Prescriptive Bayesian decision making has reached a high level of maturity and is well-supported algorithmically. However, experimental data shows that real decision makers choose such Bayes-optimal decisions surprisingly infrequently, often making decisions that are badly sub-optimal. So prevalent is such imperfect decision-making that it should be accepted as an inherent feature of real decision makers living within interacting societies. To date such societies have been investigated from an economic and gametheoretic perspective, and even to a degree from a physics perspective. However, little research has been done from the perspective of computer science and associated disciplines like machine learning, information theory and neuroscience. This book is a major contribution to such research. Some of the particular topics addressed include: How should we formalise rational decision making of a single imperfect decision maker? Does the answer change for a system of imperfect decision makers? Can we extend existing prescriptive theories for perfect decision makers to make them useful for imperfect ones? How can we exploit the relation of these problems to the control under varying and uncertain resources constraints as well as to the problem of the computational decision making? What can we learn from natural, engineered, and social systems to help us address these issues?

Decision making (DM) is ubiquitous in both natural and artificial systems. The decisions made often differ from those recommended by the axiomatically well-grounded normative Bayesian decision theory, in a large part due to limited cognitive and computational resources of decision makers (either artificial units or humans). This state of a airs is often described by saying that decision makers are imperfect and exhibit bounded rationality. The neglected influence of emotional state and personality traits is an additional reason why normative theory fails to model human DM process. The book is a joint effort of the top researchers from different disciplines to identify sources of imperfection and ways how to decrease discrepancies between the prescriptive theory and real-life DM. The contributions consider: · how a crowd of imperfect decision makers outperforms experts' decisions; · how to decrease decision makers' imperfection by reducing knowledge available; · how to decrease imperfection via automated elicitation of DM preferences; · a human's limited willingness to master the available decision-support tools as an additional source of imperfection; · how the decision maker's emotional state influences the rationality; a DM support of edutainment robot based on its system of values and respecting emotions. The book will appeal to anyone interested in the challenging topic of DM theory and its applications.

You''re tempted to accept a promising job offer in another city, but moving would entail considerable sacrifice on the part of your family. What should you do? Your elderly mother can no longer take care of herself but she doesn''t get along with your husband and dreads the prospect of moving into a nursing home. What is the solution? Whether you are faced with decisions momentous or trivial, how you go about resolving everyday dilemmas will definitely affect your level of satisfaction in life. In this engrossing and entertaining guide, David Welch, who has studied the decision-making process at the highest levels, shows how both the science and the art of decision-making are essential to us all. Welch lays out nine steps to effective decision-making and then demonstrates how to apply these steps to real-world situations. He gives readers the intellectual tools to assess their strengths and weaknesses and stresses that self-knowledge is critical for making the right decisions. This enjoyable, clearly written guide will enable decision-makers at every level to find the best possible solution for dilemmas both big and small.

Decision making (DM) is ubiquitous in both natural and artificial systems. The decisions made often differ from those recommended by the axiomatically well-grounded normative Bayesian decision theory, in a large part due to limited cognitive and computational resources of decision makers (either artificial units or humans). This state of a airs is often described by saying that decision makers are imperfect and exhibit bounded rationality. The neglected influence of emotional state and personality traits is an additional reason why normative theory fails to model human DM process. The book is a joint effort of the top researchers from different disciplines to identify sources of imperfection and ways how to decrease discrepancies between the prescriptive theory and real-life DM. The contributions consider: · how a crowd of imperfect decision makers outperforms experts' decisions; · how to decrease decision makers' imperfection by reducing knowledge available; · how to decrease imperfection via automated elicitation of DM preferences; · a human's limited willingness to master the available decision-support tools as an additional source of imperfection; · how the decision maker's emotional state influences the rationality; a DM support of edutainment robot based on its system of values and respecting emotions. The book will appeal to anyone interested in the challenging topic of DM theory and its applications.

This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selƠ̐lsh decision makers. The chapters are written by leading experts from different disciplines, all considering the many sources of imperfection in decision making, and always with an eye to decreasing the myriad discrepancies between theory and real world human decision making. Topics addressed include uncertainty, deliberation cost and the complexity arising from the inherent large computational scale of decision making in these systems. In particular, analyses and experiments are presented which concern: ĺØ task allocation to maximize ĺlthe wisdom of the crowdĺl; ĺØ design of a society of ĺledutainmentĺl robots who account for one anothersĺl emotional states; ĺØ recognizing and counteracting seemingly non-rational human decision making; ĺØ coping with extreme scale when learning causality in networks; ĺØ efƠ̐lciently incorporating expert knowledge in personalized medicine; ĺØ the effects of personality on risky decision making. The volume is a valuable source for researchers, graduate students and practitioners in machine learning, stochastic control, robotics, and economics, among other Ơ̐lelds.

Decision making is a critical part of management, and bad choices can damage careers and the bottom line. This book offers the tools and advice managers need to avoid common biases and arrive at and implement decisions that are both sound and ethical. The Harvard Business Essentials series provides comprehensive advice, personal coaching, background information, and guidance on the most relevant topics in business. Whether you are a new manager seeking to expand your skills or a seasoned professional looking to broaden your knowledge base, these solution-oriented books put reliable answers at your fingertips.

From the Nobel Prize-winning author of Thinking, Fast and Slow and the coauthor of Nudge, a revolutionary exploration of why people make bad judgments and how to make better ones—"a tour de force” (New York Times). Imagine that two doctors in the same city give different diagnoses to identical patients—or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants—or that when a company is handling customer complaints, the resolution depends on who happens to answer the phone. Now imagine that the same doctor, the same judge, the same interviewer, or the same customer service agent makes different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical. In Noise, Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein show the detrimental effects of noise in many fields, including medicine, law, economic forecasting, forensic science, bail, child protection, strategy, performance reviews, and personnel selection. Wherever there is judgment, there is noise. Yet, most of the time, individuals and organizations alike are unaware of it. They neglect noise. With a few simple remedies, people can reduce both noise and bias, and so make far better decisions. Packed with original ideas, and offering the same kinds of research-based insights that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment—and what we can do about it.