Smart people are not only just as prone to making mistakes as everyone else, they may be even more susceptible to them. This is the "intelligence trap," the subject of David Robson’s fascinating and provocative book.
The Intelligence Trap explores cutting-edge ideas in our understanding of intelligence and expertise, including "strategic ignorance," "meta-forgetfulness," and "functional stupidity." Robson reveals the surprising ways that even the brightest minds and most talented organizations can go wrong—from some of Thomas Edison’s worst ideas to failures at NASA, Nokia, and the FBI. And he offers practical advice to avoid mistakes based on the timeless lessons of Benjamin Franklin, Richard Feynman, and Daniel Kahneman.
David Robson has worked as an editor at New Scientist and BBC Future, and his writing has appeared in the Atlantic, the Observer, and the Washington Post. He lives in London.
The first two chapters discuss the role of mental representation in moral judgment and reasoning. Sloman, Fernbach, and Ewing argue that causal models are the canonical representational medium underlying moral reasoning, and Mikhail offers an account that makes use of linguistic structures and implicates legal concepts. Bilz and Nadler follow with a discussion of the ways in which laws, which are typically construed in terms of affecting behavior, exert an influence on moral attitudes, cognition, and emotions.
Baron and Ritov follow with a discussion of how people's moral cognition is often driven by law-like rules that forbid actions and suggest that value-driven judgment is relatively less concerned by the consequences of those actions than some normative standards would prescribe. Iliev et al. argue that moral cognition makes use of both rules and consequences, and review a number of laboratory studies that suggest that values influence what captures our attention, and that attention is a powerful determinant of judgment and preference. Ginges follows with a discussion of how these value-related processes influence cognition and behavior outside the laboratory, in high-stakes, real-world conflicts.
Two subsequent chapters discuss further building blocks of moral cognition. Lapsley and Narvaez discuss the development of moral characters in children, and Reyna and Casillas offer a memory-based account of moral reasoning, backed up by developmental evidence. Their theoretical framework is also very relevant to the phenomena discussed in the Sloman et al., Baron and Ritov, and Iliev et al. chapters.
The final three chapters are centrally focused on the interplay of hot and cold cognition. They examine the relationship between recent empirical findings in moral psychology and accounts that rely on concepts and distinctions borrowed from normative ethics and decision theory. Connolly and Hardman focus on bridge-building between contemporary discussions in the judgment and decision making and moral judgment literatures, offering several useful methodological and theoretical critiques. Ditto, Pizarro, and Tannenbaum argue that some forms of moral judgment that appear objective and absolute on the surface are, at bottom, more about motivated reasoning in service of some desired conclusion. Finally, Bauman and Skitka argue that moral relevance is in the eye of the perceiver and emphasize an empirical approach to identifying whether people perceive a given judgment as moral or non-moral. They describe a number of behavioral implications of people's reported perception that a judgment or choice is a moral one, and in doing so, they suggest that the way in which researchers carve out the moral domain a priori might be dubious.
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All our lives are constrained by limited space and time, limits that give rise to a particular set of problems. What should we do, or leave undone, in a day or a lifetime? How much messiness should we accept? What balance of new activities and familiar favorites is the most fulfilling? These may seem like uniquely human quandaries, but they are not: computers, too, face the same constraints, so computer scientists have been grappling with their version of such problems for decades. And the solutions they've found have much to teach us.
In a dazzlingly interdisciplinary work, acclaimed author Brian Christian (who holds degrees in computer science, philosophy, and poetry, and works at the intersection of all three) and Tom Griffiths (a UC Berkeley professor of cognitive science and psychology) show how the simple, precise algorithms used by computers can also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one's inbox to understanding the workings of human memory, Algorithms to Live By transforms the wisdom of computer science into strategies for human living.