num_pages: 336
description: Discover nine common business decision-making traps -- and learn practical tools for avoiding them -- in this "masterful," research-based guide from a professor of strategic thinking. (Daniel Kahneman, author of Thinking, Fast and Slow)We all make decisions all the time. It's so natural that we hardly stop to think about it. Yet even the smartest and most experienced among us make frequent and predictable errors. So, what makes a good decision? Should we trust our intuitions, and if so, when? How can we avoid being tripped up by cognitive biases when we are not even aware of them?In You're About to Make a Terrible Mistake!, strategy professor and management consultant Olivier Sibony draws on dozens of fascinating and engaging case studies to show how cognitive biases routinely lead all of us -- including even the most renowned business titans -- into nine common decision-making traps. But instead of rehashing the same old "debiasing" techniques that fail managers time and again, Sibony explains that the best way to avoid the pitfalls of cognitive bias is to craft an effective decision-making architecture in your organization -- a system of techniques and processes that leverage collective intelligence to help leaders make the best decisions possible -- and provides 40 concrete methods for doing so.Distinctive in the clarity and practicality of its message, You're About to Make a Terrible Mistake! distills the latest developments in behavioral economics and cognitive psychology into actionable tools for making smart, effective decisions in business and beyond.
  
    
  
"Succinct, accurate, and even-handed. I loved it!" (Angela Duckworth, bestselling author of Grit)
  "The best, funniest, most useful guide to cognitive bias in business. If you make decisions, you need to read this book." (Safi Bahcall, bestselling author of Loonshots)

language: English
title: You're About to Make a Terrible Mistake: How Biases Distort Decision-Making and What You Can Do to Fight Them
publisher: Little, Brown Spark