Embedded Interactive Predictions on LessWrong
Ought and LessWrong are excited to launch an embedded interactive prediction feature. You can now embed binary questions into LessWrong posts and comments. Hover over the widget to see other people’s predictions, and click to add your own.
Try it out
How to use this
Create a question
Go to elicit.org/binary and create your question by typing it into the field at the top
Click on the question title, and click the copy button next to the title – it looks like this:
Paste the URL into your LW post or comment. It’ll look like this in the editor:
Make a prediction
Click on the widget to add your own prediction
Click on your prediction line again to delete it
Link your accounts
Linking your LessWrong and Elicit accounts allows you to:
Filter for and browse all your LessWrong predictions on Elicit
Add notes to your LessWrong predictions on Elicit
See your calibration for your LessWrong predictions on Elicit
Predict on LessWrong questions in the Elicit app
To link your accounts:
Make an Elicit account
Send me (email@example.com) an email with your LessWrong username and your Elicit account email
We hope embedded predictions can prompt readers and authors to:
Actively engage with posts. By making predictions as they read, people have to stop and think periodically about how much they agree with the author.
Distill claims. For writers, integrating predictions challenges them to think more concretely about their claims and how readers might disagree.
Communicate uncertainty. Rather than just stating claims, writers can also communicate a confidence level.
Collect predictions. As a reader, you can build up a personal database of predictions as you browse LessWrong.
Get granular feedback. Writers can get feedback on their content at a more granular level than comments or upvotes.
By working with LessWrong on this, Ought hopes to make forecasting easier and more prevalent. As we learn more about how people think about the future, we can use Elicit to automate larger parts of the workflow and thought process until we end up with end-to-end automated reasoning that people endorse. Check out our blog post to see demos and more context.