A New Kind of Prediction Market for Science

Regardless of the actual singularity, we have definitely hit one in arXiv submissions. It is becoming apparent that the current paradigm of scientific knowledge production is not ready for AI. Accelerating research speed and expanding access means there is a lot more slop and there is also a lot more good work, but way too much for the current peer review system to delineate between. We need some accurate and decentralized way to aggregate this information. This makes a scientific prediction market, like Robin Hanson's idea futures, really appealing. The community can have markets for broad questions we care about, bystanders get paid for pointing out good research, and scientists are financially incentivized to make progress on the highest leverage issues. But with Hanson's conception, there is one big problem, which is how the market gets settled. For abstract scientific questions, like whether models are reward seeking or fitness seeking, we have no definitive event which can force market settlement. Hanson suggests that we use the convergence of scientific consensus, but this proposal takes trust in possibly misaligned opinions and requires the centralized coordination of scientists, which is hard to create---especially in pre-paradigmatic fields like AI safety. In this post, I'll lay out a theoretical framework for a new kind of scientific prediction market. TL;DR: Scientific questions become perpetuals where the fair price is the expected direction of future evidence. Here are the mechanics: • There are two kinds of markets, perpetuals on ideas and experiments that settle on their outcomes. • Anyone can create an experiment market. In the market, they are allowed to insider trade. That means, when their experiment is done, they can take risk free profit by trading the remaining liquidity in the market. HERE IS THE TRICK: when setting up a market, a scientist must commit to using their profits towards buying puts or calls on specific perpetual, dependent o