Fuzzy cognitive strategic maps in business process performance measurement

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.

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.

This paper describes a methodology for the development of a Proactive Balanced Scorecard (PBSCM). The Balanced Scorecard is one of the most popular approaches developed in the field of performance measurement. However, in spite of its reputation, there are issues that require further research. The present research addresses the problems of the Balanced Scorecard by utilizing the soft computing characteristics of Fuzzy Cognitive Maps (FCMs). By using FCMs, the proposed methodology generates a dynamic network of interconnected Key Performance Indicators (KPI), simulates each KPI with imprecise relationships and quantifies the impact of each KPI to other KPIs in order to adjust targets of performance.KeywordsPerformance MeasurementBalanced ScorecardFuzzy Cognitive MapsSimulation

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

PDF HTML阅读 XML下载 导出引用 引用提醒 基于模糊认知图的生态风险管理探究 DOI: 10.5846/stxb201301280173 作者: 作者单位: 山西大学黄土高原研究所,山西大学环境与资源学院,山西大学环境与资源学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41101558，41271513）；山西省研究生创新项目（020752901007） Exploration of ecosystem risk management based on fuzzy cognitive map Author: Affiliation: Institute of Loess Plateau,Shanxi University,Department of environment and resources,Shanxi University,Department of environment and resources,Shanxi University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:生态风险管理具有不稳定性和不确定性，如何获取和调整不同利益群体对于生态风险和环境管理的看法并把它们定量表示出来、协调其中存在的冲突是生态系统管理亟待解决的问题。以煤矿区生态系统为研究对象，探讨了模糊认知图方法在获取利益相关方认知上的应用，并基于模糊关联矩阵，采用神经网络模型进行不同生态风险管理政策的情景模拟。研究结果为：大气污染和水环境破坏是所有利益相关者最关注的问题，国家政策是最核心的变量；不同利益相关者之间的不同点在于，管理者较多地关注国家政策及煤矿开采给国家带来的财政收入与居民生活水平的提高，而当地居民和矿工更关注自己的身体健康和人身安全。通过对煤矿区生态风险管理的政策模拟，可看出要减少大气污染，政府要加大对企业的治理力度，从保护耕地、林地，增加植被覆盖度，提高生物多样性出发；另外，如果政府制定相关政策提高了次级产物的利用率，矿区生态系统的风险也会大大降低。结果表明：模糊认知图方法用在生态系统管理中，可以真实反映利益相关者对生态系统的风险认知以及风险之间的因果联系强度。神经网络模型用在生态风险管理的情景分析中，可以帮助矿区生态风险管理者选择不同的方案以达到管理目标，为在煤矿区建立资源、环境和人口协调发展的生态环境管理机制奠定了基础，从而为生态风险管理提供了一个新视角。 Abstract:There are the natures of instability and uncertainty in ecological risk management. Each stakeholder's cognition plays a crucial role in it. For this reason, the coal-mining environment managers need to choose appropriate measures to take stakeholders' interests in account in ecosystem management. But the crucial problems to be solved are how to get and adjust different stakeholders' perspectives on ecological risk and environmental management, and how to quantify their perspectives, and coordinate their conflicts. This research paper focused on coal-mining ecosystem, explored the application of fuzzy cognitive map (FCM) on getting stakeholders' perspectives and graph theory indices on quantifying them, and simulated the different ecological risk management policy options using FCM-based artificial neural network (ANN). The Fuzzy Cognitive Mapping was applied to develop a participatory environmental management model for the typical coal-mining area in Yangquan. The ANN was used to conduct simulations combining the initial state of variables and the adjacency matrix getting from the social condensed map, The results showed that different stakeholders' cognition were distinctly different, managers emphasized national policy and economic interests brought by coal-mining activity, while engineers stressed living standard more, whereas mine workers and local residents paid more attention to the work's safety and their health condition. But it is noteworthy that‘air pollution’and ‘water damage’were the variables which were mentioned by almost all of them. It revealed the severity of ‘air pollution’and‘water damage’. The most central variables in the social condensed map were‘national policy’and‘health’, as well as in the four stakeholder groups. The simulations of policy option reflected that when local governments and enterprises make management decisions in coal-mining ecosystem, they should place an emphasis on protecting farmland, increasing vegetation coverage, improving energy efficiency and recycling waste so as to decrease air pollution, protect water environment and reduce resources waste. It is helpful for learning how different choices affect the coal-mining ecosystem. The results revealed that FCM analysis and ANN simulations could provide useful information for the development of environmental management planning. FCM analysis could help us to understand the varying attitudes among stakeholder groups as they considered environmental issues affecting the ecosystem. In addition, the policy option simulations based on ANN can help the coal-mining environmental managers to choose proper measures to achieve management goals, so as to lay the foundation of ecological environment management mechanism that can harmonize the development of resource, environment and population. Throughout the full text, the FCM method has the advantages: (1) Comparing with the traditional questionnaires, semi-structured interview respects more individual's perception and leaves more space to interviewees. (2) Comparing with the traditional evaluating factor system, the FCM method can get a more accurate causal relationship among each factor. (3) Getting knowledge easily, combining qualitative analysis and quantitative calculation, and expressing cognition and inference well. (4) Getting multi-stakeholders' FCMs and comparing their different perceptions is more convenient. Moreover, this paper expanded the environment management research methods and provided some new ideas and approaches with regional ecological risk management theoretically. 参考文献 相似文献 引证文献

