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.

This book describes the new perspective of naturalistic decision making. The point of departure is how people make decisions in complex, time-pressured, ambiguous, and changing environments. The purpose of this book is to present and elaborate on past models developed to explain this type of decision making. The central philosophy of the book is that classical decision theory has been unproductive since it is so heavily grounded in economics and mathematics. The contributors believe there is little to be learned from laboratory studies about how people actually handle difficult and interesting tasks; therefore, the book presents a critique of classical decision theory. The models of naturalistic decision making described by the contributors were derived to explain the behavior of firefighters, business people, jurors, nuclear power plant operators, and command-and-control officers. The models are unique in that they address the way people use experience to frame situations and adopt courses of action. The models explain the strengths of skilled decision makers. Naturalistic decision research requires the examination of field settings, and a section of the book covers methods for conducting meaningful research outside the laboratory. In addition, since his approach has applied value, the book covers issues of training and decision support systems.

“Christopher de Bellaigue has a magic talent for writing history. It is as if we are there as the era of Suleyman the Magnificent unfolds.” —Orhan Pamuk, winner of the Nobel Prize in Literature Narrated through the eyes of the intimates of Suleyman the Magnificent, the sixteenth-century sultan of the Ottoman Empire, The Lion House animates with stunning immediacy the fears and stratagems of those brought into orbit around him: the Greek slave who becomes his Grand Vizier, the Venetian jewel dealer who acts as his go-between, the Russian consort who becomes his most beloved wife. Within a decade and a half, Suleyman held dominion over twenty-five million souls, from Baghdad to the walls of Vienna, and with the help of his brilliant pirate commander, Barbarossa, placed more Christians than ever before or since under Muslim rule. And yet the real drama takes place in close-up: in small rooms and whispered conversations, behind the curtain of power, where the sultan sleeps head-to-toe with his best friend and eats from wooden spoons with his baby boy. In The Lion House, Christopher de Bellaigue tells the story not just of rival superpowers in an existential duel, nor of one of the most consequential lives in human history, but of what it means to live in a time when a few men get to decide the fate of the world.

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.

The economic crisis of 2008–2009 was a transformational event: it demonstrated that smart people aren't as smart as they and the public think. The crisis arose because a lot of highly educated people in high-impact positions— political power brokers, business leaders, and large segments of the general public—made a lot of bad decisions despite unprecedented access to data, highly sophisticated decision support systems, methodological advances in the decision sciences, and guidance from highly experienced experts. How could we get things so wrong? The answer, says J. Davidson Frame in Framing Decisions: Decision Making That Accounts for Irrationality, People, and Constraints, is that traditional processes do not account for the three critical immeasurable elements highlighted in the book's subtitle— irrationality, people, and constraints. Frame argues that decision-makers need to move beyond their single-minded focus on rational and optimal solutions as preached by the traditional paradigm. They must accommodate a decision's social space and address the realities of dissimulation, incompetence, legacy, greed, peer pressure, and conflict. In the final analysis, when making decisions of consequence, they should focus on people – both as individuals and in groups. Framing Decisions offers a new approach to decision making that gets decision-makers to put people and social context at the heart of the decision process. It offers guidance on how to make decisions in a real world filled with real people seeking real solutions to their problems.

This volume focuses on uncovering the fundamental forces underlying dynamic decision making among multiple interacting, imperfect and selfish 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: • task allocation to maximize “the wisdom of the crowd”; • design of a society of “edutainment” robots who account for one anothers’ emotional states; • recognizing and counteracting seemingly non-rational human decision making; • coping with extreme scale when learning causality in networks; • efficiently incorporating expert knowledge in personalized medicine; • 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 fields.