CONCEPT
The Dart-Throwing Chimpanzee
Tetlock's methodological baseline: expert predictions, on average, performed no better than random guessing — a finding that became shorthand for the failure of credentialed expertise.
The dart-throwing chimpanzee is not a literal experimental subject but a statistical baseline representing chance performance. Tetlock compared expert forecasters' accuracy to what would be achieved by assigning probabilities randomly — the equivalent of a chimpanzee throwing darts at a board. Across 28,000 predictions, expert performance approximated this baseline. The comparison became the most quoted finding from Expert Political Judgment, functioning as both empirical result and rhetorical device. It challenged the authority of expertise by demonstrating that credentials, experience, and domain knowledge provided no systematic advantage in forecasting future events. The chimpanzee was never the point — the point was the superforecasters who beat the baseline consistently, proving that better judgment was possible.
In The You On AI Field Guide
The chimpanzee comparison gained traction because it was viscerally humiliating to the expert class. A twenty-year investment in education, a professional reputation built on analytical sophistication, fluency in the domain's technical vocabulary — none of it produced accuracy exceeding what a primate could achieve by accident.