Refined Neutrosophic Cognitive Maps: A Novel Framework for Modeling Indeterminate Causal Relationships

In this paper we analyzed the impact of Right to Information Act among public people using Neutrosophic Cognitive Map (NCM) which is the generalization of Fuzzy Cognitive Maps (FCMs). This paper has five sections. First section gives the information about the development of FCM and NCM. Second section gives the preliminaries of FCM’s, NCM’s and methods of determining the hidden pattern of NCM’s. In section three, we give the description of the social issues regarding the non-awareness and impact of RTI. Section four gives adaption of NCM’s to the problem and the final section gives the conclusion based on our study.

PurposeThe purpose of this paper is to apply a fuzzy LinPreRa cognitive map to evaluate the interaction and importance of factors affecting the development of strategic alliance partnerships between the outlying island duty-free shops and existing collaboration firms in duty-free shops. Meanwhile, the key factors should be considered in establishing strategic alliance partnerships while analyzing and comparing the perspectives of owners for outlying island duty-free shop and partner vendors of collaborating firms along with differences of influencing key factors on partnership quality.Design/methodology/approachThis study incorporates a fuzzy linguistic preference relation analytical network process (fuzzy LinPreRa ANP) in the fuzzy cognitive map (FCM) method to formulate a fuzzy LinPreRa cognitive map to evaluate the interactions and importance of key factors and the conditions of interactive impacts during the establishment of strategic alliance partnerships. The authors use the outlying island duty-free shops in Taiwan as the empirical subject to illustrate how the fuzzy LinPreRa cognitive map is applied. In-depth, interviews and questionnaire surveys are conducted to collect and evaluate respondents concerning key factors affecting strategic alliance partnerships establishment.FindingsThe following three findings based on the results of empirical analysis: first, the administrative behavioral patterns of managers for strategic alliance partnerships encompass shared values and goal coherence, while the associative statements are located on the first layer of fuzzy LinPreRa cognitive map core associations, which illustrates that businesses attach great importance to conceptual ideas. Second, integrity and reputations of both parties are the governing mechanism of strategic alliance partnerships, influencing mutual reputation. Third, the relationship of strategic alliance partnerships refers to the profit opportunities of both parties and their ability to respond to the market, including future development, regional indicators, marketing capabilities, brand multiplicity and customer retention. However, it can be inferred that such associative factors are located in the outer layer or belong to noncore associations, which means that both parties’ abilities to respond to market reactions are weakened.Practical implicationsThis study provides valuable relationship managerial strategies to maintain long-term partnerships for outlying island duty-free shops and their alliance collaborating firms including strengthened relationships of both parties’ managers to achieve common values and consistent objectives; improved beneficial value of both parties in strategic alliance partnerships; continued close communications to enhance the quality of strategic alliance partnerships; and establishment of personnel training mechanisms and strict formulation of management rules for strategic alliance partnerships.Originality/valueThe main valuable contributions are included the fuzzy LinPreRa cognitive map by combining two different decision methods including FCM and fuzzy LinPreRa ANP is proposed to help decision makers to improve the evaluation quality and calculation efficiency for critical elements’ interaction and importance; the fuzzy LinPreRa cognitive map can clarify considering significant factors when maintaining strategic alliance partnerships and further provide valuable relationship managerial strategies to maintain long-term relationships for duty-free shop owners and their alliance collaborating firms.

Quality in primary education has been classified into five factors involving learners, content, processes, environment and outcomes. In this paper we analyzed, quality in primary education in Chennai and find out its solution using Neutrosophic cognitive maps (NCMS), which is the generalization of fuzzy cognitive maps (FCMS) defined by W.B. Vasantha Kandasamy and Florentine Smarandache. This paper has five sections. First section gives information about development of fuzzy cognitive maps and Neutrosophic cognitive maps. Second section gives the preliminaries of FCMS and NCMS. In section three, we give the description of the problem; final section gives the conclusion based on our study.

Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application

Fuzzy cognitive strategic maps in business process performance measurement

This paper examines the use of fuzzy cognitive maps (FCMs) as a technique for modeling political and strategic issues situations and supporting the decision-making process in view of an imminent crisis. Its object domain is soft computing using as its basic elements different methods from the areas of fuzzy logic, cognitive maps, neural networks and genetic algorithms. FCMs, more specifically, use notions borrowed from artificial intelligence and combine characteristics of both fuzzy logic and neural networks, in the form of dynamic models that describe a given political setting. The present work proposes the use of the genetically evolved certainty neuron fuzzy cognitive map (GECNFCM) as an extension of certainty neuron fuzzy cognitive maps (CNFCMs) aiming at overcoming the main weaknesses of the latter, namely the recalculation of the weights corresponding to each concept every time a new strategy is adopted. This novel technique combines CNFCMs with genetic algorithms (GAs), the advantage of which lies with their ability to offer the optimal solution without a problem-solving strategy, once the requirements are defined. Using a multiple scenario analysis we demonstrate the value of such a hybrid technique in the context of a model that reflects the political and strategic complexity of the Cyprus issue, as well as the uncertainties involved in it. The issue has been treated on a purely technical level, with distances carefully kept concerning all sides involved in it.

This paper presents Fuzzy Cognitive Maps as an approach in modeling the behavior and operation of complex systems. This technique is the fusion of the advances of the fuzzy logic and cognitive maps theories, they are fuzzy weighted directed graphs with feedback that create models that emulate the behavior of complex decision processes using fuzzy causal relations. There are some applications in diverse domains (manage, multiagent systems, etc.) and novel works (dynamical characteristics, learning procedures, etc.) to improve the performance of these systems. First the description and the methodology that this theory suggests is examined, also some ideas for using this approach in the control process area, and then the implementation of a tool based on Fuzzy Cognitive Maps is described. The application of this theory in the field of control and systems might contribute to the progress of more intelligent and independent control systems. Fuzzy Cognitive Maps have been fruitfully used in decision making and simulation of complex situation and analysis.

This paper focuses on the social function of historical public space in Turkey that has been transformed with rise of modernism. Before that, it functioned as a suburban recreational area. The increasing demand for urban lands has been led to its transformation and its function as an urban component. The historical pattern of urban space can be conserved to protect and strengthen social interactions as the key issue of urban social sustainability. With a focus on the urban design through literature review, the aim is to identify the role of social interactions on social sustainability of historical urban street in Turkey. A field survey study was conducted to observe and in-depth interviews to locally evaluate the socio-spatial design principles in Bagdat street, as a research case. Fuzzy Cognitive Map (FCM) and Cognitive Spatial Map (CSM) were used to investigate the spatial experiences of the respondents. Results indicated that the historical continuity of socio-spatial activities through the time in respect to the socio-cultural values and user satisfaction has led to enhance the sense of belongings for citizens. As a conclusion, all data were integrated to propose a socio-spatial framework for designing and developing of Turkish public street, to enhance the urban social sustainability in future communities.

Fuzzy cognitive maps (FCMs) can structure virtual worlds that change with time. A FCM links causal events, actors, values, goals, and trends in a fuzzy feedback dynamical system. A fuzzy rule defines a fuzzy patch in the input -output state-space of a system. It links commonsense knowledge with state-space geometry. A FCM connects the fuzzy rules or causal flow paths that relate events. It can guide actors in a virtual world as the actors move through a web of cause and effect and react to events and to other actors. Experts draw FCM causal pictures of the virtual world. Complex FCMs can give virtual worlds with ?>new ?> or chaotic equilibrium behavior. Simple FCMs give virtual worlds with periodic behavior. They map input states to limit-cycle equilibria. A FCM limit cycle repeats a sequence of events or a chain of actions and responses. Limit cycles can control the steady-state rhythms and patterns in a virtual world. In nested FCMs each causal concept can control its own FCM or fuzzy function approximator. Appendix A shows how an additive fuzzy system can uniformly approximate any continuous (or bounded measurable) function on a compact domain to any degree of accuracy. This gives levels of fuzzy systems that can choose goals and causal webs as well as move objects and guide actors in the webs. FCM matrices sum to give a combined FCM virtual world for any number of knowledge sources. Adaptive FCMs change their fuzzy causal web as causal patterns change and as actors act and experts state their causal knowledge. Neural learning laws change the causal rules and the limit cycles. Actors learn new patterns and reinforce old ones. In complex FCMs the user can choose the dynamical structure of the virtual world from a spectrum that ranges from mildly to wildly nonlinear. We use an adaptive FCM to model an undersea virtual world of dolphins, fish, and sharks.

