future accurately, but that’s often not the goal, or at least not the sole goal. Sometimes forecasts are meant to entertain.

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.

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 w

aggregating the judgments of an equal number of people who know lots about lots of different things is most effective because the collective pool of information becomes much bigger.

In 1972 the American meteorologist Edward Lorenz wrote a paper with an arresting title: “Predictability: Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?” A decade earlier, Lorenz had discovered by accident that tiny data entry variations in computer simulations of weather patterns—like replacing 0.506127 with 0.506—could

he created a database containing hundreds of information sources—from the New York Times to obscure blogs—that are tagged by their ideological orientation, subject matter, and geographical origin, then wrote a program that selects what he should read next using criteria that emphasize diversity.

Obviously, a forecast without a time frame is absurd. And yet, forecasters routinely make them, as they did

Foxes beat hedgehogs on both calibration and resolution.

System 1 comes first. It is fast and constantly running in the background. If a question is asked and you instantly know the answer, it sprang from System 1. System 2 is charged with interrogating that answer. Does it stand up to scrutiny? Is it backed by evidence?