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.

Some years ago we, the editors of this volume, found out about each other's deeply rooted interest in the concept of time, the usage of time, and the effects of shortage of time on human thought and behavior. Since then we have fostered the idea of bringing together different perspectives in this area. We are now, there fore, very content that our idea has materialized in the present volume. There is both anecdotal and empirical evidence to suggest that time con straints may affect behavior. Managers and other professional decision makers frequently identify time pressure as a major constraint on their behavior (Isen berg, 1984). Chamberlain and Zika (1990) provide empirical support for this view, showing that complaints of insufficient time are the most frequently report ed everyday minor stressors or hassles for all groups of people except the elderly. Similarly, studies in occupational settings have identified time pressure as one of the central components of workload (Derrich, 1988; O'Donnel & Eggemeier, 1986).

Bishop & Trout present a new approach to epistemoloy, aiming to liberate the subject from the 'scholastic' debates of analytic philosophy. Rather, they wish to treat epistemology as a branch of the philosophy of science.

There are four basic goals for research in SJT (Social Judgment Theory): - to analyze judgment tasks and judgmental processes; - to analyze the relations between judgmental systems (i.e. to analyze agreement and its structure), and between tasks and judgmental systems (i.e. to analyze achievement and its structure; - to understand how relations between judgmental systems and between judgmental systems and tasks come to be whatever they are (i.e. to understand processes of communication and learning and their effects upon achievement and agreement); - to find means of improving the relation between judgmental systems (improving agreement) and between judgmental systems and tasks (improving achievement).

This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of a general attempt to place current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches. The term "A.I." is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and 'intelligence', presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether "human" or "A.I."

This work examines issues such as medical diagnosis, weather forecasting, labour negotiations, risk, public policy, business strategy, eyewitnesses, and jury decisions. This is a revision of Arkes and Hammond's 1986 collection of papers on judgment and decision-making. Updated and extended, the focus of this volume is interdisciplinary and applied.