This section discusses methods for multiple attribute decision making (MADM) when no preference information is given. Classical decision rules such as dominance, maximin, and maximum are highlighted as they yield objective solutions without requiring subjective preference data. The dominance method, in particular, is detailed, where an alternative is considered dominated if another alternative excels it in one or more attributes and equals it in the rest. This method reduces the set of alternatives by eliminating dominated ones, leading to a set of nondominated solutions. The procedure involves comparing alternatives in pairs and discarding the dominated ones, repeating this process until a nondominated set is determined. The section also includes a numerical example from the fighter aircraft problem to illustrate the application of the dominance method. Additionally, Calpine and Golding's derivation of the expected number of nondominated solutions when comparing \( m \) alternatives with \( n \) attributes is provided, focusing on the case where all elements in the decision matrix are uniformly distributed random numbers.This section discusses methods for multiple attribute decision making (MADM) when no preference information is given. Classical decision rules such as dominance, maximin, and maximum are highlighted as they yield objective solutions without requiring subjective preference data. The dominance method, in particular, is detailed, where an alternative is considered dominated if another alternative excels it in one or more attributes and equals it in the rest. This method reduces the set of alternatives by eliminating dominated ones, leading to a set of nondominated solutions. The procedure involves comparing alternatives in pairs and discarding the dominated ones, repeating this process until a nondominated set is determined. The section also includes a numerical example from the fighter aircraft problem to illustrate the application of the dominance method. Additionally, Calpine and Golding's derivation of the expected number of nondominated solutions when comparing \( m \) alternatives with \( n \) attributes is provided, focusing on the case where all elements in the decision matrix are uniformly distributed random numbers.