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Reviewer: Klaus Galensa

One of the major characteristics of science in the second half of the twentieth century was the emergence of a number of related intellectual areas, such as cybernetics, general systems research, information theory, control theory, mathematical systems theory, decision theory, operations research, and artificial intelligence. All of those areas, whose appearance and development are strongly correlated with the origins and advances of computer technology, have one thing in common: they deal with such systems problems in which informational, relational, or structural aspects predominate, whereas the kinds of entities that form the system are considerably less significant. It has increasingly been recognized that it is useful to view these interrelated developments as parts of a larger field of inquiry, usually referred to as systems science. A course that covers systems fundamentals is now offered not only in systems science, information science, or systems engineering programs, but in many programs in other disciplines as well. This book could serve as a text for a first-year graduate or upper-division undergraduate course covering the fundamentals of systems problem solving. A unique feature of this book is that the concepts, problems, and methods are introduced within the context of an architectural formulation of an expert system, referred to as the general system's problem solver (GSPS). The GSPS architecture, which is developed throughout the book, facilitates a framework that is conducive to a coherent, comprehensive, and pragmatic coverage of systems fundamentals. In chapter 1, the three basic components of system science are introduced: the domain of inquiry, the body of knowledge regarding the domain, and the methodology for the acquisition of new knowledge. The remaining five chapters present the hierarchy of epistemological levels of systems, starting at the lowest level in the hierarchy, the source and data systems in chapter 2. At this level, a system is what is distinguished as a system by the investigator, with no data regarding the variables available. Such a system is, at least potentially, a source of empirical data. When the source system is supplemented with data (actual states of the basic variables), it is called a data system. After the data system is finalized, the next stage in empirical investigation is data processing. Its aim is to determine some properties of the variables involved through which the data can be represented in a parsimonious fashion and, if desirable, generated. There are a variety of properties, but they all have a common denominator. Each of them characterizes a constraint. This is explained in great detail in chapter 3. In chapter 4, structure systems are described. A structure system is a set of lower-level systems that are based on the same set of independent variables. The lower-level systems may be coupled, in the sense that they share some variables, or may interact in some other way. If changes in the lower-level systems can be described by a rule, a relation, or some procedure, then the higher-level system is called a metasystem. Metasystems and meta-metasystems are described in chapter 5. Finally, in chapter 6, the architecture, possible use, and evolution of GSPS are presented. A subject index concludes the book, which is accompanied by a CD that contains some supplementary chapters, notes, exercises, case studies, the glossary, and the references. Therefore, the CD is an integral part of the book. Although the book is well written, the CD, which contains text in hypertext markup language (HTML), makes frequent references to a Web site that was not functioning at the time this review was written. Online Computing Reviews Service