Int J Performability Eng ›› 2020, Vol. 16 ›› Issue (9): 1383-1392.doi: 10.23940/ijpe.20.09.p7.13831392

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Prediction of Order Effects for Landing Signal Officer Guidance Decision-Making based on Quantum Interference

Hui Lia,b,*   

  1. aSchool of Computer and Information Engineering, Harbin University of Commerce, Harbin, 150028, China;
    bHeilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin, 150028, China
  • Submitted on ; Revised on ; Accepted on
  • Contact: * E-mail address:
  • About author:Hui Li is an associate professor in the School of Computer and Information Engineering at Harbin University of Commerce. His research interests include intelligent control, decision making, and quantum information processing.

Abstract: To address the instruction order effects of dynamic decision-making for landing signal officers (LSO) of carrier-based aircraft in a comparative semantic context, in this paper, we propose a prediction approach of LSO guidance decision-making based on quantum interference. By constructing the quantum decision-making model of incompatible events, the restraint of law of total probability is shaken. Comparing the decision deviation of alternatives with non-comparative and comparative contexts, we analyze the interference nature of order effects. Then, we represent the similarity of events based on projective similarity index in Hilbert space and design a q-test prediction that satisfies the order effects of quantum decision-making. The experimental results show that the modified approach is accurate in predicting for assimilation effects and contrast effects. The theoretical basis for improving the accuracy of LSO decision-making is confirmed in a less projective similarity comparative semantic context. As such, the obtained results have certain reference value for personnel allocation, work periods, and landing safety of carrier-based aircraft.

Key words: landing signal officer, decision-making, order effects, quantum interference, comparative semantic context