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This week in our re-read of Thinking, Fast and Slow, we have a chapter that needs to be read by anyone ever been asked for an estimate… ever. There are three questions that have been asked since the dawn of time:  

  1. What will “it” cost?
  2. When will “it” be done?
  3. What is “it” that I am going to get? 

Almost every person, team and/or organization is called on to answer these questions on a regular basis, regardless of method. Answering the three questions has spawned a sea of consultants, not because estimators are bad actors but rather because an inside view is often optimistic. In software development, estimates are chronically optimistic for a multitude of reasons.  The Software Process and Measurement Cast has interviewed several academics on the topic over the years, one of the most memorable was with Ricardo Valerdi. Kahneman’s discussion of the planning fallacy in this chapter illustrates why optimism is such a problem. (more…)

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This week in our re-read of Thinking, Fast and Slow, Kahneman opens with a discussion of a number of studies that show that professional predictions are far less accurate than simple algorithmic predictions. The work that sends Kahneman down this was originally done by Paul Meehl and published in the book Clinical versus Statistical Prediction: A theoretical analysis and a review of the evidence (1954). At the time of publication of Thinking Fast and Slow decades later, studies across a wide range of subjects show that formulas beat intuition at least 60% of the time. Bluntly stated, formulas beat intuition most of the time – the idea that algorithms are powerful should surprise no one in 2019. (more…)

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Last night severe thunderstorms rolled through northern Ohio.  There were lots of power outages and trees that were blown over.  This morning when I went to the grocery store, the store’s systems could not accept debit cards. I immediately made up a story that connected the storms to system failure. As we have seen before, System 1 thinking takes disparate facts and creates a coherent believable story.  No conclusion is too big a jump for System 1 thinking. My story and my belief that I had predicted the most probable cause is an illusion of validity.    (more…)

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Part 3 of Thinking, Fast and Slow is titled Overconfidence.  Chapter 19 begins by exploring several biases that affect overconfidence. Earlier in the book, we explored how System 1 thinking connects events to generate a coherent story.  This chapter begins by building on the attributes of fast thinking by stating that humans interpret behavior as a manifestation of general propensities and personal traits. One of the classic biases that cause this type of thinking is the halo effect. I overheard an example of a negative halo effect this week as I walked behind a group of people in Chicago. The group, tourists, pointed at a person sleeping rough along the river and exclaimed that the person was lazy.  One attribute of the person’s behavior was generalized into a larger narrative.   (more…)

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I know this is not Saturday however, I was not able to post week 18 last weekend because I was traveling to Canada to pick up a puppy and bring her home.  So I am trying to catch up!  Week 18 of our re-read of Thinking, Fast and Slow tackles Chapter 18, Taming Intuitive Predictions. (more…)

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Why does leadership bring a release home to great adulation only to have the next release crash and burn? Did the leader’s skill change between releases or were other random factors, such as luck, involved. Kahneman suggests a simple formula as a thought experiment.  Success = skill + luck. Chapter 17 of Thinking, Fast and Slow, Regression To The Mean, discusses correlation and causal interpretation.   (more…)

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Chapter 16, Causes Trump Statistics, was revelatory for me the first time I read  Thinking, Fast and Slow, and it was revelatory during this read. Over my career, I have been shocked many times to see a perfectly sane leader stand up and show a single statistic or estimate which promises delivery of a product at a cost or in a timeframe that is well outside of normal performance.  This chapter provides a rationale for what often seems to be less than rational. The content in this chapter helps me understand why statistical facts aren’t perceived to generate black and white answers, even when they do. Kahneman uses a story about taxi cabs to illustrate the difference between statistical base rates and causal base rates.  Statistical base rates are facts about the population but are not specific to any individual case. Causal base rates are effective because they are specific and are easily woven into a narrative about the case.   (more…)