This book provides an introduction to decision making in a distributed computational framework. When most computations were performed by a central processor, classical detection theory could assume that the processor could make decisions based on complete information. The development of distributed processors working in parallel on different parts of the same computational problem makes it necessary to make local decisions that are then conveyed to other processors, where ultimately a fusion center must make global decisions. Using numerous examples throughout the book, the author discusses such distributed detection processes under various different formulations and in a wide variety of network topologies. By providing a unified treatment of the recent advances, this book should prove valuable not only to researchers active in the field, but also to graduate students and others embarking on research in detection, signal processing, and statistical decision theory. Some prior knowledge of detection theory is assumed.