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The 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.

This 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.

The 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.

This 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.

The 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.

The developing academic field of machine ethics seeks to make artificial agents safer as they become more pervasive throughout society. In contrast to computer ethics, machine ethics is concerned with the behavior of machines toward human users and other machines. This study aims to use an action-based ethical theory founded on the combinational aspects of deontological and teleological theories of ethics in the construction of an artificial moral agent (AMA).

The decision results derived by the AMA are acquired via fuzzy logic interpretation of the relative values of the steady-state simulations of the corresponding rule-based fuzzy cognitive map (RBFCM).

Through the use of RBFCMs, the following paper illustrates the possibility of incorporating ethical components into machines, where latent semantic analysis (LSA) and RBFCMs can be used to model dynamic and complex situations, and to provide abilities in acquiring causal knowledge.

This approach is especially appropriate for data-poor and uncertain situations common in ethics. Nonetheless, to ensure that a machine with an ethical component can function autonomously in the world, research in artificial intelligence will need to further investigate the representation and determination of ethical principles, the incorporation of these ethical principles into a system’s decision procedure, ethical decision-making with incomplete and uncertain knowledge, the explanation for decisions made using ethical principles and the evaluation of systems that act based upon ethical principles.

To date, the conducted research has contributed to a theoretical foundation for machine ethics through exploration of the rationale and the feasibility of adding an ethical dimension to machines. Further, the constructed AMA illustrates the possibility of utilizing an action-based ethical theory that provides guidance in ethical decision-making according to the precepts of its respective duties. The use of LSA illustrates their powerful capabilities in understanding text and their potential application as information retrieval systems in AMAs. The use of cognitive maps provides an approach and a decision procedure for resolving conflicts between different duties.

This paper suggests that cognitive maps could be used in AMAs as tools for meta-analysis, where comparisons regarding multiple ethical principles and duties can be examined and considered. With cognitive mapping, complex and abstract variables that cannot easily be measured but are important to decision-making can be modeled. This approach is especially appropriate for data-poor and uncertain situations common in ethics.

The aim of this study is to present a model for service supply chain performance evaluation (SSCPE) based on fuzzy cognitive mapping (FCM).

Despite the unique nature of services, which distinguishes them from goods, the performance evaluation of service supply chains (SSCs) has still not received adequate attention from researchers or practitioners. With the help of in-depth interviews conducted with insurance industry experts in Iran, the performance measures of SSCs are defined, and the relevance between the performance measures, which are based upon the knowledge of experts in this field, are presented as a fuzzy cognitive map.

By blocking the aggregate map of experts, a model for SSCPE is developed that discusses performance measures and metrics of strategic, tactical and operational SSC processes, such as service-providing management, service-enabling management, market management, cash-flow management, customer-relationship management, supplier-relationship management, knowledge- and information-flow management and risk management. Indexes of FCM models indicate that the supply chain service quality and strategy blocks have the most importance compared with others.

The paper develops an original SSCPE model that can be used as a basis for SSC improvement and develops an excellence model for SSC.

The purpose of this paper is to present the common use of Delphi method, analytic hierarchy process (AHP) and dynamic fuzzy cognitive maps (DFCM) for development of future scenarios in correlation with climate warnings.

The developed global model is based on a cognitive map composed of 28 factors. Among these factors two are independent, namely human consciousness and living standard. These two factors represent the most important levers of control within the designed model. Two approaches to scenario development have been applied, so that the comparison of the results between conventional fuzzy method and dynamic fuzzy method could give some further insights about global climate development and sustainability, as well as about differences between the two approaches.

Results of the first model (no‐time‐delay in cause‐effect relation) show that in general there is no absolute convergence to a single vector C(tn+1) for various initial values for C(0). But an interesting phenomenon of cyclical convergence of the model was observed, which coincides with the empirical research results that observe cycles in the climate change awareness and environmental impact. In the second model, time‐delay in the adjacency matrix was considered during the iteration. An interesting phenomenon of cyclical convergence of this model was observed as well. In two antimetric cases the authors have observed the phenomenon of two cycles, where the second, final cycle is developing after the occurrence of the first.

The paper can be of interest for foresight practitioners and policy makers who want to employ a hybrid approach to scenario development and use it as a learning device.

The paper presents a different approach, based on dynamic fuzzy cognitive maps, to future scenarios development related to climate warning.

The purpose of the chapter is to study the possibility of applying modern intellectual methods and technologies of decision-making for managing complex poorly structured systems.

The methodology of the chapter includes fuzzy cognitive approach, analysis of causal connections, dynamic modeling with application of the impulse processes tools, sustainability analysis of the studied system, and scenarios analysis.

The authors offer fuzzy cognitive approach to modeling the risk management system of commercial Bank POS-loaning processes. The simulated system is represented as a fuzzy-oriented weighted multigraph with a pulse action transmitted through it. The modeling process is implemented in the form of successive execution of the following stages: determining the goal, formation of fuzzy cognitive map, dynamic modeling with application of impulse processes tools, and scenarios analysis of development situation and selection the best.

The developed model of management system is a basis for analyzing the tendencies of various situations development that appear during work of banks in the express loaning segment. It allows forecasting and modeling the strategies of behavior as a reaction to external influences and to determine trajectories of management that allow reducing the internal risks of commercial Bank POS-loaning processes. Fuzzy cognitive approach is an effective tool for decision support during risk management in activities of a commercial bank and could be used for modeling and analysis of functioning and other poorly structured socioeconomic systems.

