Unlike statistical decision theory, adaptive modeling makes full use of the decision maker's knowledge of a given system to reduce the number of investigated alternatives. This book at once introduces the entire area of adaptive modeling and precisely defines a new decision-making procedure for developing and using models of large, complex socioeconomic systems. It draws on material from a number of fields and may be used by people with disciplinary backgrounds ranging from statistics and economics to decision theory and management science. Foremost perhaps is the study's obvious implications for comprehensive planning at the national level.
The adaptive procedure proposed in this book uses a man-machine system: “Human beings contribute knowledge of the system being modeled and of the decision-making process; computers (which contain a core model) contribute calculating speed and accuracy.” The book describes how recent developments in computer systems make possible a continuous man-machine conversation in which policy maker can change his inputs—policy variables or parameters—and see the results instantaneously. Simulating in a conversational mode thus allows the operator to modify his core model so that it reflects alternative representations of the reality being modeled.
While this “learning-by-doing” procedure is potentially useful in a formal educational setting, the author has chosen to concentrate on decision making in two types of planning: long-run sectoral planning to achieve growth (the economic growth problem); and short-run macroeconomic planning to achieve stabilization (the stabilization problem). Both input-output and macroeconomic models are used in these adaptive procedures.