Data Consistency in Emergency Management


  • Daji Ergu College of Electrical and Information Engineering Southwest University for Nationalities Chengdu, China, 610041
  • Gang Kou School of Management and Economics University of Electronic Science and Technology of China Chengdu, China, 610054
  • Yi Peng School of Management and Economics University of Electronic Science and Technology of China Chengdu, China, 610054
  • Feixiong Li School of Management and Economics University of Electronic Science and Technology of China Chengdu, China, 610054
  • Yong Shi 1. Research Center on Fictitious Economy and Data Sciences Chinese Academy of Sciences, Beijing 100190, China, and 2. College of Information Science & Technology University of Nebraska at Omaha, Omaha, NE 68182, USA


cardinal inconsistency, positive reciprocal matrix, geometric mean induced bias matrix (GMIBM), inconsistency identification


Timely response is extremely important in emergency management. However, cardinal inconsistent data may exist in a judgment matrix because of the limited expertise, preference conflict as well as the complexity nature of the decision problems. The existing inconsistent data processing models for positive reciprocal matrix either are complicated or dependent on the priority weights, which will delay the decision making process in emergency. In this paper, a geometric mean induced bias matrix (GMIBM), which is only based on the original matrix A, is proposed to quickly identify the most inconsistent data in the judgment matrix. The correctness and effectiveness of the proposed model are proved mathematically and illustrated by two numerical examples. The results show that the proposed model not only preserves most of the original information in matrix A, but also is faster than existing methods.


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