Assessment of influence productivity in cognitive models

This article proposes a new influence productivity assessment methodology that is a cognitive intelligence system for the scenario planning of control impacts (generation and choice) for systems that are represented by directed weighted signed graphs based on the algorithm of effective controls. The algorithm implements a control model that expresses the direction of development (growth) of the system. The algorithm is based on the spectral properties of the adjacency matrix of a graph representing the model of a socioeconomic system and does not impose any constraints on the directions of the edges or the sign and weight range on the edges. Scenarios are assessed based on their compliance with tactical and strategic goals according to the codirectionality degree of the response vector with respect to the base vector of the model. The base vector is the effective control vector without constraints on the controls under the conditions of adequate model operation. The new methodology has three distinctive features: (1) the scenario approach is implemented with respect to a set of controls, (2) this approach is applicable for models with heterogeneous factors and does not require preliminary aggregation of the primary model elements of the system; and (3) this approach has a clear formalization metric for the selecting and generating of a set of control impacts. The process does not require the decision maker to have special mathematical training.

[1]  Dong-Hwan Kim,et al.  Cognitive Maps of Policy Makers on Financial Crises of South Korea and Malaysia: A Comparative Study , 2004 .

[2]  Flávio Neves,et al.  A dynamic fuzzy cognitive map applied to chemical process supervision , 2013, Eng. Appl. Artif. Intell..

[3]  Mohammad Ali Shafia,et al.  Ranking Fuzzy Cognitive Map based scenarios using ELECTRE III: Applied on housing market , 2016, Expert Syst. J. Knowl. Eng..

[4]  Sambit Ghosh,et al.  A graph spectral-based scoring scheme for network comparison , 2016, J. Complex Networks.

[5]  Byung Sung Yoon,et al.  Comparative analysis for Fuzzy Cognitive Mapping , 2016, 2016 Portland International Conference on Management of Engineering and Technology (PICMET).

[6]  Lúcia Valéria Ramos de Arruda,et al.  Hybrid Dynamic Fuzzy Cognitive Maps and Hierarchical Fuzzy logic controllers for Autonomous Mobile Navigation , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[7]  Alexander Tselykh,et al.  Method Maximizing the Spread of Influence in Directed Signed Weighted Graphs , 2017 .

[8]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[9]  Aschkan Omidvar,et al.  Intellectual capital evaluation using fuzzy cognitive maps: A scenario-based development planning , 2016, Expert Syst. Appl..

[10]  Rossitza Setchi,et al.  Modelling IT projects success with Fuzzy Cognitive Maps , 2007, Expert Syst. Appl..

[11]  Jose L. Salmeron,et al.  A Review of Fuzzy Cognitive Maps Research During the Last Decade , 2013, IEEE Transactions on Fuzzy Systems.

[12]  Flávio Neves,et al.  A cooperative architecture for swarm robotic based on dynamic fuzzy cognitive maps , 2017, Eng. Appl. Artif. Intell..

[13]  Kee-Young Kwahk,et al.  Supporting business process redesign using cognitive maps , 1999, Decis. Support Syst..

[14]  Domenico Camarda,et al.  Fuzzy cognitive mapping to support multi-agent decisions in development of urban policymaking , 2019, Sustainable Cities and Society.

[15]  Gülçin Büyüközkan,et al.  Analyzing of CPFR success factors using fuzzy cognitive maps in retail industry , 2012, Expert Syst. Appl..

[16]  Alexander Tselykh,et al.  Knowledge discovery using maximization of the spread of influence in an expert system , 2018, Expert Syst. J. Knowl. Eng..

[17]  Aditya Khamparia,et al.  A comprehensive survey of edge prediction in social networks: Techniques, parameters and challenges , 2019, Expert Syst. Appl..

[18]  Jose L. Salmeron,et al.  Ranking fuzzy cognitive map based scenarios with TOPSIS , 2012, Expert Syst. Appl..

[19]  Hanne Nørreklit The Balanced Scorecard: what is the score? A rhetorical analysis of the Balanced Scorecard , 2003 .

[20]  Lúcia Valéria Ramos de Arruda,et al.  Autonomous Navigation Applying Dynamic-Fuzzy Cognitive Maps and Fuzzy Logic , 2013, AIAI.

[21]  Benjamin F. Hobbs,et al.  FUZZY COGNITIVE MAPPING AS A TOOL TO DEFINE MANAGEMENT OBJECTIVES FOR COMPLEX ECOSYSTEMS , 2002 .

[22]  Paul E. Dunne,et al.  Spectral Techniques in Argumentation Framework Analysis , 2016, COMMA.

[23]  Alexandra S. Penn,et al.  Linear and sigmoidal fuzzy cognitive maps: An analysis of fixed points , 2014, Appl. Soft Comput..

[24]  Abraham Kandel,et al.  Automatic construction of FCMs , 1998, Fuzzy Sets Syst..

[25]  Dimitri P. Bertsekas The Method of Multipliers for Equality Constrained Problems , 1982 .

[26]  M. Alipour,et al.  A new hybrid fuzzy cognitive map-based scenario planning approach for Iran's oil production pathways in the post–sanction period , 2017 .

[27]  Anjali Awasthi,et al.  A scenario simulation approach for sustainable mobility project evaluation based on fuzzy cognitive maps , 2018 .

[28]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[29]  Simaan M. AbouRizk,et al.  Fuzzy cognitive maps enabled root cause analysis in complex projects , 2017, Appl. Soft Comput..

[30]  Alexander Yastrebov,et al.  Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts , 2019, Biosyst..

[31]  Frances Drake,et al.  Beyond the tradition: Using Fuzzy Cognitive Maps to elicit expert views on coastal susceptibility to erosion in Bangladesh , 2018, CATENA.

[32]  R. Vidhya,et al.  Diagnosis of drivers behind historic change in land use using receiver operating characteristic curve , 2018 .

[33]  Koen Vanhoof,et al.  A review on methods and software for fuzzy cognitive maps , 2019, Artificial Intelligence Review.

[34]  Alexander Tselykh,et al.  Management of Control Impacts Based on Maximizing the Spread of Influence , 2019, Int. J. Autom. Comput..

[35]  Konstantinos G. Margaritis,et al.  Using Fuzzy Cognitive Maps as a Decision Support System for Political Decisions , 2001, Panhellenic Conference on Informatics.

[36]  Michalis Glykas,et al.  Performance measurement scenarios with fuzzy cognitive strategic maps , 2012, Int. J. Inf. Manag..