Thinking, Fast and Slow by Daniel Kahneman

Thinking, Fast and Slow, by Daniel Kahneman, 2011, New York: Farrar, Straus and Giroux, 499 pp. ISBN: 978-0-37427-563-1.Cognitive psychologist and Nobel Laureate Daniel Kahneman will be familiar to most Journal of Risk and Insurance (JRI) readers because of his work with mathematical and cognitive psychologist Amos Tversky regarding decision making when risk is involved. Two of their more frequently cited articles on this subject, "Judgment Under Uncertainty: Heuristics and Biases," originally published in Science, Vol. 185, in 1974, and "Choices, Values and Frames," originally published in American Psychologist, Vol. 34, in 1984, were cited by the Nobel Committee when awarding Kahneman the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel, more familiarly known as the Nobel Prize in Economics, in 2002.1WhUe Kahneman devotes some time discussing this work in his introduction, it is not the primary focus of this book. In Thinking, Fast and Slow, Kahneman uses his considerable wealth of knowledge of behavioral economics, statistics, and the psychology of judgment and decision making to discuss what recent research tells us about why people make bad choices, including some involving risk."Thinking fast" occurs when persons rely on what psychologists refer to as "system 1," a kind of thinking that occurs automatically and very quickly, includes the automatic development of memories, and is associated more with impressions and feelings. "Thinking slow" happens when persons rely on "system 2," a more effortful way of thinking that requires concentration and helps more effectively manage complex problem solving, for example. Loss aversion is a system 1 characteristic traceable to the amygdala, the brain's threat detection center. As we all understand, for most persons, losses hurt more than gains of identical amount help. Calculating an expected return on an investment portfolio or a fair risk premium also requires system 2 resources.Not surprisingly, Kahneman traces most errors of judgment and decision making back to an overreliance on system 1 thinking. Because the system 1 /system 2 framework seems so essential in understanding the rest of the book, the writer devotes the first nine chapters to it; these chapters collectively constitute Part I, "Two Systems."Nearly all of the research cited in this section comes from publications in neuroscience, psychology, and science journals and concerns mainly how the brain functions. The author explains the research using a conversational and largely nontechnical tone so that a reader with only a modest understanding of psychology can understand these chapters.In "Heuristics and Biases," Part II, Kahneman addresses judgment heuristics, or simple, efficient rules that can help explain how judgments get made. He also considers cognitive biases, or in general terms, thought patterns that contribute to irrational decisions. He talks about how these rules and biases apply to statistics, a subject that most would agree requires complex thinking. The statistical issues he addresses are lower level concerns, ranging in simplicity from selecting the right sample size to establishing accurate probability estimates to regression to the mean. All of these topics should be easily accessible to most if not all JRI readers.Part II contains a host of interesting insights, including the fact that most trained researchers ignore sample size requirements when conducting studies, typically relying on samples that are simply too small to generate valid inferences. One of the most fascinating chapters, Chapter 16, "Causes Trump Statistics," includes evidence that people will ignore scientifically valid statistics in favor of compelling stories that touch their emotions. In practice, this means that using one or two representative cases in, say, explaining why all homeowners in a flood-prone area should be required to purchase homeowners insurance will be far more effective in persuading most consumers and politicians than a statistics-heavy presentation documenting total losses, for example. …