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Название: Integrating Soft Computing into Strategic Prospective Methods: Towards an Adaptive Learning Environment Supported by Futures Studies Автор: Raul Trujillo-Cabezas, Jose Luis Verdegay Издательство: Springer Серия: Studies in Fuzziness and Soft Computing Год: 2019 (2020 Edition) Страниц: 242 Язык: английский Формат: pdf (true), djvu Размер: 10.1 MB
Soft Computing is one of the fundamental areas in Artificial Intelligence, while prospective is a very important Future Studies approach. For several decades, many Soft Computing and Futures Studies books have been written, some of which contain hundreds of pages with pleasant discussions about uncertainty. This is the main topic of discussion in this book. The field of Soft Computing in Humanities and Social Sciences in recent years has changed. The proposal to include hybrid models of Soft Computing in the field of Futures Studies gave us the motivation to write this book, which is, however, not intended to replace others. Hence, this book introduces a new route that brings together two disciplines thus helping build a prospective reflection framework based on anticipation, learning, and adaptation.
This book discusses how to build optimization tools able to generate better future studies. It aims at showing how these tools can be used to develop an adaptive learning environment that can be used for decision making in the presence of uncertainties. The book starts with existing fuzzy techniques and multicriteria decision making approaches and shows how to combine them in more effective tools to model future events and take therefore better decisions. The first part of the book is dedicated to the theories behind fuzzy optimization and fuzzy cognitive map, while the second part presents new approaches developed by the authors with their practical application to trend impact analysis, scenario planning and strategic formulation. The book is aimed at two groups of readers, interested in linking the future studies with Artificial Intelligence. The first group includes social scientists seeking for improved methods for strategic prospective. The second group includes computer scientists and engineers seeking for new applications and current developments of Soft Computing methods for forecasting in social science, but not limited to this.
This book offers a guide to Soft Computing, with a special emphasis on the connections to the field of Futures Studies, and proposes a novel approach for strategic prospective, called Meta-Prospective. It builds and tests a framework that is able to reduce uncertainty in the processes of long-term strategic reflection.
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Integrating Soft Computing into Strategic Prospective Methods: Towards an Adaptive Learning Environment Supported by Futures Studies 
