A quantum framework for modelling subjectivity in multi-attribute group decision making

Abstract Due to the increasing complexity of decision tasks, the experiences, knowledge or opinions from multiple decision makers (DMs) often need to be aggregated. Many multi-attribute group decision making (MAGDM) approaches have been widely studied. In a MAGDM problem, the multiple DMs are usually regarded as independent. However, in many practical situations, the opinions from different DMs are likely to influence each other or to be affected by the environment. To address it, a quantum framework based on quantum probability theory is proposed in this paper. The goal is to model the subjectivity in MAGDM when we have subjective beliefs towards DMs’ independence or relations. The modeled subjectivity sources from the interference of beliefs. However, in classical models, subjectivity usually comes from the preference of DMs when determining weights or giving an evaluation towards alternatives. In this paper, the classical MAGDM technics are used to do the preparation firstly. Then a Bayesian network is constructed and it is extended to a quantum framework. The beliefs toward DMs are in a superposition. Namely, the opinions of DMs are viewed as various wave functions that are occurring at the same time. Then the beliefs will interfere with each and influence the final result. When all the DMs are deemed independent, the quantum framework will degenerate into a classical Bayesian network. The various cases of a simple example have been discussed and analyzed. A real supplier section problem is also used to illustrate and validate our model. Finally, an overall comparative analysis regarding the subjectivity in MAGDM is performed.

[1]  Zhongliang Yue,et al.  Group decision making with multi-attribute interval data , 2013, Inf. Fusion.

[2]  Zhongliang Yue,et al.  An avoiding information loss approach to group decision making , 2013 .

[3]  X H Hu,et al.  A Bayesian-based two-stage inexact optimization method for supporting stream water quality management in the Three Gorges Reservoir region , 2016, Environmental Science and Pollution Research.

[4]  Xinyang Deng,et al.  An Evidential Axiomatic Design Approach for Decision Making Using the Evaluation of Belief Structure Satisfaction to Uncertain Target Values , 2018, Int. J. Intell. Syst..

[5]  Mohammad Izadikhah,et al.  An algorithmic method to extend TOPSIS for decision-making problems with interval data , 2006, Appl. Math. Comput..

[6]  Madjid Tavana,et al.  An extended VIKOR method using stochastic data and subjective judgments , 2016, Comput. Ind. Eng..

[7]  Da Ruan,et al.  Multi-Objective Group Decision Making - Methods, Software and Applications with Fuzzy Set Techniques(With CD-ROM) , 2007, Series in Electrical and Computer Engineering.

[8]  Zhibin Wu,et al.  The maximizing deviation method for group multiple attribute decision making under linguistic environment , 2007, Fuzzy Sets Syst..

[9]  Kash Barker,et al.  A Bayesian network model for resilience-based supplier selection , 2016 .

[10]  C Sierra,et al.  Developing a new Bayesian Risk Index for risk evaluation of soil contamination. , 2017, The Science of the total environment.

[11]  Yong Hu,et al.  A DEMATEL-based completion method for incomplete pairwise comparison matrix in AHP , 2016, Ann. Oper. Res..

[12]  Yong Deng,et al.  A novel method for forecasting time series based on fuzzy logic and visibility graph , 2017, Advances in Data Analysis and Classification.

[13]  Ali al-Nowaihi,et al.  The Ellsberg Paradox: A Challenge to Quantum Decision Theory? , 2016 .

[14]  Wen Jiang,et al.  An evidential dynamical model to predict the interference effect of categorization on decision making results , 2018, Knowl. Based Syst..

[15]  Wen Jiang,et al.  An improved soft likelihood function for Dempster–Shafer belief structures , 2018, Int. J. Intell. Syst..

[16]  Shu Li,et al.  A test of "reason-based" and "reluctance-to-think" accounts of the disjunction effect , 2012, Inf. Sci..

[17]  Kash Barker,et al.  Modeling infrastructure resilience using Bayesian networks: A case study of inland waterway ports , 2016, Comput. Ind. Eng..

[18]  Yashuai Li,et al.  Linguistic multi-attribute decision making with multiple priorities , 2017, Comput. Ind. Eng..

[19]  Enrique Herrera-Viedma,et al.  On multi-granular fuzzy linguistic modeling in group decision making problems: A systematic review and future trends , 2015, Knowl. Based Syst..

