These forecasts are then used to power a search engine for probabilities, which can be found [here](https://metaforecast.org/) (try searching "Trump", "China" or "Semiconductors") (source code [here](https://github.com/QURIresearch/metaforecast-website-nextjs)). A json endpoint can be found [here](https://metaforecast.org/data/metaforecasts.json).
I also created a search engine using Elicit's IDE, which uses GPT-3 to deliver vastly superior semantic search (as opposed to fuzzy word matching). If you have access to the Elicit IDE, you can use the action "Search Metaforecast database".
Private session cookies are necessary to query CSET-foretell, Good Judgment Open and Hypermind. You can get these cookies by creating an account in said platforms and then making and inspecting a request (e.g., by making a prediction, or browsing questions). After doing this, you should create a `src/privatekeys.json`, in the same format as `src/privatekeys_example.json`
Star ratings—e.g. ★★★☆☆—are an indicator of the quality of an aggregate forecast for a question. These ratings currently try to reflect my own best judgment based on my experience forecasting on these platforms. Thus, stars have a strong subjective component which could be formalized and refined in the future.
Currently, stars are computed using a simple rule dependent on both the platform and the number of forecasts:
- CSET-foretell: ★★☆☆☆, but ★☆☆☆☆ if a question has less than 100 forecasts
- Elicit: ★☆☆☆☆
- Good Judgment (various superforecaster dashboards): ★★★★☆
- Good Judgment Open: ★★★☆☆, ★★☆☆☆ if a question has less than 100 forecasts
- Hypermind: ★★★☆☆
- Metaculus: ★★★★☆ if a question has more than 300 forecasts, ★★★☆☆ if it has more than 100, ★★☆☆☆ otherwise.
- Right now, I'm fetching only a couple of common properties, such as the title, url, platform, whether a question is binary (yes/no), its percentage, and the number of forecasts. However, the code contains more fields commented out, such as trade volume, liquidity, etc.
