This book examines how people make decisions under risk and uncertainty in operational settings and opens the black box by specifying the cognitive processes that lead to human behavior. Drawing on economics, psychology and artificial intelligence, the book provides an innovative perspective on behavioral operations. It shows how to build optimization as well as heuristic models for describing human behavior and how to compare such models on various dimensions such as predictive power and transparency, as well as discussing interventions for improving human behavior. This book will be particularly valuable to academics and practitioners who seek to select a modeling approach that suits the operational decision at hand.
Rules for building formal models that use fast-and-frugal heuristics, extending the psychological study of classification to the real world of uncertainty. This book focuses on classification--allocating objects into categories--"in the wild," in real-world situations and far from the certainty of the lab. In the wild, unlike in typical psychological experiments, the future is not knowable and uncertainty cannot be meaningfully reduced to probability. Connecting the science of heuristics with machine learning, the book shows how to create formal models using classification rules that are simple, fast, and transparent and that can be as accurate as mathematically sophisticated algorithms developed for machine learning.
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