A Prospect-Theory-Based Operation Loop Decision-Making Method for Kill Web

Luyao Wang, Libin Chen, Zhiwei Yang (), Minghao Li (), Kewei Yang and Mengjun Li
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Luyao Wang: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Libin Chen: College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
Zhiwei Yang: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Minghao Li: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Kewei Yang: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Mengjun Li: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Mathematics, 2022, vol. 10, issue 19, 1-28

Abstract: In the military field, decision making has become the core of the new operational concept, known as the “kill web”. Although the theory of kill web has been widely recognized by many countries, the decision-making methods for the kill web are still in the early stage. Therefore, there is a need for a new decision-making method for the kill web. Firstly, different from the traditional scheme decision, the kill web is a complex system. The method of complex network provides a new perspective on complex systems, so the kill web was modeled based on complex network. Secondly, the kill web relies on artificial intelligence to provide decision-makers with operation loop solutions, and then decision-makers rely on the experience to make a final decision. However, the current decision-making methods only consider one of the intelligent and human decision-making methods, while the kill web needs to consider both. Hence, we combined intelligent decision making with human decision making through multi-objective optimization and the prospect theory. Finally, we designed a nondominated sorting ant colony genetic algorithm-II (NSACGA-II) to solve large-scale problems, since the kill web is a large-scale system. In addition, an illustrative case was used to verify the feasibility and effectiveness of the proposed model. The results showed that, compared with other classical multi-objective optimization algorithms, the NSACGA-II is superior to other superior algorithms in terms of the hypervolume (HV) and spacing (SP), which verifies the effectiveness of the method and greatly improves the quality of commanders’ decision-making.

Keywords: operation loop; kill web; combat decision making; prospect theory; decision preference; multi-objective optimization; multi-criteria decision making; TODIM; TOPSIS; nondominated sorting genetic algorithm-II (NSGA-II); ant colony (AC) algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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