Books published by MIT Press Ltd

An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance.Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance.Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance.Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.

Through essays, poetry, stories, and images, writers and artists offer their perceptions of how we fit into the world and where we might be headed.

How Jerry Wiesner, presidential science adviser and president of MIT, worked to make a better and safer world, as told by friends and colleagues and in his own autobiographical writings.

Influential neoclassical economist Lionel McKenzie has made major contributions to postwar economic thought in the fields of equilibrium, trade, and capital accumulation. This selection of his papers traces the development of his thinking in these three crucial areas.McKenzie's early academic life took him to Duke, Princeton, Oxford, the University of Chicago, and the Cowles Commission. In 1957, he went to the University of Rochester to head the economics department there, and he remains at Rochester, now Wilson Professor Emeritus of Economics. McKenzie's most significant research was undertaken during a period that saw the development of the major themes of neoclassical economics and the use of fundamental mathematical methods to do so. McKenzie contributed to both aspects of this research program. He helped shape the direction of the field and, at Rochester, influenced generations of future scholars. In 2002, The MIT Press published McKenzie's Classical General Equilibrium Theory, a detailed summary of the model and methodology. This book, collecting his most important papers in the form in which they were originally published, can be seen as a companion to that one. The many state-of-the-art results achieved in McKenzie's original papers present sophisticated theoretical work that will continue to be important to future developments in the discipline.

A survey of probabilistic approaches to modeling and understanding brain function.