We reviewed in an earlier article Daniel Kahnemann's Prospect Theory, his latest book "Thinking Fast and Slow" and some of the key findings of Behavioural Finance and we'll be discussing a number of the implications for investors in a subsequent piece...
However, in this article, we'd like to dwell in one interesting discussion in Chapter 21 of the book- "Intuition vs Formulas". This discusses in some detail the efficacy and value of checklists & algorithms in addressing some of the predictable flaws in human decision-making -users of Stockopedia will already know that these kinds of tools are a key part of our feature set.
The Efficacy of Checklists
Kahnemann writes that a key source of inspiration for his work was the book, Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence by Paul Meehl. Meehl was an American psychologist who studied the successes and failures of predictions in many different settings in the 1940s. He found overwhelming evidence that predictions based on mechanical (formal, algorithmic) methods of data combination outperformed clinical (e.g., subjective, informal, "in the head") methods based on expert judgement.
A famous example confirming Meehl’s conclusion is the “Apgar score,” invented by the anesthesiologist Virginia Apgar in 1953 to guide the treatment of newborn babies. The Apgar score is a simple formula based on five vital signs that can be measured quickly: Appearance, Pulse, Grimace, Activity,Respiration. It does better than the average doctor in deciding whether the baby needs immediate help. It is now used everywhere and saves the lives of thousands of babies.
Another amusing example of the power of statistical prediction is the Dawes formula for the durability of marriage. This formula apparently does better than the average marriage counselor in predicting whether a marriage will last. The formula is:
“frequency of love-making minus frequency of quarrels.”
Similarly, Andrew McAfee of the Harvard Business Review references a 2000 paper which surveyed 136 studies in which human judgment was compared to algorithmic prediction. Only eight of the studies found that people were significantly better predictors of the task at hand. He also cites the case of Princeton economist Orley Ashenfleter who predicts Bordeaux wine quality using a simple model he developed that takes into accountwinter and harvest rainfall and temperature. Although wine…