Computer science and decision theory: preface

In today’s world, methods of decision theory are applied in fields ranging from engineering to psychology to medicine to economics to homeland security. Researchers using traditional methods of decision theory face the challenge of applying remarkable new technologies, dealing with huge amounts of information to help them in reaching good decisions, and the necessity to share vast quantities of information at unprecedented speeds. While these tools and resources often lead to better decisions, they can present daunting new problems: incomplete or unreliable or distributed data; difficulty in quantifying uncertainty; coordinating interoperating/distributed decision makers and even decision making devices; fusing many sources of data into a good decision; sharing information under privacyand securityrelated restrictions. When faced with such issues, there are few highly efficient algorithms available to support decisions. Increasingly, research work at the interface between Decision Theory and Computer Science is called for. A primary goal of this research is to improve the ability of decision makers to perform in the face of new challenges and problems, often through the use of methods of theoretical computer science, in particular algorithmic methods. It is necessary to explore and develop algorithmic approaches to decision problems arising in a variety of applications areas. Since many of the decision problems investigated arise in Artificial Intelligence, an important sub-goal involves the cross-fertilization of Decision Theory and Artificial Intelligence. The goal of this special volume of the Annals of Operations Research is to give examples of work at the interface between Decision Theory and Computer Science, providing a first comprehensive overview of this rapidly emerging research area.