Literature DB >> 33400662

CEQD: A Complex Mass Function to Predict Interference Effects.

Fuyuan Xiao.   

Abstract

Uncertainty is inevitable in the decision-making process of real applications. Quantum mechanics has become an interesting and popular topic in predicting and explaining human decision-making behaviors, especially regarding interference effects caused by uncertainty in the process of decision making, due to the limitations of Bayesian reasoning. In addition, complex evidence theory (CET), as a generalized Dempster-Shafer evidence theory, has been proposed to represent and handle uncertainty in the framework of the complex plane, and it is an effective tool in uncertainty reasoning. Particularly, the complex mass function, also known as a complex basic belief assignment in CET, is complex-value modeled, which is superior to the classical mass function in expressing uncertain information. CET is considered to have certain inherent connections with quantum mechanics since both are complex-value modeled and can be applied in handling uncertainty in decision-making problems. In this article, therefore, by bridging CET and quantum mechanics, we propose a new complex evidential quantum dynamical (CEQD) model to predict interference effects on human decision-making behaviors. In addition, uniform and weighted complex Pignistic belief transformation functions are proposed, which can be used effectively in the CEQD model to help explain interference effects. The experimental results and comparisons demonstrate the effectiveness of the proposed method. In summary, the proposed CEQD method provides a new perspective to study and explain the interference effects involved in human decision-making behaviors, which is significant for decision theory.

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Year:  2022        PMID: 33400662     DOI: 10.1109/TCYB.2020.3040770

Source DB:  PubMed          Journal:  IEEE Trans Cybern        ISSN: 2168-2267            Impact factor:   19.118


  7 in total

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Journal:  J Healthc Eng       Date:  2021-02-24       Impact factor: 2.682

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5.  Probabilistic Modeling of Motion Blur for Time-of-Flight Sensors.

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Journal:  Sensors (Basel)       Date:  2022-02-04       Impact factor: 3.576

6.  A Compressed Sensing Measurement Matrix Construction Method Based on TDMA for Wireless Sensor Networks.

Authors:  Yan Yang; Haoqi Liu; Jing Hou
Journal:  Entropy (Basel)       Date:  2022-03-31       Impact factor: 2.738

7.  A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign.

Authors:  Luca Romeo; Emanuele Frontoni
Journal:  Pattern Recognit       Date:  2021-07-22       Impact factor: 7.740

  7 in total

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