Simple Forecasting Metrics? Iâve been thinking about the simplicity of explaining certain forecasting concepts versus the complexity of others. Take calibration, for instance: itâs simple to explain. If someone says something is 80% likely, it should happen about 80% of the time. But other metrics, like the Brier score, are harder to convey: What exactly does it measure? How well does it reflect a forecasterâs accuracy? How do you interpret it? All of this requires a lot of explanation for anyone not interested in the science of Forecasting.
What if we had an easily interpretable metric that could tell you, at a glance, whether a forecaster is accurate? A metric so simple that it could fit within a tweet or catch the attention of someone skimming a reportâsomeone who might be interested in platforms like Metaculus. Imagine if we could say, âWhen Metaculus predicts something with 80% certainty, it happens between X and Y% of the time,â or âOn average, Metaculus forecasts are off by X%â. This kind of clarity could make comparing forecasting sources and platforms far easier.
Iâm curious whether anyone has explored creating such a concise metricâone that simplifies these ideas for newcomers while still being informative. It could be a valuable way to persuade others to trust and use forecasting platforms or prediction markets as reliable sources. Iâm interested in hearing any thoughts or seeing any work that has been done in this direction.
Imagine if we could say, âWhen Metaculus predicts something with 80% certainty, it happens between X and Y% of the time,â or âOn average, Metaculus forecasts are off by X%â.
Fyi, the Metaculus track recordâthe âCommunity Prediction calibrationâ part, specificallyâlets us do this already. When Metaculus predicts something with 80% certainty, for example, it happens around 82% of the time:
Thank you for the response! I should have been a bit clearer: This is what inspired me to write this, but I still need 3-5 sentences to explain to a policymaker what they are looking at when you show them this kind of calibration graph. I am looking for something even shorter than that.
Simple Forecasting Metrics?
Iâve been thinking about the simplicity of explaining certain forecasting concepts versus the complexity of others. Take calibration, for instance: itâs simple to explain. If someone says something is 80% likely, it should happen about 80% of the time. But other metrics, like the Brier score, are harder to convey: What exactly does it measure? How well does it reflect a forecasterâs accuracy? How do you interpret it? All of this requires a lot of explanation for anyone not interested in the science of Forecasting.
What if we had an easily interpretable metric that could tell you, at a glance, whether a forecaster is accurate? A metric so simple that it could fit within a tweet or catch the attention of someone skimming a reportâsomeone who might be interested in platforms like Metaculus. Imagine if we could say, âWhen Metaculus predicts something with 80% certainty, it happens between X and Y% of the time,â or âOn average, Metaculus forecasts are off by X%â. This kind of clarity could make comparing forecasting sources and platforms far easier.
Iâm curious whether anyone has explored creating such a concise metricâone that simplifies these ideas for newcomers while still being informative. It could be a valuable way to persuade others to trust and use forecasting platforms or prediction markets as reliable sources. Iâm interested in hearing any thoughts or seeing any work that has been done in this direction.
Fyi, the Metaculus track recordâthe âCommunity Prediction calibrationâ part, specificallyâlets us do this already. When Metaculus predicts something with 80% certainty, for example, it happens around 82% of the time:
Thank you for the response! I should have been a bit clearer: This is what inspired me to write this, but I still need 3-5 sentences to explain to a policymaker what they are looking at when you show them this kind of calibration graph. I am looking for something even shorter than that.