Relative Complexity Index for Decision-Making Method

Abstract

The complexity of a method has been discussed in the decision-making area since complexity may impose some disadvantages such as loss of information and a high degree of uncertainty. However, there is no empirical justification to determine the complexity level of a method. This paper focuses on introducing a method of measuring the complexity of the decision-making method. In the computational area, there is an established method of measuring complexity named Big-O Notation. This paper adopts the method for determining the complexity level of the decision-making method. However, there is a lack of applying Big-O in the decision-making method. Applying Big-O in decision-making may not be able to differentiate the complexity level of two different decision-making methods. Hence, this paper introduces a Relative Complexity Index (RCI) to cater to this problem. The basic properties of the Relative Complexity Index are also discussed. After the introduction of the Relative Complexity Index, the method is implemented in Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method.

Research on UAV Flight Parameter Identification Method Based on Launch Force and Airspeed

“…To compare the complexity of the three algorithms, an analysis was conducted using the Big-O notation method proposed by Juris Hartmanis and Richard E. Stearns [ 40 , 41 , 42 ]. In Big-O notation, O represents the order of magnitude, and O(f(n)) indicates how the algorithm’s complexity grows as the size of the problem (n) increases.…”

“…As the window widens, the number of points exceeding the threshold gradually increases, and the launch force stabilizes, making the recognition results more reliable. To compare the complexity of the three algorithms, an analysis was conducted using the Big-O notation method proposed by Juris Hartmanis and Richard E. Stearns [40][41][42]. In Big-O notation, O represents the order of magnitude, and O(f(n)) indicates how the algorithm's complexity grows as the size of the problem (n) increases.…”