What is epistemic arrogance? Who’s guilty of epistemic arrogance, and how does thinking we know what we actually don’t get us into trouble?<\/p>\n\n\n\n
Epistemic arrogance is the tendency to overestimate our ability to predict when we\u2019re overconfident in our knowledge. We’re all guilty of epistemic arrogance, but it hits “experts” the hardest.<\/p>\n\n\n\n
We’ll cover how epistemic arrogance leads to bad predictions<\/a> and why more information isn’t always better.<\/p>\n\n\n\n\n\n\n\n Before we dive into epistemic arrogance, let’s look at why predictions are really lies.<\/p>\n\n\n\n With the rapid advance of technology\u2014computer chips, cellular networks, the Internet\u2014it stands to reason that our predictive capabilities too are advancing. But consider how few of these groundbreaking advances in technology were themselves <\/em>predicted. For example, no one predicted the Internet, and it was more or less ignored when it was created. (Shortform note: It\u2019s unclear how Taleb<\/a> defines \u201cpredicted.\u201d Plenty of science-fiction writers and cultural commentators anticipated recent technologies like the Internet and augmented and virtual reality.) It is an inconvenient truth that humans\u2019 predictive capabilities are extremely limited; we are continuously faced with catastrophic or revolutionary events that arrive completely unexpectedly and for which we have no plan. Yet, nevertheless, we maintain that the future is knowable and that we can adequately prepare for it. Taleb calls this tendency the scandal of prediction<\/em>. But our obvliviousness to the scandal can lead to epistemic arrogance. The reason we overestimate our ability to predict is that we\u2019re overconfident in our knowledge. This is epistemic arrogance. A classic illustration of epistemic arrogance comes from a study conducted by a pair of Harvard researchers. In the study, the researchers asked subjects to answer specific questions with numerical ranges. (A sample question might be, \u201cHow many redwoods are there in Redwood Park in California?\u201d To which the subject would respond, \u201cI\u2019m 98% sure there are between x <\/em>and y <\/em>number of redwoods.) The researchers found that the subjects, though they were 98% sure of their answers, ended up being wrong 45% of the time! (Fun fact: The subjects of the study were Harvard MBAs.) In other words, the subjects picked overly narrow ranges because they overestimated they own ability to estimate<\/em>. If they had picked wider ranges\u2014and, in so doing, acknowledged their own lack of knowledge\u2014they would have scored much better.<\/p>\n\n\n\n Taleb calls our overconfidence in our knowledge \u201cepistemic arrogance.\u201d On the one hand, we overestimate what we know; on the other, we underestimate what we don\u2019t\u2014uncertainty<\/em>. It\u2019s important to recognize that Taleb isn\u2019t talking about how much or how little we actually know, but rather the disparity between what we know and what we think <\/em>we know. We\u2019re arrogant because we think we know more than we actually do<\/em><\/strong>. This epistemic arrogance leads us to draw a distinction between \u201cguessing\u201d and \u201cpredicting.\u201d Guessing is when we attempt to fill in a nonrandom<\/em> variable based on incomplete information, whereas predicting is attempting to fill in a random<\/em> variable based on incomplete information. Say, for example, someone asks you to estimate how many natural lakes there are in Georgia. There\u2019s a right answer to the question\u2014it\u2019s 0\u2014but you don\u2019t know it, so your answer is a \u201cguess.\u201d But say that same someone asks you what the U.S. unemployment rate will be in a year. You might look at past figures, GDP growth, and other metrics to try and make a \u201cprediction.\u201d But the fact is, your answer will still be a \u201cguess\u201d<\/strong>\u2014there are just too many factors (unknown unknowns<\/a>) to venture anything better than a guess. If you think the figures give you answers, you’re demonstrating epistemic arrogance. It stands to reason that the greater our information is about a particular problem, the more likely we are to come upon a solution. And the same goes, it would seem, for predictions: The more information we have to make <\/em>a prediction, the more accurate our prediction will be. But an array of studies shows that an increase in information actually has negligible\u2014and even <\/strong>negative<\/em><\/strong>\u2014effects on our predictions<\/strong>. For example, the psychologist Paul Slovic conducted a study on oddsmakers at horse tracks. He had the oddsmakers pick the ten most important variables for making odds, then asked the oddsmakers to create odds for a series of races using only those variables. In the second part of the experiment, Slovic gave the oddsmakers ten more <\/em>variables and asked them to predict again. The accuracy of their predictions was the same<\/strong> (though their confidence <\/em>in their predictions increased significantly). The more information you have, the higher your epistemic arrogance. The negative outcome of an increase in information is that we become increasingly sure of our predictions even as their accuracy remains constant<\/strong>. <\/p>\n\n\n\n The most epistemically arrogant group in terms of their predictions\u2014and least aware of their own ignorance\u2014are so-called \u201cexperts.\u201d These are the credentialed and\/or laureled people whose opinions are granted weight by society. Taleb divides this group by two. There are those who are arrogant but also display some degree of competence, and then there are those who are arrogant and <\/em>incompetent. \u201cCompetent Arrogants\u201d are those experts with actual predictive abilities and discernible skills. This group includes astronomers, physicists, surgeons, and mathematicians<\/strong> (when dealing exclusively with pure, rather than applied, mathematics). \u201cIncompetent Arrogants\u201d are \u201cexperts\u201d whose predictive abilities and skills aren\u2019t significantly greater than the average person\u2019s. This group includes stockbrokers, intelligence analysts, clinical psychologists, psychiatrists, economists, finance professors, and personal financial advisors<\/strong>. These people are sufferers of epistemic arrogance.<\/p>\n\n\n\n In numerous empirical studies, the forecasting ability of the \u201cincompetent arrogants\u201d has been shown to be almost nonexistent. For example, over the course of several years, psychologist Philip Tetlock asked almost 300 specialists\u2014political scientists, economists, journalists, and politicians\u2014to offer predictions of world events (the timeframe was usually \u201cwithin the next five years\u201d). He discovered that the experts\u2019 predictions were barely more accurate than random selection and often worse than simple computer simulations<\/strong>. He also found that the more prominent a person was in his or her field, the worse were his or her predictions.<\/em> The reason for this finding was that prominent people tend to become prominent based on their having one big idea<\/em>. These experts marry themselves to their singular idea and neglect other possibilities\u2014and thus, when randomness rears its head, they\u2019re shown to have been woefully misguided. Still, they maintain their epistemic arrogance. Tetlock\u2019s main interest, however, wasn\u2019t the fact of experts\u2019 poor forecasting abilities but rather those experts\u2019 lack of accountability<\/a> <\/em>for being wrong. He found that experts (unconsciously) employ a number of excuses to explain away their errors. The Scandal of Prediction<\/strong><\/h2>\n\n\n\n
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<\/p>\n\n\n\nEpistemic Arrogance<\/strong><\/h2>\n\n\n\n
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<\/p>\n\n\n\nThe Curse of Information<\/strong><\/h3>\n\n\n\n
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<\/p>\n\n\n\nExperts\u2014The Worst Offenders<\/strong> of Epistemic Arrogance<\/h2>\n\n\n\n
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<\/p>\n\n\n\n1) Competent Arrogants<\/strong>
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<\/p>\n\n\n\n2) Incompetent Arrogants<\/strong>
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<\/p>\n\n\n\nDefenses of the Epistemically Arrogant<\/h2>\n\n\n\n