Investment decision using D numbers

Investment decision is a complex problem influenced by many factors such as investment value, cost, sales, etc. One critical issue of investment decision is the representation of uncertain information. Many theories can deal with uncertain information. However, more or less deficiencies of these methods are revealed. Recently, D numbers, a new method to represent uncertain and incomplete information, has been widely applied in dealing with uncertain information. In this paper, D numbers is applied to investment decision problem. Investment value, cost, sales, proportion of national income and the extent of environmental pollution are represented by D numbers and the algorithm of maximizing deviations is used to calculate the weight of different factors. An illustrative case is given to show the effectiveness of the new method.

[1]  Fabio Cuzzolin,et al.  $L_{p}$ Consonant Approximations of Belief Functions , 2014, IEEE Transactions on Fuzzy Systems.

[2]  Zeshui Xu,et al.  Uncertain Multi-Attribute Decision Making , 2015 .

[3]  Ayodele Mobolurin,et al.  An approach to using the analytic hierarchy process for solving multiple criteria decision making problems , 1994 .

[4]  Zeshui Xu,et al.  Uncertain Multi-Attribute Decision Making: Methods and Applications , 2015 .

[5]  G. Wei,et al.  Generalized triangular fuzzy correlated averaging operator and their application to multiple attribute decision making , 2012 .

[6]  Sankaran Mahadevan,et al.  Environmental impact assessment based on D numbers , 2014, Expert Syst. Appl..

[7]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[8]  Gleb Beliakov,et al.  How to build aggregation operators from data , 2003, Int. J. Intell. Syst..

[9]  Yong Deng,et al.  A new fuzzy dempster MCDM method and its application in supplier selection , 2011, Expert Syst. Appl..

[10]  Xinyang Deng,et al.  Bridge Condition Assessment Using D Numbers , 2014, TheScientificWorldJournal.

[11]  Xinyang Deng,et al.  Supplier selection using AHP methodology extended by D numbers , 2014, Expert Syst. Appl..

[12]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[13]  Yong Deng,et al.  Generalized evidence theory , 2014, Applied Intelligence.

[14]  Yong Deng,et al.  D-CFPR: D numbers extended consistent fuzzy preference relations , 2014, Knowl. Based Syst..

[15]  Yong Deng D Numbers: Theory and Applications ? , 2012 .

[16]  Arthur P. Dempster,et al.  A Generalization of Bayesian Inference , 1968, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[17]  Endre Boros,et al.  A note on "Optimal resource allocation for security in reliability systems" , 2009, Eur. J. Oper. Res..

[18]  Gao Feng-ji Multiple Attribute Decision Making on Plans with Alternative Preference under Incomplete Information , 2000 .

[19]  Yong Deng,et al.  D numbers theory: a generalization of Dempster-Shafer evidence theory , 2014, ArXiv.

[20]  Jian Guo A risk assessment approach for failure mode and effects analysis based on intuitionistic fuzzy sets and evidence theory , 2016, J. Intell. Fuzzy Syst..

[21]  Gangyao Kuang,et al.  Target Recognition via Information Aggregation Through Dempster–Shafer's Evidence Theory , 2015, IEEE Geoscience and Remote Sensing Letters.

[22]  Hu-Chen Liu,et al.  Failure mode and effects analysis using D numbers and grey relational projection method , 2014, Expert Syst. Appl..