Statistical analysis of past accidents in maritime may demonstrate the trends for certain contributing factors in accidents, however, there is a lack of a suitable technique to model the complex interrelations between these factors. Due to aforementioned complex interrelations and insufficient information stored in accident databases, it was not possible to understand the importance of each factor in accidents, which prevented researchers from considering these factors in risk assessments. Therefore, there is a need for a capable technique to estimate the importance of each factor. The results of such a technique can be used to inform risk assessments and predict the effectiveness of risk control options. Thus, this study introduces a new technique for Marine Accident Learning with Fuzzy Cognitive Maps (MALFCMs). The novelty of MALFCM is the application of fuzzy cognitive maps (FCMs) to model the relationships of maritime accident contributors by directly learning from an accident database as well as having the ability to combine expert opinion. As each fuzzy cognitive map is derived from real occurrences supported by expert opinion, the results can be considered more objective. Thus, MALFCM may overcome the main disadvantage of fuzzy cognitive maps by eliminating or controlling the subjectivity in results.•A novel MALFCM method to weight human-contributing factors into maritime accidents has been developed.•With MALFCM method the main disadvantage of traditional FCMs is overcome.•The MALFCM method can produce logical results even by solely using information from historical data in the absence of expert judgement.

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.

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.

Coconut is one of the major perennial food crops that has a long development phase of 44 months. The climatic and seasonal variations affect all stages of coconut's long development cycle. Besides, the soil composition also plays a vital role in deciding the coconut yield behavior. The present study is focused on categorizing the coconut production level for the given set of agro-climatic conditions using the methodology of fuzzy cognitive map (FCM) enhanced by its learning capabilities. Additionally, an attempt is made to study the impact of climatic variations and weather parameters on the coconut yield behavior using the reasoning capabilities of FCM. Real coconut field data of different seasons for the period from 2009 to 2013 of Kerala state's Malabar region were used for training and evaluation of the FCM. The present work demonstrates the classification and prediction capabilities of FCM for the described precision agriculture application, with the two most known and efficient FCM learning approaches, viz., nonlinear Hebbian (NHL) and data-driven nonlinear Hebbian (DDNHL). The DDNHL-FCM offers an overall classification accuracy of 96 %. The various case studies furnished in the paper demonstrate the power of NHL-FCM in effectively reasoning new knowledge pertaining to the presented precision agriculture application.

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.

The purposes of this research are to find a model to forecast the electricity consumption in a household based on fuzzy cognitive map (FCM) prediction capabilities. The data analysis has been performed with three different learning algorithms based on the fuzzy cognitive map model which are (a) the multi-step gradient method (MGM), (b) the real coded genetic algorithm (RCGA) and (c) the structure optimization genetic algorithm (SOGA) as a new improvement of the RCGA able to handle the model's complexity investigated in our study. The suitable forecasting methods and two different forecasting periods were chosen by considering the smallest value of MSE (Mean Square Error) and RMSE (Root Mean Square Error), respectively. The proposed FCM-based prediction algorithms were compared with known statistical methods such as ARIMA and artificial intelligent methods such as ANNs and neuro-fuzzy models. The experiments conducted in this study showed that the multi gradient-based algorithm for FCM learning was the most efficient with respect to the accuracy of prediction expressed by the well-known used errors in the two selected forecasting periods.

Fuzzy cognitive maps (FCMs) are widely used for modeling complex systems and supporting explainable AI. However, traditional FCMs cannot effectively represent indeterminacy that arises from incomplete or conflicting information. Neutrosophic cognitive maps (NCMs) extended FCMs to handle such uncertainty, but their use of a single indeterminate symbol (I) limits their ability to model complex, real-world interactions. This paper introduces Refined neutrosophic cognitive maps (RNCMs), a novel framework that combines single-valued neutrosophic sets (SVNSs) for representing concept states with a new refined dual neutrosophic set (RDNS) for modeling causal relationships. RNCMs capture five distinct causal relationships—positive to positive, positive to negative, negative to positive, negative to negative, and indeterminate to indeterminate—allowing a richer and more precise modeling of dual and uncertain relationships than FCMs or NCMs. A case study on students’ academic performance demonstrates that RNCMs reproduce the outputs of FCMs and NCMs while uncovering additional causal nuances, such as polarity shifts and the distinction between genuine indeterminacy and measurable influence. RNCMs also reveal when positive states in certain factors lead to negative outcomes—insights that earlier models fail to capture. Although the model requires higher computational effort, it remains robust across thresholds and provides better explainability and interpretability. Overall, RNCMs advance cognitive map modeling by integrating refined uncertainty handling with dual causal relationships, making them a powerful and explainable tool for complex decision-making systems.

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.

Modelling Business and Management Systems Using Fuzzy Cognitive Maps: A Critical Overview