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Fuzzy Multicriteria Decision-Making: Models, Algorithms and Applications addresses theoretical and practical gaps in considering uncertainty and multicriteria factors encountered in the design, planning, and control of complex systems.

The challenges posed to our environment by climate change are immense and are increasing every day. Its impacts on ecosystems, weather patterns, and human health are extensive. It is imperative that we tackle climate change to save the environment and ensure a sustainable future. It is essential to implement effective climate change mitigation and control strategies to conserve natural resources and improve global well-being. Therefore, we develop a novel decision making model based on the fuzzy credibility graph to select the best climate change mitigation strategies. In this article, the fuzzy credibility graph, the direct product of fuzzy credibility graphs, the degree of a vertex, and the total degree of a vertex are defined first. After that, we apply the proposed decision making model to select the best climate change mitigation strategy. For this, we collect the expert information and the fuzzy credibility edges information about the climate change mitigation strategies, and process the proposed model to compute the relative closeness of the alternatives and identify the most suitable climate change mitigation strategy. To evaluate the performance of the proposed model, we compare it with existing decision making methods. The results demonstrate that our model provides accurate and effective decision support. Additionally, we use Spearman’s correlation coefficient to verify the consistency of the rankings. The comparative analysis confirms the validity and reliability of the proposed model in supporting climate mitigation decisions.

In this modern world, 3D printing technology plays a very important role in the manufacturing sector. It can be found easily in recent decades, be the increasing use of 3D printing in many fields and including Fusion Deposition Modeling (FDM) technology. This research paper is for an Indian electrical switch product-based manufacturing Micro Small and Medium Enterprises (MSME) company, to select the optimum set of printing parameters of the FDM machine for producing a high-quality final product in less time. To this end, the fifteen pieces of ASTM D63 8 tensile specimen were fabricated with a modified cluster of fourteen printing parameters for ensuring the mechanical property with less production time by the results of fabrication time, tensile test, and microstructure analysis. Moreover, the Design of Experiments (DoE) has been used for the analysis of the tensile strength and Field Emission Scanning Electron Microscope (FESEM) equipment has been used for the analysis of microstructures. Finally, the optimum printing/process parameters have been suggested to the MSME company based on the experimental results.

Photovoltaic electricity generation is key to achieving deep decarbonization with a high degree of electrification. It is predicted that the energy sector will reduce carbon dioxide by producing electricity mainly from photovoltaic (PV) power. Although dynamic development of the implementation of photovoltaic panels has been observed, their choice considering customer specificity is still a problem. Therefore, the purpose of this study is to propose the model of choice photovoltaic panels considering customers’ expectations. It can support the choice of a photovoltaic panel of a certain quality (satisfaction of concrete customer) in combination with the cost of its purchase. The proposed model includes acquiring and then processing customers’ expectations into technical criteria, while simultaneously considering the weighting of these criteria. It is realized in a standardized way, i.e., the zero-unitarization method (MUZ), after which normalized values of the quality of the photovoltaic panels’ criteria are obtained. In turn, the quality of these products is estimated by the weighted sum model (WSM) and then integrated with purchase cost in qualitative cost analysis (AKJ). As a result, using the scale of relative states, it is possible to categorize customer satisfaction from indicating qualitative cost and selecting the photovoltaic panel expected by customers (the most satisfactory). The effectiveness of the model was demonstrated by a sensitivity analysis, after which the key PV criteria were indicated. The proposed model is intended for any entity who selects a photovoltaic panel for customers. The computerization of calculations may contribute to its utilitarian dissemination.

An entrepreneur is likely to make decisions using a personal cognitive framework formed over time through the interactions of several contextual and psychological variables, including personal characteristics, traits, and attitudes (CTAs). While past studies have demonstrated the importance of CTAs in new venture creation, they are far from providing a holistic understanding of the role of CTAs in entrepreneurial decision-making (EDM). This review explores the role of specific CTAs on EDM in the context of the early-stage new venture. The authors reviewed the literature between 1990 and 2021 with the following keywords appearing in the title, abstract, and keywords in published journal articles: “entrepreneurial decision making” and “characteristic” or “trait” or “attitude” or “effectuation” or “causation” or “effectual” or “causal”. The authors specifically reviewed the articles published in the Australian Business Deans Council (ABDC) journals. By exploring various CTAs, diverse EDM frameworks, and contextual variations, we aim to provide a comprehensive and nuanced understanding of how individual attributes impact entrepreneurial decision-making.

