Why do you make bad predictions? Is there any way to make better predictions?<\/p>\n\n\n\n
According to Nassim Nicholas Taleb<\/a> in The Black Swan<\/a><\/em>, some of the most world-altering events are unpredictable. Although many day-to-day events can be predicted, humans are bad at making accurate predictions about many major events. We’ll look at 12 fallacies and cognitive biases<\/a> that encourage your bad predictions.<\/p>\n\n\n\n\n\n\n\n The Black Swan is named after a classic error of induction wherein an observer assumes that because all the swans he\u2019s seen are white, all swans must <\/em>be white. Black Swans have three salient features: Taleb\u2019s thesis, however, is that Black Swans, by their very nature, are<\/strong> always <\/em><\/strong>unpredictable<\/strong>\u2014they are the \u201cunknown unknowns\u201d <\/em>for which even our most comprehensive models can\u2019t account. The fall of the Berlin Wall<\/a>, the 1987 stock market crash, the creation of the Internet, 9\/11, the 2008 financial crisis\u2014all are Black Swans. We all tend to think <\/em>we have a grasp of what\u2019s going on in the world when, in fact, the world is far more complex than we know. For example, all the adults around Taleb predicted the civil crisis would last a matter of days (it ended up lasting around 17 years). Despite the fact that events kept contradicting people\u2019s forecasts, people acted each day as though nothing exceptional had occurred. History always appears predictable (or, at least, explainable) in our retrospective accounts of events. In the case of the Lebanese Civil War, the adults whose forecasts were continually proved incorrect were always able to explain the surprising events <\/strong>after the fact<\/em><\/strong>. In other words, the events always were indeed<\/em> predictable, but one could only predict them after they\u2019d already happened<\/em>. We accumulate information and listen to expert analysis of that information, but these elements never measure up to real events.<\/p>\n\n\n\n Taleb cites the example of his grandfather, who eventually rose to deputy prime minister of Lebanon. Although his grandfather was an educated man with years of experience in politics, his forecasts were proven wrong as routinely as those of his uneducated driver. Neither knew more than the other about the twists and turns of the war<\/strong>. All too often we draw universal<\/em> conclusions from a particular <\/em>set of facts. For example, if we were presented with evidence that showed a turkey had been fed and housed for 1,000 straight days, we would likely predict the same for day 1,001 and for day 1,100. Taleb calls this prediction the \u201cround-trip fallacy.\u201d When we commit the round-trip fallacy, we assume that \u201cno evidence of <\/strong>x<\/em><\/strong>\u201d\u2014where <\/strong>x <\/em><\/strong>is any event or phenomenon\u2014is the same as \u201cevidence of no <\/strong>x<\/em><\/strong>.\u201d<\/strong> For example, in the turkey illustration, we might assume that \u201cno evidence of the possibility of slaughter\u201d equals \u201cevidence of the impossibility of slaughter.\u201d To take a medical example, if a cancer screening comes back negative, there is \u201cno evidence of cancer,\u201d not \u201cevidence of no cancer\u201d (because the scan isn\u2019t perfect and could have missed something). In addition to drawing broad conclusions from narrow observations, we also have a tendency to select<\/em> evidence on the basis of preconceived frameworks, biases, or hypotheses. For example, a scientist conducting an experiment may, often unconsciously, discount evidence that disconfirms her hypothesis in favor of the evidence that confirms it. Taleb calls this habit \u201cnaive empiricism,\u201d but it\u2019s more commonly known as \u201cconfirmation bias.\u201d Taleb\u2019s solution to naive empiricism\/confirmation bias is negative empiricism<\/em>\u2014the rigorous search for disconfirming, rather than corroborating, evidence. This technique was pioneered by a philosopher of science named Karl Popper, who called it \u201cfalsification.\u201d The reason negative empiricism\/falsification is so effective is that we can be far more sure of wrong answers than right ones<\/strong>. Because humans are naturally inclined to stories, with distinct causes and effects, we are perennially in danger of committing the \u201cnarrative fallacy\u201d\u2014the ascription of meaning or cause to random events. Our tendency to narrativize is part and parcel of our compulsion to interpret<\/em>. Humans are evolutionarily conditioned\u2014by the development of the left hemisphere of our brains\u2014to reduce the complexity of the world\u2019s information (we\u2019ll discuss why in a moment); and the most efficient way of simplifying that complexity is through interpretation.