Cognitive mapping was introduced as a method to model complex systems that reflects how experts or stakeholders understand cause-and-effect relationships. Later, fuzzy cognitive mapping (FCM) combined cognitive mapping with artificial neural networks (ANN), resulting in the unique capacity to capture and use qualitative data to perform quantitative analysis and study system behavior in response to changes of system elements. However, when it comes to building FCM models from qualitative data, particularly from secondary data sources, guidance for modelers is scarce. This article introduces a step by step guideline for building FCM that not only deals with causal relationships but also offers techniques to adjust the inconsistencies, and tune the granularities through parent-child relationships. It also proposes two techniques, isolated graph analysis, and receiver-only-concept analysis to investigate the completeness of the final FCM and hypothesize new connections to fill the gaps.

Fuzzy Cognitive Maps (FCMs) are a causal modelling technique. FCM models contain nodes (representing the concepts to be modelled) and directed weighted edges (representing the causal relations between the concepts). Data-driven FCM learning algorithms are an objective approach with the potential to discover the causal relations that are unknown to human experts. Learning FCM from data can be a difficult problem because the size of the solution space grows quadratically with the number of nodes in the FCM models. A data-driven learning algorithm based on Ant Colony Optimization (ACO) is proposed to develop Fuzzy Cognitive Maps (FCMs). The FCM models can be isomorphically represented as weight vectors. The objective function is to minimize the difference between the estimated response of the FCM model and the target response observed from the to-be-modelled system. An ACO algorithm with heuristic information is proposed to find the best FCM model. The performance of the ACO algorithm was tested on both randomly generated data and DREAM4 project data (publicly available in-silico gene expression data). The experiment results show that the ACO algorithm is able to learn FCMs with at least 40 nodes.

PurposeThe evaluation of readiness provides insight into the readiness of its individual components for successful accomplishment of tasks. This study aims to evaluate readiness in leagility of supply chains based on the design and analysis of fuzzy cognitive maps (FCM) and interpretive structural modeling (ISM).Design/methodology/approachOn the basis of the purpose of this study, data are gathered via the Delphi method. Moreover, FCM and ISM are also used to evaluate readiness.FindingsFindings initially demonstrate a categorization of factors influencing leagility into static and dynamic variables according to the degree of their influence derived from the resultant behavior of FCM and ISM. It is also found that evaluating readiness in leagility of supply chains with ISM and FCM was done with respect to the type and role of the study variables, which were determined within the minimum and maximum ranges of 20 to 100 per cent, respectively.Originality/valueThe evaluation of the readiness using the FCM and ISM is proved to be more efficient than other classical methods. Experimental results of the study contribute to improve readiness of leagility of supply chain as well as develop functional areas of business.

A Hybrid Modelling Framework for Ecosystem-Based Climate Change Adaptation Using System Dynamics and Bayesian Networks

CAKES-NEGO: Causal knowledge-based expert system for B2B negotiation

This survey makes a review of the most recent applications and trends on fuzzy cognitive maps (FCMs) over the past decade. FCMs are inference networks, using cyclic digraphs, for knowledge representation and reasoning. Over the past decade, FCMs have gained considerable research interest and are widely used to analyze causal complex systems, which have originated from the combination of fuzzy logic and neural networks. FCMs have been applied in diverse application domains, such as computer science, engineering, environmental sciences, behavioral sciences, medicine, business, information systems, and information technology. Their dynamic characteristics and learning capabilities make them essential for a number of tasks such as modeling, analysis, decision making, forecast, etc. Overall, this paper summarizes the current state of knowledge of the topic of FCMs. It creates an understanding of the topic for the reader by discussing the findings presented in recent research papers. A survey on FCM studies concentrated on FCM applications on diverse scientific areas, where the FCMs emerged with a high degree of applicability, has also been done during the past ten years.