Download Free Decision Making Recent Developments And Worldwide Applications Book in PDF and EPUB Free Download. You can read online Decision Making Recent Developments And Worldwide Applications and write the review.

This chapter describes a study conducted at the Swinburne University of Technology in Australia, in their School of Business. The study was to explore the applicability of a judgment-analytic decision support system to the assessment of the likelihood of an applicant being selected for admission to the School's Graduate Certificate in Business Administration (GCBA) program. The likelihood of a program administrator selecting a particular applicant is directly linked to the assessment of the likelihood of that applicant's success in the GCBA program. The purpose of this study, in effect, was to analyze the administrative judgment process in assessment of an applicant's likelihood of success in the program. THE PROCESS OF HUMAN JUDGMENT Human judgment is a process through which an individual uses social infonnation to make decisions. The social infonnation is obtained from an individual's environment and is interpreted through the individual's cognitive image of the environment. The. cognitive image provides a representation of the environment based on past experiences and training, and essentially predisposes the person to respond to social infonnation in predictable ways. An individual's policies or beliefs about the environment represent these patterns. Human judgments are based then upon one's interpretation of available infonnation. They are probability statements about one's environment and how one reacts to it. This condition leads to the human judgment process being inherently limited. It is fundamentally a covert process. It is seldom possible for an individual to accurately describe his or her judgment process accurately.
This work examines all the fuzzy multicriteria methods recently developed, such as fuzzy AHP, fuzzy TOPSIS, interactive fuzzy multiobjective stochastic linear programming, fuzzy multiobjective dynamic programming, grey fuzzy multiobjective optimization, fuzzy multiobjective geometric programming, and more. Each of the 22 chapters includes practical applications along with new developments/results. This book may be used as a textbook in graduate operations research, industrial engineering, and economics courses. It will also be an excellent resource, providing new suggestions and directions for further research, for computer programmers, mathematicians, and scientists in a variety of disciplines where multicriteria decision making is needed.
Portfolio Decision Analysis: Improved Methods for Resource Allocation provides an extensive, up-to-date coverage of decision analytic methods which help firms and public organizations allocate resources to 'lumpy' investment opportunities while explicitly recognizing relevant financial and non-financial evaluation criteria and the presence of alternative investment opportunities. In particular, it discusses the evolution of these methods, presents new methodological advances and illustrates their use across several application domains. The book offers a many-faceted treatment of portfolio decision analysis (PDA). Among other things, it (i) synthesizes the state-of-play in PDA, (ii) describes novel methodologies, (iii) fosters the deployment of these methodologies, and (iv) contributes to the strengthening of research on PDA. Portfolio problems are widely regarded as the single most important application context of decision analysis, and, with its extensive and unique coverage of these problems, this book is a much-needed addition to the literature. The book also presents innovative treatments of new methodological approaches and their uses in applications. The intended audience consists of practitioners and researchers who wish to gain a good understanding of portfolio decision analysis and insights into how PDA methods can be leveraged in different application contexts. The book can also be employed in courses at the post-graduate level.
In recent years rough set theory has attracted the attention of many researchers and practitioners all over the world, who have contributed essentially to its development and applications. Weareobservingagrowingresearchinterestinthefoundationsofroughsets, including the various logical, mathematical and philosophical aspects of rough sets. Some relationships have already been established between rough sets and other approaches, and also with a wide range of hybrid systems. As a result, rough sets are linked with decision system modeling and analysis of complex systems, fuzzy sets, neural networks, evolutionary computing, data mining and knowledge discovery, pattern recognition, machine learning, and approximate reasoning. In particular, rough sets are used in probabilistic reasoning, granular computing (including information granule calculi based on rough mereology), intelligent control, intelligent agent modeling, identi?cation of autonomous s- tems, and process speci?cation. Methods based on rough set theory alone or in combination with other - proacheshavebeendiscoveredwith awide rangeofapplicationsinsuchareasas: acoustics, bioinformatics, business and ?nance, chemistry, computer engineering (e.g., data compression, digital image processing, digital signal processing, p- allel and distributed computer systems, sensor fusion, fractal engineering), de- sion analysis and systems, economics, electrical engineering (e.g., control, signal analysis, power systems), environmental studies, informatics, medicine, mole- lar biology, musicology, neurology, robotics, social science, software engineering, spatial visualization, Web engineering, and Web mining.