[20]  J. Busemeyer,et al.  Empirical Comparison of Markov and Quantum models of decision-making , 2009 .

[21]  Ray Y. Zhong,et al.  A customer satisfaction evaluation model for logistics services using fuzzy analytic hierarchy process , 2016, Ind. Manag. Data Syst..

[22]  Chuan Yue,et al.  A geometric approach for ranking interval-valued intuitionistic fuzzy numbers with an application to group decision-making , 2016, Comput. Ind. Eng..

[23]  Petr Ekel,et al.  A flexible consensus scheme for multicriteria group decision making under linguistic assessments , 2010, Inf. Sci..

[24]  Madjid Tavana,et al.  An Integrated Intuitionistic Fuzzy AHP and SWOT Method for Outsourcing Reverse Logistics Highlights , 2015 .

[25]  Shu-Ping Wan,et al.  Some new generalized aggregation operators for triangular intuitionistic fuzzy numbers and application to multi-attribute group decision making , 2016, Comput. Ind. Eng..

[26]  Jerome R Busemeyer,et al.  Can quantum probability provide a new direction for cognitive modeling? , 2013, The Behavioral and brain sciences.

[27]  Irina Basieva,et al.  Quantum-Like Representation Algorithm for Trichotomous Observables , 2011 .

[28]  Xinyang Deng,et al.  Dependence assessment in human reliability analysis using an evidential network approach extended by belief rules and uncertainty measures , 2018, Annals of Nuclear Energy.

[29]  Olga Choustova,et al.  Quantum probability and financial market , 2009, Inf. Sci..

[30]  Xinyang Deng,et al.  D-AHP method with different credibility of information , 2017, Soft Computing.

[31]  Burak Efe,et al.  An integrated fuzzy multi criteria group decision making approach for ERP system selection , 2016, Appl. Soft Comput..

[32]  James Nga-Kwok Liu,et al.  Application of decision-making techniques in supplier selection: A systematic review of literature , 2013, Expert Syst. Appl..

[33]  Sankaran Mahadevan,et al.  Reliability analysis with linguistic data: An evidential network approach , 2017, Reliab. Eng. Syst. Saf..

[34]  Adil Baykasolu,et al.  Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS , 2017, Expert Syst. Appl..

[35]  Dong Li,et al.  A hierarchical model for eco-design of consumer electronic products , 2014 .

[36]  Shuai Xu,et al.  A modified Physarum-inspired model for the user equilibrium traffic assignment problem , 2016, ArXiv.

[37]  Wen Jiang,et al.  Intuitionistic fuzzy evidential power aggregation operator and its application in multiple criteria decision-making , 2018, Int. J. Syst. Sci..

[38]  Wen Jiang,et al.  An Uncertainty Measure for Interval-valued Evidences , 2017, Int. J. Comput. Commun. Control.

[39]  Drakoulis Martakos,et al.  In-depth analysis and simulation study of an innovative fuzzy approach for ranking alternatives in multiple attribute decision making problems based on TOPSIS , 2011, Appl. Soft Comput..

[40]  Francisco Herrera,et al.  A fusion approach for managing multi-granularity linguistic term sets in decision making , 2000, Fuzzy Sets Syst..

[41]  Xiuli Geng,et al.  An extended 2-tuple linguistic DEA for solving MAGDM problems considering the influence relationships among attributes , 2017, Comput. Ind. Eng..

[42]  Sankaran Mahadevan,et al.  Supplier selection based on evidence theory and analytic network process , 2016 .

[43]  Chao Fu,et al.  A method of determining attribute weights in evidential reasoning approach based on incompatibility among attributes , 2015, Comput. Ind. Eng..

[44]  Xinyang Deng,et al.  Analyzing the monotonicity of belief interval based uncertainty measures in belief function theory , 2017, Int. J. Intell. Syst..

[45]  Zhigang Zeng,et al.  Entropy-weighted ANP fuzzy comprehensive evaluation of interim product production schemes in one-of-a-kind production , 2016, Comput. Ind. Eng..

[46]  Andreas Wichert,et al.  Are quantum-like Bayesian networks more powerful than classical Bayesian networks? , 2018 .

[47]  Richard M. Shiffrin,et al.  Context effects produced by question orders reveal quantum nature of human judgments , 2014, Proceedings of the National Academy of Sciences.

