Mechanisms to overcome “ignorance”
A case for standardized checklists, algorithms and simple rules to reduce complexity
Daniel Kahneman (2011), the psychologist and Nobel Prize winner in economics, quotes Paul Meehl, (who Kahneman rates as “one of the most versatile psychologists of the twentieth century”), as saying that one reason experts are almost always outperformed in predictive capabilities by simple algorithms, is that they think they are quite capable of dealing with massive amounts of data and information – and they are almost always wrong. They know that they are very smart people – but they “try to be (too) clever, think outside the box and consider complex combinations of features in making predictions – Complexity (most often) reduces validity”. Many studies have shown that human decision-makers are inferior to relatively simple formulae, statistics and checklists when assessing and making decisions about the success of complex scenarios such as mergers and acquisitions amongst others. In research studies, even when smart people are given the result provided by formulae, these same people tend to overrule it and ignore it because they feel that they have more knowledge and information than that produced by the formulae. Kahneman notes that “they are most often wrong”.
Standardized approaches, simple algorithms and checklists can be very powerful tools. Atul Gawande, (2013), a general surgeon in Boston and assistant professor at Harvard Medical School, defines the power of checklists in this way:
“We (humans) have accumulated stupendous know-how. We have put it in the hands of some of the most highly skilled and hardworking people in our society. And with it they have accomplished extraordinary things. Nonetheless, that know-how is often unmanageable. Avoidable failures are common and persistent, not to mention demoralizing and frustrating across many fields – from finance, business to government. And the reason is increasingly evident: the volume and complexity of what we know has exceeded our individual ability to deliver its benefits correctly, safely and reliably. Knowledge has both saved us and burdened us” … but there is such a strategy (to solve this problem) – though it is almost ridiculous in its simplicity, maybe even crazy to those who have spent years carefully developing ever more advanced skills and technologies (and indeed is resisted in many companies for this reason). It is a checklist!
Kahneman puts forward his own personal judgment and predictive capabilities (or lack of) as a young military psychologist charged with assessing the leadership capabilities of aspiring officers; he was initially dismal at this task. He also highlights examples of poor capabilities of highly trained counselors predicting the success levels of college freshmen based on several aptitude tests and other extensive data compared to the predictive accuracy of a simple statistical algorithm using a fraction of the information available – the algorithm was more successful than the trained counselors by far. Kahneman continues to reference cases of experienced medical doctors predicting the longevity of cancer patients, the prediction of the susceptibility of babies to sudden death syndrome, predictions of new business success and evaluations of credit risk, all the way to marital stability and the ability to predict the future value of fine Bordeaux wines. In all these cases, the accuracy of highly trained experts was most often exceeded by simple algorithms, much to the consternation, occasional anger and derision of the experts concerned.
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