Kahneman continues to reference cases of experienced medical doctors predicting the longevity of cancer patients, predicting the susceptibility of babies to sudden death syndrome, and predicting new business success and evaluations of credit risk. His examples range 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.
Jonah Lehrer (2009) similarly referenced studies conducted at MIT in which students given access to large amounts of data performed poorly in predicting stock prices when compared with a control group of students with access to far less information. He notes that the prefrontal cortex of the brain has great difficulty NOT paying attention to large amounts of information which can overwhelm the ability of the brain to estimate and predict. Lehrer concludes that access to excessive quantities of information can have “diminishing returns” when conducting assessments and predicting future outcomes.
Lehrer observes that corporations, in particular, often fall into the “excessive information” trap. Leaders of these organizations tend to invest huge amounts of resources in collecting data. This date, in turn, can overwhelm and confuse the human brain—just the opposite from the intended outcome of informed decision-making. Lehrer describes the challenging situation faced by medical doctors who were diagnosing back pain several decades ago. With the introduction of MRI in the 1980’s and with far greater detail available, medical practitioners hoped that increasingly better predictions of the sources of back pain would be made. The converse happened.
Massive amounts of detail produced by the MRI actually worsened their assessment and predictive capabilities. Poorer assessments were made. Goldberg and his colleagues at ORI were further vindicated. Kahneman refers to scenarios that contain a high level of complexity, uncertainty and unpredictability as “low-validity environments”. Experts can become overwhelmed by complexity when engaged in decision making. Leadership coaches can assist greatly by developing checklists or other simple decision support tools that help to limit biases and confusion arising from data overload.
The power of something as simple as a checklist has been shown by Kahneman to have “saved hundreds of thousands of infants”. He offers the example of assessing the health status of newborn infants. We can go back a few decades. Obstetricians had always known that an infant who is not breathing normally within a few minutes of birth is at high risk for brain damage or death. Physicians and midwives through the 1950’s typically used their varying levels of medical judgment to determine whether a baby was in distress. Different practitioners used their own experience—focuses on specific signs and symptoms to determine the level and extent of this distress. Looking at these different symptoms meant that danger signs were often overlooked or missed. Many newborn babies died.
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