[48]  Andreas Wichert,et al.  Quantum-Like Bayesian Networks for Modeling Decision Making , 2016, Front. Psychol..

[49]  Diederik Aerts,et al.  Quantum structure in economics: The Ellsberg paradox , 2012 .

[50]  A. Milani,et al.  The effect of normalization norms in multiple attribute decision making models: a case study in gear material selection , 2005 .

[51]  Juan Liu,et al.  An integrating OWA-TOPSIS framework in intuitionistic fuzzy settings for multiple attribute decision making , 2016, Comput. Ind. Eng..

[52]  Shyi-Ming Chen,et al.  Multiple attribute decision making based on interval-valued intuitionistic fuzzy sets, linear programming methodology, and the extended TOPSIS method , 2017, Inf. Sci..

[53]  Yejun Xu,et al.  Standard and mean deviation methods for linguistic group decision making and their applications , 2010, Expert Syst. Appl..

[54]  James M. Yearsley,et al.  Advanced tools and concepts for quantum cognition: A tutorial , 2017 .

[55]  Tao Jiang,et al.  Parameter inference for non-repairable multi-state system reliability models by multi-level observation sequences , 2017, Reliab. Eng. Syst. Saf..

[56]  Luciano Ferreira,et al.  A fuzzy-Bayesian model for supplier selection , 2012, Expert Syst. Appl..

[57]  J. Busemeyer,et al.  A quantum probability explanation for violations of ‘rational’ decision theory , 2009, Proceedings of the Royal Society B: Biological Sciences.

[58]  Bingyi Kang,et al.  Stable strategies analysis based on the utility of Z-number in the evolutionary games , 2018, Appl. Math. Comput..

[59]  Zichang He,et al.  Evidential Supplier Selection Based on Interval Data Fusion , 2017, Int. J. Fuzzy Syst..

[60]  James T. Townsend,et al.  Quantum dynamics of human decision-making , 2006 .

[61]  Xinyang Deng,et al.  Dependence assessment in human reliability analysis based on D numbers and AHP , 2017 .

[62]  Diederik Aerts,et al.  Beyond-Quantum Modeling of Question Order Effects and Response Replicability in Psychological Measurements , 2015, ArXiv.

[63]  Zeshui Xu,et al.  Probabilistic linguistic term sets in multi-attribute group decision making , 2016, Inf. Sci..

[64]  Masanori Ohya,et al.  A quantum-like model of selection behavior , 2017, 1705.08536.

[65]  Zhibin Wu,et al.  Managing consistency and consensus in group decision making with hesitant fuzzy linguistic preference relations , 2016 .

[66]  Jerome R. Busemeyer,et al.  What Is Quantum Cognition, and How Is It Applied to Psychology? , 2015 .

[67]  Didier Sornette,et al.  Quantum Decision Theory as Quantum Theory of Measurement , 2008, ArXiv.

[68]  Matthew P. A. Fisher,et al.  Quantum Cognition: The possibility of processing with nuclear spins in the brain , 2015, 1508.05929.

[69]  Jerome R. Busemeyer,et al.  Quantum Models of Cognition and Decision , 2012 .

[70]  Jerome R. Busemeyer,et al.  Data fusion using Hilbert space multi-dimensional models , 2018, Theor. Comput. Sci..

[71]  Sen Liu,et al.  Decision making for the selection of cloud vendor: An improved approach under group decision-making with integrated weights and objective/subjective attributes , 2016, Expert Syst. Appl..

[72]  Andreas Wichert,et al.  Interference Effects in Quantum Belief Networks , 2014, Appl. Soft Comput..

[73]  Yong Deng,et al.  Evaluation method based on fuzzy relations between Dempster–Shafer belief structure , 2018, Int. J. Intell. Syst..

[74]  José María Moreno-Jiménez,et al.  Consensus Building in AHP-Group Decision Making: A Bayesian Approach , 2010, Oper. Res..

[75]  Cengiz Kahraman,et al.  Fuzzy Multicriteria Decision-Making: A Literature Review , 2015, Int. J. Comput. Intell. Syst..

[76]  Zheng Wang,et al.  Interference effects of categorization on decision making , 2016, Cognition.

[77]  R. Shiffrin,et al.  Bayesian Model Comparison Favors Quantum Over Standard Decision Theory Account of Dynamic Inconsistency , 2015 .