<\/p>\n\n\n\n Neurotransmitters in the brain<\/a>, too, encourage interpretation. When patients are administered dopamine supplements, they become more likely to see patterns where there are none. Why are humans predisposed to interpretation? For a very practical reason: It makes information easier for our brains to store. Whereas retaining 100 randomly ordered numbers would be near impossible, retaining 100 numbers that were ordered according to a specific rule <\/em>would be much easier. When we interpret\u2014or narrativize\u2014we\u2019re attempting to impose our own organizing rule on the random facts of the world<\/strong>. History\u2014which Taleb defines as \u201cany succession of events seen with the effect of posterity\u201d\u2014is inevitably, necessarily distorted. That is, no matter how \u201cfactual\u201d or \u201cobjective,\u201d the historical record is always a product of our tendency to narrate and thus always biased<\/em>. What the narratives are biased <\/strong>against <\/em><\/strong>is <\/strong>randomness<\/em><\/strong>\u2014the inexplicability and importance of Black Swans<\/strong>. Take most CEOs\u2019 and entrepreneurs\u2019 (auto)biographies. These books attempt to draw a causal link between the CEO\/entrepreneur\u2019s (a) character traits, education, and business acumen and (b) later success. The \u201csilent evidence\u201d (which Taleb also calls \u201cthe cemetery\u201d) in these narratives is that there are many more people with the same attributes as the triumphant CEOs\/entrepreneurs who <\/strong>failed<\/em><\/strong>. <\/strong>The fact is, in business, like in so many other fields, the deciding factor is nothing other than <\/strong>luck <\/em><\/strong>(i.e., randomness).<\/strong> Once we become attuned to the existence of \u201csilent evidence\u201d\u2014which we can think of as the \u201cflipside\u201d or contrary to any story we\u2019re told\u2014we can see it everywhere. A repercussion of the Distortion of Silent Evidence<\/a>, \u201ctunneling\u201d describes the natural human tendency to favor knowns <\/em>and known unknowns<\/em> rather than unknown unknowns<\/a><\/em>. In other words, our understanding of uncertainty is based almost exclusively on what <\/strong>has happened<\/em><\/strong> in the past rather than what <\/strong>could have happened<\/em><\/strong>. The primary practitioners of tunneling are those Taleb calls \u201cnerds\u201d\u2014academics, mathematicians, engineers, statisticians, and the like. Nerds are those who think entirely \u201cinside the box\u201d; they Platonify the world and can\u2019t perceive possibilities that lie outside their scientific models and academic training.Are Bad Predictions Inevitable?<\/h2>\n\n\n\n
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<\/p>\n\n\n\nBad Prediction Reason #1: The Illusion of Understanding<\/strong><\/h4>\n\n\n\n
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<\/p>\n\n\n\nBad Prediction Reason #2: The Retrospective Distortion<\/strong><\/h4>\n\n\n\n
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<\/p>\n\n\n\nBad Prediction Reason #3: The Overvaluation of Facts and the Flaw of Expertise<\/strong><\/h4>\n\n\n\n
Newspapers, too, did nothing to help the Lebanese understand the war. They communicated information, but they didn\u2019t make anyone\u2019s predictions any more accurate. The reporters of the war also tended to \u201ccluster\u201d\u2014emphasizing the same details and using the same categories as each other. Clustering reduces the complexity of the world\u2014and leaves us vulnerable to Black Swans.<\/p>\n\n\n\nBad Prediction Reason #4: The Error of Confirmation<\/strong><\/h4>\n\n\n\n
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<\/p>\n\n\n\nBad Prediction Reason #5: The Narrative Fallacy<\/strong><\/h4>\n\n\n\n
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<\/p>\n\n\n\nBad Prediction Reason #6: The Distortion of Silent Evidence<\/strong><\/h4>\n\n\n\n
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<\/p>\n\n\n\nBad Prediction Reason #7: Our Tendency to \u201cTunnel\u201d<\/strong><\/h4>\n\n\n\n
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