I have read all of Michael Mauboussin's HBR Blog posts and thus have eagerly anticipated the publication of this volume in which he examines - in much greater depth than the articles permit -- a common mistake when making predictions: failing to recognize the existence or miscalculating its influence, "and as a consequence we dwell too much on the specific evidence, especially recent evidence. This makes it tougher to judge performance...The problem is that we commonly twist, distort, or ignore the role that luck plays in our successes and failures. Thinking explicitly about how luck influences our lives can help offset that cognitive bias."
Obviously, luck can be an important factor. The challenge that Mauboussin embraces is to explore and thereby understand "the extent to which luck contributes to our achievements, successes, and failures." He shares what he has learned so that those who read this book will be much better prepared to take luck into full account when making decisions, especially those that serious implications and could have major consequences.
These are among the dozens of passages that are of special interest and value to me:
o Skill, Luck, and Prediction (Pages 5-10) o The Luck-Skill Continuum and Three Lessons (23-29) o Stories, Causality, and the Post Hoc Fallacy (34-38) o The Paradox of Skill -- More Skill Means Luck Is More Important (53-58) o Simulation: Blending Distributions to Match the Results (70-73) o Gauging Luck: Independent and Dependent Outcomes (110-116) o Power Laws and the Mechanisms That Generate Them (116-123) o Deliberate Practice: Structure, Hard Work, and All About Feedback (157-163) o When Success Is Probabilistic, Focus on Process (167-174) o How to Live in a World That We Do Not Understand (190-195) o Mean Reversion Mistakes (200-206)
Some of the most valuable material in the book focuses on the difficulties that result from attempting to "untangle" skill and luck. (Many people are unaware of that "tangle.") Mauboussin suggests that there is "a basic desire to find cause and effect in every situation, whether or not that view represents reality." He identifies several reasons that help to explain misjudgments. They include the aforementioned desire to see causality where none exists, the regency bias, mean reversion, nd the sample bias. He also has much of value to say about what I characterize as "reverse causality," indicated by the assertion that wet highways cause rain. How to improve the odds when making an important decision? Be sure to check out "Ten Suggestions to Improve the Art of Good Guesswork in a World That Combines Skill and Luck" (Pages 215-233).
No brief commentary such as mine can possibly do full justice to the scope and depth of material that Michael Mauboussin provides in this volume but I hope that I have at least suggested why I think so highly of him and his work. Also, I hope that those who read this commentary will be better prepared to determine whether or not they wish to read it and, in that event, will have at least some idea of how the information, insights, and wisdom could perhaps be of substantial benefit to them and to their own organization.