Explores how decision-makers can manage uncertainty that varies in both kind and severity by extending and supplementing Bayesian decision theory.

When making decisions, people naturally face uncertainty about the potential consequences of their actions due in part to limits in their capacity to represent,

When making decisions, people naturally face uncertainty about the potential consequences of their actions due in part to limits in their capacity to represent,

This book describes the classical axiomatic theories of decision under uncertainty, as well as critiques thereof and alternative theories. It focuses on the mea

Goal-Directed Decision Making: Computations and Neural Circuits examines the role of goal-directed choice. It begins with an examination of the computations per