Today's ever more complex world creates challenges for decision makers. This volume reviews the principles underlying complex decision making, the handling of uncertainties in dynamic environments, and the various modeling approaches. Beginning with a discussion of the underlying concepts, theories and empirical evidence, the book gives you a range of practical tools and techniques for decision making in complex environments and systems.

The Third Richmond Conference centered on foundational problems in decision-making. The conference is reported in two volumes. Volume II provides the major papers themselves: Observable Components of the Decision Process and a Revised Theoretical Position, by Gordon Pask; Decision Making as an Event-Search: Traffic on a Multi-dimensional Structure by R.H. Atkin: Decision: Foundation and Practice by Brian R. Gaines; Competing Modes of Cognition and Communication in Simulated and Self-Reflective Systems by Stein Braten; On the Spontaneous Emergence of Decision Making Constraints in Communicating Hierarchical Systems, by John S. Nicolis; and also a paper by Maria Nowakowska, on a new model of decision under risk.

In order to ensure the criteria for monitoring and managing the various problems and design for decision control, a mathematical description of exact human knowledge is required for the management of adaptive and complex systems. Decision Control, Management, and Support in Adaptive and Complex Systems: Quantitative Models presents an application and demonstration of a new mathematical technique for descriptions of complex systems. This comprehensive collection contains scientific results in the field of contemporary approaches to adaptive decision making that is essential for researchers, scholars, and students alike.

The study of complex systems attracts the attention of many researchers in diverse fields. Complex systems are characterized by a high number of entities and a high degree of interactions. One of the most important features is that they do not involve a central organizing authority, but the various elements that make up the systems are self-organized. Moreover, some complex systems possess an emergency priority: climate change and sustainable development research, studies of public health, ecosystem habitats, epidemiology, and medicine, among others. Unfortunately, a great number of today’s overlapping approaches fail to meet the needs of decision makers when managing complex domains. Indeed, the design of complex systems often requires the integration of a number of artificial intelligence tools and techniques. The problem can be viewed in terms of goals, states, and actions, choosing the best action to move the system toward its desired state or behavior. This is why agent-based approaches are used to model complex systems. The main objective of this book is to bring together existing methods for decision support systems creation within a coherent agent-based framework and to provide an interdisciplinary and flexible methodology for modeling complex and systemic domains.

Twelve papers on the subject of human decision making were presented and discussed. There were several recurrent themes to the conference proceedings: (1) The otherwise confusing picture of human decision making is somewhat simplified by the existence of individually and culturally consistent cognitive styles and methods of recall. Hence, given a kit of decision tools, including computer aids as well as mathematical models, it is possible either to adapt the tools to suit the human decision maker or, vice versa, to train the decision maker to use decision tools that are chosen on grounds of efficiency or elegance; (2) It is possible that people may be trained to respond in a particular way that yields performance data: for example, to use confidence estimation and rating facilities. However, this does not mean that they can be trained to decide; (3) Attempts to fit simple minded models to human performance are generally unsuccessful, especially for sequential and temporally dependent choice. More predictive models are available at the price of increased sophistication and properly accounting for underlying cognitive processes; (4) Decision analysis is not, as sometimes supposed, a prescriptive exercise, introducing the decision maker to what he should do. Nor, as sometimes supposed, is it descriptive. The chief merit of the method, which has attestable value when used in this spirit, is to establish a compromise between the decision maker and the expert, so that, as a result of learning and dialogue, it is possible to construct a compromise model, which is on the one hand acceptably rational and communicable, on the other hand, tailored to the decision makers' ingrained habits.