An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms

This is the post-print version of the final paper published in Industrial Marketing Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their cause–effect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.Ministry of Education, Science and Technology (Korea

A Fuzzy Approach to the Synthesis of Cognitive Maps for Modeling Decision Making in Complex Systems

The object of this study is fuzzy cognitive modeling as a means of studying semistructured socio-economic systems. The features of constructing cognitive maps, providing the ability to choose management decisions in complex semistructured socio-economic systems, are described. It is shown that further improvement of technologies necessary for developing decision support systems and their practical use is still relevant. This work aimed to improve the accuracy of cognitive modeling of semistructured systems based on a fuzzy cognitive map of structuring nonformalized situations (MSNS) with the evaluation of root-mean-square error (RMSE) and mean average squared error (MASE) coefficients. In order to achieve the goal, the following main methods were used: systems analysis methods, fuzzy logic and fuzzy sets theory postulates, theory of integral wavelet transform, correlation and autocorrelation analyses. As a result, a new methodology for constructing MSNS was proposed—a map of structuring nonformalized situations that combines the positive properties of previous fuzzy cognitive maps. The solution of modeling problems based on this methodology should increase the reliability and quality of analysis and modeling of semistructured systems and processes under uncertainty. The analysis using open datasets proved that compared to the classical ARIMA, SVR, MLP, and Fuzzy time series models, our proposed model provides better performance in terms of MASE and RMSE metrics, which confirms its advantage. Thus, it is advisable to use our proposed algorithm in the future as a mathematical basis for developing software tools for the analysis and modeling of problems in semistructured systems and processes. Doi: 10.28991/ESJ-2022-06-02-012 Full Text: PD

Developing Fuzzy Cognitive Mapping Techniques for Consequence Analysis of Second and Third Order Effects

The Defense Threat Reduction Agency (DTRA) is the Department of Defense’s (DOD) official Combat Support Agency for countering weapons of mass destruction (WMD). DTRA focuses on WMD and mitigating the consequences of a chemical, biological, radiological, nuclear and high yield explosive threat (CBRNE). The initial direct effects of a CBRNE incident are well defined and documented; however, the second and third order effect’s are complex and not thoroughly understood or documented.  Consequence analysis is the practice of analyzing the effects of major events such as a CBRNE event and can assist in predicting the second and third order effects.  Currently there is no method to predict or analyze the second and third order effects of CBRNE events. This research focused on identifying the entities associated with a CBRNE event initially.  The use of experts and surveys developed an exhaustive list of entities and associated realtionships.  The follow-on research focused on the type and strength of the entity relationships.  Next, Fuzzy Cognitive Mapping (FCM) techniques identify and evaluate the complex relationships of the second and third order effects.   Using a mind mapping computer program, FCM techniques produced second and third order effect relationships.  The final product provided a solid first attempt at analyzing a CBRNE event and the associated second and third order effects.  Subsequent research will require greater effort to employ system dynamics techniques to enhance the product and develop a more thorough model

Perceived key determinants of payment instrument usage: a fuzzy cognitive mapping-based approach

The recent economic climate has had direct repercussions on people’s daily lives. This has occurred not only in how they use payment instruments, but is also evinced in new concerns adjacent to technological advances, people’s safety and the credibility of financial institutions. In this regard, the banking sector has had a crucial role in countries’ economic development, making it increasingly important to understand how the banking system operates and what payment instruments are available to users. Relying on specialized literature and the application of fuzzy cognitive mapping, this study aims to understand the cause-and-effect relationships between customers’ preference factors in using payment instruments. The results show that usability aspects and safety concerns constitute the factors which users pay more attention to. Strengths and limitations of our proposal are also discussed.info:eu-repo/semantics/publishedVersio

Cluster Data Analysis with a Fuzzy Equivalence Relation to Substantiate a Medical Diagnosis