The purpose of the paper is to present the common use of the Delphi method, the analytical hierarchy process (AHP) and dynamic fuzzy cognitive maps for the development of a future scenario in correlation with climate warming.

A combination of the Delphi method, the analytical hierarchy process (AHP) and dynamic fuzzy cognitive maps was employed to construct a future scenario as a learning device.

Based on the data, the future is not going to be so bad but also not very attractive even if it is assumed that human consciousness about global warming issues will remain high. After a few years or so it will be possible to stop negative climate processes but not possible to fix the problem in the sense that the situation we have now can be substantially improved.

This paper is of interest for foresight practitioners and policy makers who want to employ a hybrid approach to scenario development.

The paper is the first to try to use a combination of different methods for scenario development related to climate change.

This chapter discusses the application of fuzzy cognitive map (FCM) modelling to construction management (CM) challenges and problems. It focuses on the critical issue of managing the complexity and uncertainty inherent in CM by providing a new intelligent layer that enhances classical approaches to construction modelling and management. It investigates how the myriad types of internal and external factors affecting the feasibility and performance of construction projects can be modelled using a fuzzy hybrid method that explores the complex relationships among many contributing factors and assesses and evaluates their impacts on past and future projects. This chapter proposes a hybrid modelling approach in the traditional context of cost, schedule and risk management and describes how augmenting and enhancing existing state-of-the-art tools and processes in CM can assist construction managers. This chapter provides a background on the theory of FCMs, presents foundational and current research, and explains how to apply this approach in the CM domain. This chapter also provides a detailed description of how to develop, modify and employ interactive models to specific CM challenges and problems. It includes a customisable, interactive base model and demonstrates how the model has been applied to specific CM events and issues. Examples are presented that highlight the interplay between project-specific goals and characteristics and the way these impact the interrelated and often opposing triad of cost, schedule and risk. The presented examples and practical applications make this state-of-the-art approach useful to both academic and industry practitioners.

The purpose of this paper is to explore a very new approach of creating scenarios with fuzzy cognitive maps (FCM); specifically to create scenarios for wind energy deployment.

In this paper a small exercise is conducted to illustrate the usefulness of FCM‐based scenarios.

Scenario‐based planning is often used in technology management and scenarios are generated to cater for uncertainty and facilitate development of responsive and robust strategic plans. Scenario analysis helps to identify different alternatives of the future state of technologies, needs, policies and environment. Scenario planning helps to overcome thinking limitations by presenting multiple futures. FCMs are based on causal cognitive maps and combine the benefits of both qualitative and quantitative analysis. Domain knowledge of multiple experts can be integrated into an FCM to present collective knowledge base.

This is a conceptual paper exploring use of fuzzy cognitive maps to create scenarios for wind energy deployment. The paper provides scenarios and a way to integrate them into technology roadmaps.

Conventional rule‐based systems are insufficient for description of complex dynamic systems requiring nontrivial decision procedures. Fuzzy cognitive maps seem to be convenient to overcome these limitations. However, they lack ability of self‐learning and therefore some adaptation approaches are needed. The purpose of this paper is both to show the use of fuzzy cognitive maps for such systems and to present migration algorithms as convenient adaptation means.

Some problems of a complex dynamic system description by knowledge‐based means are discussed. Fuzzy cognitive maps are presented as a possible way to solve these problems followed by description of migration algorithms as their adaptation means. Their use is clarified on an example of the so‐called parking problem based on path planning using a graph search algorithm and a traffic simulation system.

After series of simulations the reality of the proposed system and selected methods with their modifications was proved. It has shown the robustness of the presented solution under circumstances of uncertainty, too.

The paper points to stability investigation of the proposed approach introducing uncertainties into the traffic simulation system to take into account, e.g. unexpected events. Further, a possibility of developing a linguistic information retrieval system is mentioned.

The proposed approach can find various implementations not only in planning tasks but also in robotic navigation and multi‐agent applications in general. In addition, it suggests possibilities of knowledge‐based systems, directly using human‐like approaches, to areas of decision making under uncertainties and contradictories.

An new modification of migration algorithms for adaptation of parameters for fuzzy cognitive maps is introduced and compared to other known self‐learning methods. Further, the concept of a traffic simulation system for path planning is presented.

The purpose of the paper is to bring attention to documentary credits and the efforts to reduce debt obligations in credit history is recognized as an important source of uncommitted bank earnings. Credit risk has a significant impact on the stability of the banking system. This paper identifies the types of credit risk in the banking supply chain.

The authors model the types of credit risk using the intuitive fuzzy failure modes and effects analysis (IFMEA) and intuitive fuzzy cognitive mapping. The population of the study that is needed for the interviews and expert panels comprises senior managers and experts of a leading bank in Iran. The respondents are experienced in credit and banking risk and were selected through judgment sampling and snowballing.

The findings suggest that reducing the risks of the foreign letters of credit contracts can mitigate the risk in the agricultural sector, the specific risks of rent-to-own contracts, the risk of the long-term facilities and the specific risk of the domestic letter of credit contracts.

This research investigates Iran Tejart Bank’s credit risk, formulates a model of the types of credit risk present and analyzes them using the intuitive fuzzy failure modes and effects analysis and intuitive fuzzy cognitive map. Through this credit risk model, one can then facilitate risk management for better financial stability. Also, the model can be used to evaluate the risk indicators.