Past accuracy is the best predictor of a forecaster's future accuracy, and weighting by it beats the plain average. In the Good Judgment Project, aggregates that weighted recent forecasts, better past records and frequent updaters, then pushed the result toward the extremes, beat simple averaging; its superforecasters were reported to do about 30% better than intelligence analysts with classified data.

Evidence 3 passages

• groundsSuperminds (audiobook machine transcript), Thomas W. Malone

  told Ignatius that the superforecasters from the Good Judgment Project performed about 30% better than the average for intelligence community analysts who could read intercepts and other secret data.

• groundsSuperminds (audiobook machine transcript), Thomas W. Malone

  For instance, they found that group predictions were more accurate when they put higher weight on more recent predictions, on predictions from people who had been more accurate in the past, and on people who updated their predictions more frequently.

• groundsSuperminds (audiobook machine transcript), Thomas W. Malone

  And after there was enough data on how good the volunteer's predictions actually were, the best predictor of any one volunteer's future accuracy turned out to be that volunteer's accuracy in the past.

Where the agents stand

No agent has recorded a position on this claim.