Sustainability issues have gained more importance in contemporary globalization, pushing decision makers to find a systematic mathematical approach to conduct analyses of this real-world problem. The growing complexity in modern social-economics or engineering environments or systems has forced researchers to solve complicated problems by using multi-criteria decision-making (MCDM) approaches. However, traditional MCDM research mainly focuses on reaching the highest economic value or efficiency, and issues related to sustainability are still not closely explored. Advanced Multi-Criteria Decision Making for Addressing Complex Sustainability Issues discusses and addresses the challenges in the implementation of decision-making models in the context of green and sustainable engineering, criteria identification, quantification, comparison, selection, and analysis in the context of manufacturing, supply chain, transportation, and energy sectors. All academic communities in the areas of management, economics, business sciences, mechanical, and manufacturing technologies are able to use, apply, and implement the models presented in this book. It is intended for researchers, manufacturers, engineers, managers, industry professionals, academicians, and students.

This book is a methodological guide intended for those who wish to better understand how to conduct research in the education and training sciences. It is organized into three main parts. The first part deals with postures, emphasizing the idea that engaging in a research process involves taking a different stance from that of a social or professional actor. For example, this may require converting a professional or social question into a research question or reflecting on the use of a social vocabulary in research. The second part concerns practices, that is, how research is conducted: the definition of a research question based on findings, theoretical exploration and problematization, the production of empirical information and its analysis and restitution. The third and final part concludes by focusing on the diversity of research forms; not only research cultures specific to disciplinary fields and approaches, such as action research, collaborative research or research training, but also the design choices in terms of multi-, inter- or trans-disciplinarily.

In The Mind within the Brain, David Redish brings together cutting edge research in psychology, robotics, economics, neuroscience, and the new fields of neuroeconomics and computational psychiatry, to offer a unified theory of human decision-making. Most importantly, Redish shows how vulnerabilities, or "failure-modes," in the decision-making system can lead to serious dysfunctions, such as irrational behavior, addictions, problem gambling, and PTSD. Told with verve and humor in an easily readable style, Redish makes these difficult concepts understandable. Ranging widely from the surprising roles of emotion, habit, and narrative in decision-making, to the larger philosophical questions of how mind and brain are related, what makes us human, the nature of morality, free will, and the conundrum of robotics and consciousness, The Mind within the Brain offers fresh insight into one of the most complex aspects of human behavior.

This book demonstrates an original concept for implementing the rough set theory in the construction of decision-making systems. It addresses three types of decisions, including those in which the information or input data is insufficient. Though decision-making and classification in cases with missing or inaccurate data is a common task, classical decision-making systems are not naturally adapted to it. One solution is to apply the rough set theory proposed by Prof. Pawlak. The proposed classifiers are applied and tested in two configurations: The first is an iterative mode in which a single classification system requests completion of the input data until an unequivocal decision (classification) is obtained. It allows us to start classification processes using very limited input data and supplementing it only as needed, which limits the cost of obtaining data. The second configuration is an ensemble mode in which several rough set-based classification systems achieve the unequivocal decision collectively, even though the systems cannot separately deliver such results.