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The Superiority of Algorithm-Driven Interview Techniques

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The last of the ideas from Daniel Kahneman’s book, Thinking, Fast and Slow, I’m going to write up is the superiority of algorithm-driven interview techniques in personnel selection.  This is related to his thinking about when to trust intuition I wrote up in a previous post.

Kahneman says that hundreds of studies have been completed comparing the effectiveness of clinical vs. statistical predictions of long-term mental health in psychology.  Sixty percent show statistical prediction is better; the remainder show no difference, but statistical prediction is much cheaper.

Kahneman believes, therefore, that for predictions in similarly “noisy” environments (he calls them “low-validity”)—such as personnel decisions—we should also use algorithms rather than intuitions to make our selections.  He thinks algorithms are superior for four reasons:

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When to Trust Intuition

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Over the past few days I’ve been commenting on Daniel Kahneman’s book, Thinking, Fast and Slow.  I’ve written about the advice he has for avoiding biases in planning and forecasting and about how loss aversion may help explain why it is so common for companies to throw good money after bad.

Today, I want to focus on Kahneman’s thoughts about when to trust intuition, which he developed with fellow psychologist Gary Klein.

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Loss Aversion, and Why Companies Throw Good Money After Bad

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Yesterday I discussed how much I admired Daniel Kahneman’s book, Thinking, Fast and Slow, and wrote about the advice he has for avoiding biases in planning and forecasting.

Today, I want to focus on Prospect Theory, loss aversion, and how these explain why it seems companies often throw good money after bad.

Loss aversion is a key part of Prospect Theory, for which Kahneman won his Nobel.  Simply put, loss aversion says that humans are more averse to losses than they are happy with gains.  Few people will take a bet with equal chance to lose $1000 or win $1000;  they feel the pain of losing $1000 more intensely than the joy of winning $1000.

Going further, although they are economically equivalent, we feel better about a 90% chance of a $1000 loss than a 100% chance of a $900 loss.  One reason is that we are subject to decreasing sensitivity to losses as they get larger.  We experience a $900 loss more than 90% as intensely as a $1000 loss.  We are also subject to a certainty effect. We give less psychological weight than warranted to events that are highly probable but not certain.

These factors make managers prone to unfortunate gambles when faced with difficult choices.

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Thinking, Fast and Slow: Avoiding Biases In Planning and Forecasting

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I recently finished reading—no, devouring, Daniel Kahneman’s masterpiece of a book, Thinking, Fast and Slow.

Kahneman is the winner of the 2002 Nobel prize in economics, even though he’s technically a psychologist.  Most of his work has focused on the psychology of decision making.

Thinking is one of the best business books I’ve read in the past several years.  Basically, it is a comprehensive summary of many of the major findings of psychological research from the past 50 years as applied to problems we commonly face in business and other organizations.

There were four topics in the book I found particularly interesting:

  1. Biases in planning and forecasting, and how to avoid them
  2. Loss aversion, and why companies throw good money after bad
  3. When to trust intuition
  4. The superiority of algorithms in personnel selection

I’ll cover the first topic in this post and summarize the others in subsequent posts.

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Solar Dealers Spend $5373 to Acquire Each Customer

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Woodlawn recently examined the sales and marketing practices of residential solar dealer/installers in the United States.  We collected detailed quantitative and qualitative information from a representative cross section of U.S. solar installers.  With this, we were able to answer questions such as:

  • What is the average solar customer acquisition cost? (An average of $5373, or 16.9% of revenue)
  • What is the “typical” marketing mix?
  • Which channels account for the most customers?
  • Which marketing channels are most effective in terms of new customers per dollar spent?
  • How can dealers optimize their mix of channels?
  • How can dealers maximize the potential of each channel?

You can download an extract.  Please feel free to contact us with any questions.