“Recursive forecasting: Eliciting long-term forecasts from myopic fitness-seekers” by Jozdien, Alex Mallen

About this episode

The episode discusses a method called recursive forecasting to elicit long-term predictions from AI models that focus on short-term performance.

We’d like to use powerful AIs to answer questions that may take a long time to resolve. But if a model only cares about performing well in ways that are verifiable shortly after answering (e.g., a myopic fitness seeker), it may be difficult to get useful work from it on questions that resolve much later. In this post, I’ll describe a proposal for eliciting good long-horizon forecasts from these models. Instead of asking a model to directly predict a far-future outcome, we can recursively: Ask it to predict what it will predict at the next time step, Use its prediction at the next time step to provide intermediate rewards, Finally reward using ground truth at the last step. This lets us replace a single distant forecast with a chain of short-horizon forecasts, each verifiable shortly after answering. I call this proposal recursive forecasting. It does have limitations: for example, it requires that developers maintain control over the reward signal at least until the final step, which makes it most useful for forecasting events that resolve well before developers are disempowered (if they are). This post was primarily written by Arun, with most of the ideas in this post [...]…

People in this episode

Guests: Jozdien, Alex Mallen

Topics covered

• forecasting
• AI
• long-term predictions
• myopic fitness-seekers
• recursive forecasting

Keywords

• AI
• forecasting
• long-term
• myopic
• predictions
• recursive

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