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We can think of this type of iterative, exploratory work as ‘forensic’ statistics,
“When the facts change, I change my mind,” the legendary British economist John Maynard Keynes declared.”
― Superforecasting: The Art and Science of Prediction
― Superforecasting: The Art and Science of Prediction
“It is wise to take admissions of uncertainty seriously,” Daniel Kahneman noted, “but declarations of high confidence mainly tell you that an individual has constructed a coherent story in his mind, not necessarily that the story is true.”17”
― Superforecasting: The Art and Science of Prediction
― Superforecasting: The Art and Science of Prediction
“Fuzzy thinking can never be proven wrong. And only when we are proven wrong so clearly that we can no longer deny it to ourselves will we adjust our mental models of the world—producing a clearer picture of reality. Forecast, measure, revise: it is the surest path to seeing better.”
― Superforecasting: The Art and Science of Prediction
― Superforecasting: The Art and Science of Prediction
“Novice forecasters often ask why not just say 0.5, coin toss, whenever they “know nothing” about a problem. There are several reasons why not. One is the risk of being ensnared in self-contradictions. Imagine you are asked whether the Nikkei stock index will close above 20,000 by June 30, 2015. Knowing nothing, you say 0.5 chance. Now suppose you are asked whether it will close above 22,000—and you again say 0.5—or between 20,000 and 22,000, and you again say 0.5. The more possibilities the questioner unpacks, the more obvious it becomes that the casual user of 0.5 is assigning incoherent probabilities that far exceed 1.0. See Amos Tversky and Derek Koehler, “Support Theory: A Nonextensional Representation of Subjective Probability,” Psychological Review”
― Superforecasting: The Art and Science of Prediction
― Superforecasting: The Art and Science of Prediction
“In the EPJ results, there were two statistically distinguishable groups of experts. The first failed to do better than random guessing, and in their longer-range forecasts even managed to lose to the chimp. The second group beat the chimp, though not by a wide margin, and they still had plenty of reason to be humble. Indeed, they only barely beat simple algorithms like “always predict no change” or “predict the recent rate of change.” Still, however modest their foresight was, they had some. So why did one group do better than the other? It wasn’t whether they had PhDs or access to classified information. Nor was it what they thought—whether they were liberals or conservatives, optimists or pessimists. The critical factor was how they thought. One group tended to organize their thinking around Big Ideas, although they didn’t agree on which Big Ideas were true or false. Some were environmental doomsters (“We’re running out of everything”); others were cornucopian boomsters (“We can find cost-effective substitutes for everything”). Some were socialists (who favored state control of the commanding heights of the economy); others were free-market fundamentalists (who wanted to minimize regulation). As ideologically diverse as they were, they were united by the fact that their thinking was so ideological. They sought to squeeze complex problems into the preferred cause-effect templates and treated what did not fit as irrelevant distractions. Allergic to wishy-washy answers, they kept pushing their analyses to the limit (and then some), using terms like “furthermore” and “moreover” while piling up reasons why they were right and others wrong. As a result, they were unusually confident and likelier to declare things “impossible” or “certain.” Committed to their conclusions, they were reluctant to change their minds even when their predictions clearly failed. They would tell us, “Just wait.” The other group consisted of more pragmatic experts who drew on many analytical tools, with the choice of tool hinging on the particular problem they faced. These experts gathered as much information from as many sources as they could. When thinking, they often shifted mental gears, sprinkling their speech with transition markers such as “however,” “but,” “although,” and “on the other hand.” They talked about possibilities and probabilities, not certainties. And while no one likes to say “I was wrong,” these experts more readily admitted it and changed their minds. Decades ago, the philosopher Isaiah Berlin wrote a much-acclaimed but rarely read essay that compared the styles of thinking of great authors through the ages. To organize his observations, he drew on a scrap of 2,500-year-old Greek poetry attributed to the warrior-poet Archilochus: “The fox knows many things but the hedgehog knows one big thing.” No one will ever know whether Archilochus was on the side of the fox or the hedgehog but Berlin favored foxes. I felt no need to take sides. I just liked the metaphor because it captured something deep in my data. I dubbed the Big Idea experts “hedgehogs” and the more eclectic experts “foxes.” Foxes beat hedgehogs. And the foxes didn’t just win by acting like chickens, playing it safe with 60% and 70% forecasts where hedgehogs boldly went with 90% and 100%. Foxes beat hedgehogs on both calibration and resolution. Foxes had real foresight. Hedgehogs didn’t.”
― Superforecasting: The Art and Science of Prediction
― Superforecasting: The Art and Science of Prediction
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