- Series: Bradford Books
- Hardcover: 352 pages
- Publisher: The MIT Press; 1 edition (September 4, 2009)
- Language: English
- ISBN-10: 0262013398
- ISBN-13: 978-0262013390
- Product Dimensions: 6 x 0.9 x 9 inches
- Shipping Weight: 1.4 pounds (View shipping rates and policies)
- Average Customer Review: 20 customer reviews
- Amazon Best Sellers Rank: #725,574 in Books (See Top 100 in Books)
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Streetlights and Shadows: Searching for the Keys to Adaptive Decision Making (Bradford Books) 1st Edition
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"I know of no one who combines theory and observation--intellectual rigor and painstaking observation of the real world--so brilliantly and gracefully as Gary Klein."--Malcolm Gladwell, author of Outliers and Blink(Malcolm Gladwell)
"Gary Klein has taken aim at attempts to base decision making on analytic reasoning. To his credit, he does not claim that analytic decision models are useless. He argues that they are limited, and he shows how and why. Klein shows the importance of human understanding and experience as alternatives to analytic models, especially in complex and dynamic situations. He makes his point with many excellent examples, drawn both from his own extensive experience and from the literature. This is a book that should be read by anyone with a serious interest in how decisions ought to be made, whether by humans or machines." Earl Hunt , Professor Emeritus, University of Washington
"I know of no one who combines theory and observation—intellectual rigor and painstaking observation of the real world—so brilliantly and gracefully as Gary Klein." Malcolm Gladwell , author of Outliers and Blink
About the Author
Gary Klein is a Senior Scientist at Applied Research Associates. He is the author of Sources of Power: How People Make Decisions (1999) and the coauthor of Working Minds: A Practitioner's Guide to Cognitive Task Analysis (2006), both published by the MIT Press.
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The key thread in the book is examining the relationship between analysis and intuition, largely comparing standard procedures and skills based on experience. `The way we see in bright light differs from the way we see in shadows. Neither is the "right" way. We need both.' is a quote that summarises Klein's argument well. He examines contexts in which standardising ways of doing things helps and the contexts where that hurts, in particular with skilled performers, showing that experts mostly rely on heuristics drawn from stories instead of rules. There are many nice stories in the book about decision biases, but often arguing for the oposite conclusion from most popular psychology books. Klein discredits most of the research on decision biases and exposing how reasoning strategies can lead to errors because they were done using college students performing tasks that are unfamiliar, artificial, and relatively independent of context. His idea is that biases aren't distorting our thinking, but instead reflect our thinking. As in his previous work, Klein attacks the Rational Choice model of decision making, arguing that analysing several options and comparing to pick the best one doesn't help anyone make better decisions, because it ignores the way experienced people actually make decisions, and does not really help novices.
With the rising popularity of big data and company analytics, I found particularly interesting the topic of collecting too much information and how that can lead to worse results. Concluding that "most experts use fewer than five cues when making judgments", Klein quotes research showing that teams with incomplete information often perform better than teams with detailed information, as more information often does not improve the accuracy of judgements but leads to overconfidence. There is also a nice tie-in with experiment-based strategies becoming more popular in the software world ("The only ones who succeeded had jumped to conclusions and tested them.") and an examination of the value and importance of feedback when dealing with "wicked problems", in which the goals are incomplete and keep changing, as well as occasionally conflicting.