What is an AI superforecaster? How AI puts honest odds on the future
An AI superforecaster researches a question about the future and gives a calibrated probability. How it works, how it's scored, and where it stands in 2026.
Read the articleA base rate is how often similar things have happened before. Start there, then adjust for the evidence: the surest way to avoid overconfident forecasts.

A base rate is how often something similar has happened before. It is the answer to the question: out of all the cases like this one, how many turned out this way? Good forecasters start there, then adjust for the specifics. Poor forecasters start with the story and never look at the numbers.
It is the simplest habit in forecasting, and one of the most powerful.
Daniel Kahneman and Dan Lovallo described two ways to approach a forecast in a 1993 paper on bold forecasts and timid choices (McKinsey summary):
The inside view tends to be optimistic, because it only sees the path to success. The outside view includes all the ways similar cases actually went wrong.
People routinely ignore base rates when a vivid story is in front of them. Research by Amos Tversky and Daniel Kahneman showed that we judge likelihood by how easily examples come to mind, and by how well a case fits a stereotype, rather than by how common it is (Tversky and Kahneman, 1974).
A classic example: a detailed, confident plan for a big project feels likely to finish on time. The base rate for large projects says otherwise.
Reference class forecasting, developed from Kahneman and Tversky's work and formalised by Bent Flyvbjerg, makes the outside view systematic (McKinsey, Flyvbjerg). Flyvbjerg's research on major projects found that cost and schedule forecasts are generally highly inaccurate and biased toward optimism, and that anchoring on similar past projects corrects much of that.
Say the question is whether a space agency will meet an ambitious launch date.
This is the kind of reasoning behind questions in the Sikt feed, such as whether China lands astronauts on the Moon before 2030.
Starting from the base rate protects against the most common forecasting failure: being too sure. It keeps calibration honest, and it is also the natural baseline for a Brier skill score: a forecaster who cannot beat "always predict the base rate" is not adding information.
It is also why AI superforecasters are built to find the base rate before reading the news. The news is the inside view. The base rate is the anchor. It is a big part of why AI forecasting improved so quickly (see can AI predict the future?).
How often an outcome has happened in past cases similar to the one you are forecasting. It is the starting point before you adjust for the specific evidence.
The inside view focuses on the details of the case in front of you. The outside view looks at how similar cases turned out. The outside view is usually more accurate, and good forecasts combine both.
A method that forecasts an outcome by looking at the distribution of outcomes for a set of similar past cases, the reference class, and anchoring on it.