Publisher's Synopsis
DSS is getting a lot of attention from many businesses as a way to promote better projections, management and analysis within a company or business. DSS comes in many forms, and the term basically refers to a computer-aided system that helps managers and planners make decisions. DSS combines the power of human thought with the power of modeling systems to get optimal, informed decision making. Decision support systems (DSS) have proved to be efficient for helping humans to make a decision in various domains such as health. Decision support technology can also be a tool that analyzes sales data and makes predictions, or monitors existing patterns. Whether it's big picture decision support tools, active or passive solutions, or any other kind of DSS tool, planners often tackle sales numbers using a variety of decision support resources. There are other uses for this powerful software option - to make good projections on the future for a business, or to get an overall "bird's eye view" of events that determine company's progress. This can come in handy in difficult situations where a lot of financial projection may be necessary when determining expenditures and revenues. However, before being used in practice, these systems need to be extensively evaluated to ensure their validity and their efficiency. DSS evaluation usually includes two steps: first, testing the DSS under controlled conditions, and second, evaluating the DSS in real use, during a randomized trial. DSSs are usually built from a non-structured knowledge source, for instance a clinical practice guideline (a textual guide that provides recommendations to the physicians about the diagnosis or the therapy for a given disease), a set of cases (for a system using case-based reasoning) or a group of domain experts; this knowledge source is then structured into a knowledge base, for instance a set of rules or a case database, and finally, an inference engine applies the knowledge base to the system's input and determines the output. This book, Diverse Applications of Decision Support Systems, deals with diverse application domains, methods, and types of assistance. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book discusses some approaches to computational modelling of decision making. Concretely, it concerns with connectionist models of decision making and it contributes to the categorization of such models. The models presented in this book are algorithmic and structural descriptions of the mental process of decision making. DSS is getting a lot of attention from many businesses as a way to promote better projections, management and analysis within a company or business. DSS comes in many forms, and the term basically refers to a computer-aided system that helps managers and planners make decisions. DSS combines the power of human thought with the power of modeling systems to get optimal, informed decision making. Decision support systems (DSS) have proved to be efficient for helping humans to make a decision in various domains such as health. Decision support technology can also be a tool that analyzes sales data and makes predictions, or monitors existing patterns. Whether it's big picture decision support tools, active or passive solutions, or any other kind of DSS tool, planners often tackle sales numbers using a variety of decision support resources. There are other uses for this powerful software option - to make good projections on the future for a business, or to get an overall "bird's eye view" of events that determine company's progress. This can come in handy in difficult situations where a lot of financial projection may be necessary when determining expenditures and revenues. However, before being used in practice, these systems need to be extensively evaluated to ensure their validity and their efficiency. DSS evaluation usually includes two steps: first, testing the DSS under controlled conditions, and second, evaluating the DSS in real use, during a randomized trial. DSSs are usually built from a non-structured knowledge source, for instance a clinical practice guideline (a textual guide that provides recommendations to the physicians about the diagnosis or the therapy for a given disease), a set of cases (for a system using case-based reasoning) or a group of domain experts; this knowledge source is then structured into a knowledge base, for instance a set of rules or a case database, and finally, an inference engine applies the knowledge base to the system's input and determines the output. This book, Diverse Applications of Decision Support Systems, deals with diverse application domains, methods, and types of assistance. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book discusses some approaches to computational modelling of decision making. Concretely, it concerns with connectionist models of decision making and it contributes to the categorization of such models. The models presented in this book are algorithmic and structural descriptions of the mental process of decision making. DSS is getting a lot of attention from many businesses as a way to promote better projections, management and analysis within a company or business. DSS comes in many forms, and the term basically refers to a computer-aided system that helps managers and planners make decisions. DSS combines the power of human thought with the power of modeling systems to get optimal, informed decision making. Decision support systems (DSS) have proved to be efficient for helping humans to make a decision in various domains such as health. Decision support technology can also be a tool that analyzes sales data and makes predictions, or monitors existing patterns. Whether it's big picture decision support tools, active or passive solutions, or any other kind of DSS tool, planners often tackle sales numbers using a variety of decision support resources. There are other uses for this powerful software option - to make good projections on the future for a business, or to get an overall "bird's eye view" of events that determine company's progress. This can come in handy in difficult situations where a lot of financial projection may be necessary when determining expenditures and revenues. However, before being used in practice, these systems need to be extensively evaluated to ensure their validity and their efficiency. DSS evaluation usually includes two steps: first, testing the DSS under controlled conditions, and second, evaluating the DSS in real use, during a randomized trial. DSSs are usually built from a non-structured knowledge source, for instance a clinical practice guideline (a textual guide that provides recommendations to the physicians about the diagnosis or the therapy for a given disease), a set of cases (for a system using case-based reasoning) or a group of domain experts; this knowledge source is then structured into a knowledge base, for instance a set of rules or a case database, and finally, an inference engine applies the knowledge base to the system's input and determines the output. This book, Diverse Applications of Decision Support Systems, deals with diverse application domains, methods, and types of assistance. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book discusses some approaches to computational modelling of decision making. Concretely, it concerns with connectionist models of decision making and it contributes to the categorization of such models. The models presented in this book are algorithmic and structural descriptions of the mental process of decision making.
