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In this example a company should prefer product B's risk and payoffs under realistic risk preference coefficients. Multiple-criteria decision-making (MCDM) or multiple-criteria decision analysis (MCDA) is a sub-discipline of operations research that explicitly evaluates multiple conflicting criteria in decision making (both in daily life and in settings such as business, government and medicine).
In MCPs, the alternatives are evaluated over a set of criteria. A criterion is an attribute that incorporates preferential information. Thus, the decision model should have some form of monotonic relationship with respect to the criteria. This kind of information is explicitly introduced (a priory) in multicriteria methods for MCPs.
The European Working Group on Multiple Criteria Decision Aiding (also, EURO Working Group on Multicriteria Decision Aiding, EWG on Multicriteria Aid for Decisions, or EWG-MCDA) is a working group whose objective is to promote original research in the field of multicriteria decision aiding at the European level. [1]
Multi-objective optimization or Pareto optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, or multiattribute optimization) is an area of multiple-criteria decision making that is concerned with mathematical optimization problems involving more than one objective function to be optimized simultaneously.
A basic decision matrix consists of establishing a set of criteria and a group of potential candidate designs. One of these is a reference candidate design. The other designs are then compared to this reference design and being ranked as better, worse, or same based on each criterion.
Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery". A stated confidence level generally applies only to each test considered individually, but often it is desirable to have a confidence level for the whole family of simultaneous tests. [ 4 ]
The following table defines the possible outcomes when testing multiple null hypotheses. Suppose we have a number m of null hypotheses, denoted by: H 1, H 2, ..., H m. Using a statistical test, we reject the null hypothesis if the test is declared significant. We do not reject the null hypothesis if the test is non-significant.
This means that criteria and preference information can be uncertain, inaccurate or partially missing. Incomplete information is represented in SMAA using suitable probability distributions. The method is based on stochastic simulation by drawing random values for criteria measurements and weights from their corresponding distributions. [1]
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