This study aims to develop a methodology for the justification of medical diagnostic decisions based on the clustering of large volumes of statistical information stored in decision support systems. This aim is relevant since the analyzed medical data are often incomplete and inaccurate, negatively affecting the correctness of medical diagnosis and the subsequent choice of the most effective treatment actions. Clustering is an effective mathematical tool for selecting useful information under conditions of initial data uncertainty. The analysis showed that the most appropriate algorithm to solve the problem is based on fuzzy clustering and fuzzy equivalence relation. The methods of the present study are based on the use of this algorithm forming the technique of analyzing large volumes of medical data due to prepare a rationale for making medical diagnostic decisions. The proposed methodology involves the sequential implementation of the following procedures: preliminary data preparation, selecting the purpose of cluster data analysis, determining the form of results presentation, data normalization, selection of criteria for assessing the quality of the solution, application of fuzzy data clustering, evaluation of the sample, results and their use in further work. Fuzzy clustering quality evaluation criteria include partition coefficient, entropy separation criterion, separation efficiency ratio, and cluster power criterion. The novelty of the results of this article is related to the fact that the proposed methodology makes it possible to work with clusters of arbitrary shape and missing centers, which is impossible when using universal algorithms. Doi: 10.28991/esj-2021-01305 Full Text: PD

Identifying the Components and Interrelationships of Smart Cities in Indonesia: Supporting Policymaking via Fuzzy Cognitive Systems

Multiple Indonesian cities currently aim to qualify as “smart cities.” Previous research on defining smart cities (e.g., the implementation-oriented maturity model) tends to focus on components over interrelationships, is challenging to apply to a specific context such as Indonesia, and offers limited support for policy-relevant questions. In this paper, we propose to address these shortcomings to support policymakers in identifying concrete action plans in Indonesia specifically. Our approach clarifies interrelationships for the context of use and supports structural (e.g., what aspects of a “smart city” are impacted by an intervention?) as well as what-if policy questions. We started with a systems\u27 science approach to developing a cognitive map of the components and their interrelationships, as is increasingly done in participatory modeling and particularly in socio-ecological management. We transformed semi-structured interviews of 10 Indonesian experts into maps and assembled them to create the first comprehensive smart cities cognitive map for Indonesia, totaling 52 concepts and 98 relationships. While a cognitive map already provides support for decision-making (e.g., by identifying loops in the system), it is only conceptual and thus cannot form predictions. Consequently, we extended our cognitive map into a fuzzy cognitive map (FCM), whose inference abilities allow to examine the dynamic response of an indicator (e.g., “smart city”) in response to different interventions. As fuzzy cognitive maps include the strengths of interrelationships but not the notion of time, future research may refine our model using system dynamics. This refinement would support policymakers in investigating when to conduct and/or evaluate an intervention

Perceived key determinants of payment instrument usage: A fuzzy cognitive mapping-based approach

The recent economic climate has had direct repercussions on people’s daily lives. This has occurred not only in how they use payment instruments, but is also evinced in new concerns adjacent to technological advances, security and the credibility of financial institutions. In this regard, the banking sector has had a crucial role in countries’ economic development, making it increasingly important to understand how the banking system operates and what payment instruments are available to users. Relying on specialized literature and the application of fuzzy cognitive mapping, the current study aims to understand the cause-and-effect relationships between customers’ preference factors in using payment instruments. The results show that usability aspects and security issues constitute the factors which users pay more attention to. Strengths and limitations of the study are also discussed.info:eu-repo/semantics/publishedVersio

Software Technology to Develop Large-Scale Self-Adaptive Systems: Accelerating Agent-Based Models and Fuzzy Cognitive Maps via CUDA

Agent-Based Models (ABMs) have long served to study self-adaptive systems and the emergence of population-wide patterns from simple rules applied to individuals. Recently, the rules for each agent have been expressed using a Fuzzy Cognitive Map (FCM), which is elicited from a subject-matter expert. This provides a transparent and participatory process to externalize the `mental model' of an expert and directly embed it into agents. However, software technology has been lacking to support such hybrid ABM/FCM models at scale, which has drastically limited the scope of applications and the ability of researchers to study emergent phenomena over large populations. In this paper, we designed and implemented the first open-source library that automatically accelerates ABM/FCM models by leveraging the CUDA cores available in a Graphical Processing Unit. We demonstrate the correctness and scaling of our library on a case study as well as across different networks representing agent interactions