gathering/sharing activity can be more valuable than that quantified by the value of clairvoyance. In decision theory, the expected value of perfect information (EVPI) is the price one would be willing to pay to have 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 of which the payer does not yet have knowledge, which has probability pj of being in state j.