The economics literature is replete with examples of monotone comparative statics; that is, scenarios where optimal decisions or equilibria in a parameterized collection of models vary monotonically with the parameter. Most of these examples are manifestations of complementarity, with a common explicit or implicit theoretical basis in properties of a super-modular function on a lattice. Supermodular functions yield a characterization for complementarity and extend the notion of complementarity to a general setting that is a natural mathematical context for studying complementarity and monotone comparative statics. Concepts and results related to supermodularity and monotone comparative statics constitute a new and important formal step in the long line of economics literature on complementarity. This monograph links complementarity to powerful concepts and results involving supermodular functions on lattices and focuses on analyses and issues related to monotone comparative statics. Don Topkis, who is known for his seminal contributions to this area, here presents a self-contained and up-to-date view of this field, including many new results, to scholars interested in economic theory and its applications as well as to those in related disciplines. The emphasis is on methodology. The book systematically develops a comprehensive, integrated theory pertaining to supermodularity, complementarity, and monotone comparative statics. It then applies that theory in the analysis of many diverse economic models formulated as decision problems, noncooperative games, and cooperative games.

Artificial Intelligence (AI) startups possess four key attributes; being small enterprises, adopting AI technology, undergoing digital transformation, and using big data systems to enhance their competitiveness. This study aims to identify the key influencing factors needed to enhance the competitiveness of AI technology-based startups and to suggest a decision-making model to improve the technology and business competitiveness of AI startups in the digital era. To achieve this, the hierarchy concept framework was built with four evaluation areas based on the mechanism-based view theory, and the 16 evaluation factors that can influence were identified through existing literature, combining factors related to the digital transformation, technological application, and business competitiveness of the startups. These factors were analyzed using the Analytic Hierarchy Process (AHP) by the survey, targeting experts in South Korea. The analysis results indicate that the subject area was the most crucial for the business competitiveness of AI startups. It was also revealed that the subject's strategic mind is the most significant factor to AI startups' success. In the case of two control groups, categorized as 'AI experts' and 'startup experts', AI experts chose the subject as the most important area, whereas startup experts selected the environment, and significant differences were observed in all other factors. The results of this study will provide implications for strengthening the business competitiveness of AI startups and factors important for the growth of AI startups in this era.

Much of our understanding of human thinking is based on probabilistic models. This innovative book by Jerome R. Busemeyer and Peter D. Bruza argues that, actually, the underlying mathematical structures from quantum theory provide a much better account of human thinking than traditional models. They introduce the foundations for modeling probabilistic-dynamic systems using two aspects of quantum theory. The first, 'contextuality', is a way to understand interference effects found with inferences and decisions under conditions of uncertainty. The second, 'quantum entanglement', allows cognitive phenomena to be modeled in non-reductionist ways. Employing these principles drawn from quantum theory allows us to view human cognition and decision in a totally new light. Introducing the basic principles in an easy-to-follow way, this book does not assume a physics background or a quantum brain and comes complete with a tutorial and fully worked-out applications in important areas of cognition and decision.

Conservation management is of increasing importance in ecology as most ecosystems nowadays are essentially managed ecosystems. Conservation managers work within a political-ecological system when they develop and attempt to implement a conservation plan that is designed to meet particular conservation goals. In this article, we develop a decision support tool that can identify a conservation policy for a managed wildlife population that is both sustainable and politically feasible. Part of our tool consists of a simulation model composed of interacting influence diagrams. We build, fit, and use our tool on the case of rhino horn trafficking between South Africa and Asia. Using these diagrams, we show how a rhino poacher’s belief system can be modified by such a policy and locate it in a perceived risks-benefits space before and after policy implementation. We statistically fit our model to observations on group actions and rhino abundance. We then use this fitted model to compute a politically feasible conservation policy.

Purpose Digital transformation (DT) leverages digital technologies to change current processes and introduce new processes in any organisation’s business model, customer/user experience and operational processes (DT pillars). Artificial intelligence (AI) plays a significant role in achieving DT. As DT is touching each sphere of humanity, AI led DT is raising many fundamental questions. These questions raise concerns for the systems deployed, how they should behave, what risks they carry, the monitoring and evaluation control we have in hand, etc. These issues call for the need to integrate ethics in AI led DT. The purpose of this study is to develop an “AI led ethical digital transformation framework”. Design/methodology/approach Based on the literature survey, various existing business ethics decision-making models were synthesised. The authors mapped essential characteristics such as intensity and the individual, organisational and opportunity factors of ethics models with the proposed AI led ethical DT. The DT framework is evaluated using a thematic analysis of 23 expert interviews with relevant AI ethics personas from industry and society. The qualitative data of the interviews and opinion data has been analysed using MAXQDA software. Findings The authors have explored how AI can drive the ethical DT framework and have identified the core constituents of developing an AI led ethical DT framework. Backed by established ethical theories, the paper presents how DT pillars are related and sequenced to ethical factors. This research provides the potential to examine theoretically sequenced ethical factors with practical DT pillars. Originality/value The study establishes deduced and induced ethical value codes based on thematic analysis to develop guidelines for the pursuit of ethical DT. The authors identify four unique induced themes, namely, corporate social responsibility, perceived value, standard benchmarking and learning willingness. The comprehensive findings of this research, supported by a robust theoretical background, have substantial implications for academic research and corporate applicability. The proposed AI led ethical DT framework is unique and can be used for integrated social, technological and economic ethical research.