Volume IV of the Transactions on Rough Sets (TRS) introduces a number of new advances in the theory and application of rough sets. Rough sets and - proximationspaceswereintroducedmorethan30yearsagobyZdzis lawPawlak. These advances have profound implications in a number of research areas such as the foundations of rough sets, approximate reasoning, arti?cial intelligence, bioinformatics,computationalintelligence, cognitivescience, intelligentsystems, datamining,machineintelligence,andsecurity. Inaddition,itisevidentfromthe papers included in this volume that the foundations and applications of rough sets is a very active research area worldwide. A total of 16 researchers from 7 countries are represented in this volume, namely, Canada, India, Norway, S- den, Poland, Russia and the United States of America. Evidence of the vigor, breadth and depth of research in the theory and applications of rough sets can be found in the 10 articles in this volume. Prof. Pawlak has contributed a treatise on the philosophical underpinnings of rough sets. In this treatise, observations are made about the Cantor notion of a set, antinomies arising from Cantor sets, the problem of vagueness (es- cially, vague (imprecise) concepts), fuzzy sets, rough sets, fuzzy vs. rough sets as well as logic and rough sets. Among the many vistas and research directions suggested by Prof. Pawlak, one of the most fruitful concerns the model for a rough membership function, which was incarnated in many di?erent forms since its introduction by Pawlakand Skowronin 1994. Recall, here, that Prof.
The Cross-Cultural Decision Making (CCDM) research focuses on improved decision making across a variety of cultural constructs, including geographical, historical, sociological, organizational, team, and technology interactions. This includes the research of experts and industry practitioners from multidisciplinary backgrounds, including sociology, linguistics, human-computer interaction, human factors engineering, systems engineering, military science, psychology, neuroscience, instructional design, and education, who showcase the latest advances in our understanding of the role of culture on decision making in numerous settings. Improved decision making among members of diverse teams and within organizational systems, and innovative ways to measure and assess that process, comprise the foundation for many projects discussed in these volumes. The influence of culture on decision making is pervasive, as reflected in the diverse disciplines represented by those individuals and entities involved in sociocultural research and engineering. This CCDM book features papers that discuss emerging concepts, theories, and applications of cross-cultural decision making knowledge. The work described in these chapters reflects dedicated research by a wide range of expert academics and practitioners from around the world.
The field of knowledge for development now occupies a top position on the agenda of all Asian governments as well as large development organizations. This book reflects this mega-trend of development towards KBEs (Knowledge Based Economies). For this 2nd edition all chapters have been thoroughly edited and data, tables and graphs have been updated to reflect the latest available statistics. Trends have been re-evaluated and adjusted to reflect recent developments in the fast-moving scene of knowledge governance and knowledge management.
When people or computers need to make a decision, typically multiple conflicting criteria need to be evaluated; for example, when we buy a car, we need to consider safety, cost and comfort. Multiple criteria decision making (MCDM) has been researched for decades. Now as the rising trend of big-data analytics in supporting decision making, MCDM can be more powerful when combined with state-of-the-art analytics and machine learning. In this book, the authors introduce a new framework of MCDM, which can lead to more accurate decision making. Several real-world cases will be included to illustrate the new hybrid approaches.
This book presents research on recent developments in collective decision-making. With contributions from leading scholars from a variety of disciplines, it provides an up-to-date overview of applications in social choice theory, welfare economics, and industrial organization. The contributions address, amongst others, topics such as measuring power, the manipulability of collective decisions, and experimental approaches. Applications range from analysis of the complicated institutional rules of the European Union to responsibility-based allocation of cartel damages or the design of webpage rankings. With its interdisciplinary focus, the book seeks to bridge the gap between different disciplinary approaches by pointing to open questions that can only be resolved through collaborative efforts.
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.