Consider the advantage prediction markets have over traditional news. If I want to keep track of some variable X, such as “the amount of investment going into Stargate”, and all I have are traditional news, I have to constantly manually sift through all related news reports data in search of related information. With prediction markets, however, I can just bookmark this page and check it periodically.
An issue with prediction markets is that they’re not well-organized. You have the tag system, but you don’t know which outcomes feed into other events, you don’t necessarily know what prompts specific market updates (unless someone mentions that in the comments), you don’t have a high-level outline of the ontology of a given domain, etc. Traditional news reports offer some of that, at least: if competently written and truthful, they offer causal models and narratives behind the events.
It would be nice if we could fuse the two. An interface for engaging with the news that combines conciseness of prediction-market updates with an attempt at a model-based understanding offered by traditional news.
One obvious idea is to arrange it into the form of a Bayes net. People (perhaps the site’s managers, perhaps anyone) could set up “causal models”, in which specific variables are downstream of other variables. Other people (forecasters/experts hired by the project’s managers, or anyone, like in prediction markets) could bet on which models are true[1], and within the models, on the values of specific variables[2]. (Relevant.)
Among other things, this would ensure built-in “consistency checks”. If, within a given model, a variable X is downstream of outcomes A, B, C, such that X only happens if all of A, B, C happen, but the market-estimated P(X) isn’t equal to P(ABC), this would suggest either that the prediction markets are screwing up, or that there’s something wrong with the given model.
Furthermore, one way for this to gain notoriety/mainstream appeal is if specific high-status people or institutions set up their own “official” causal models. For example, an official AI 2027 causal model, or an official MIRI model of AI doom which avoids the multlple-stage fallacy and clearly shows how it’s convergent.
Tons of ways this might not work out, but I think it’s an interesting idea to try. (Though maybe it’s something that should be lobbed off to Manifold Markets’ leadership.)
Hm. Galaxy-brained idea for how to use this as a springboard to make prediction markets go mainstream:
Convince friendly prominent alignment research institutions (e. g. MIRI, the AI Futures project) to submit their models to the platform.
Socially pressure AGI labs to submit their own official models there as well, e. g. starting from Anthropic. (This should be relatively low-cost for them; at least, inasmuch as they buy their own hype and safety assurances.)
Now you’ve got a bunch of high-profile organizations making implicit official endorsements of the platform.
Move beyond the domain of AI, similarly starting with friendly smaller organizations (EA orgs, etc.) then reaching out to bigger established ones.
Everyone in the world ends up prediction-market-pilled.
???
Civilizational sanity waterline rises!
(Note that it follows the standard advice for startup growth, where you start in a very niche market, gradually eat it all, then expand beyond this market, iterating until your reach is all-pervading.)
Another idea I’ve been thinking about:
Consider the advantage prediction markets have over traditional news. If I want to keep track of some variable X, such as “the amount of investment going into Stargate”, and all I have are traditional news, I have to constantly manually sift through all related news reports data in search of related information. With prediction markets, however, I can just bookmark this page and check it periodically.
An issue with prediction markets is that they’re not well-organized. You have the tag system, but you don’t know which outcomes feed into other events, you don’t necessarily know what prompts specific market updates (unless someone mentions that in the comments), you don’t have a high-level outline of the ontology of a given domain, etc. Traditional news reports offer some of that, at least: if competently written and truthful, they offer causal models and narratives behind the events.
It would be nice if we could fuse the two. An interface for engaging with the news that combines conciseness of prediction-market updates with an attempt at a model-based understanding offered by traditional news.
One obvious idea is to arrange it into the form of a Bayes net. People (perhaps the site’s managers, perhaps anyone) could set up “causal models”, in which specific variables are downstream of other variables. Other people (forecasters/experts hired by the project’s managers, or anyone, like in prediction markets) could bet on which models are true[1], and within the models, on the values of specific variables[2]. (Relevant.)
Among other things, this would ensure built-in “consistency checks”. If, within a given model, a variable X is downstream of outcomes A, B, C, such that X only happens if all of A, B, C happen, but the market-estimated P(X) isn’t equal to P(ABC), this would suggest either that the prediction markets are screwing up, or that there’s something wrong with the given model.
Furthermore, one way for this to gain notoriety/mainstream appeal is if specific high-status people or institutions set up their own “official” causal models. For example, an official AI 2027 causal model, or an official MIRI model of AI doom which avoids the multlple-stage fallacy and clearly shows how it’s convergent.
Tons of ways this might not work out, but I think it’s an interesting idea to try. (Though maybe it’s something that should be lobbed off to Manifold Markets’ leadership.)
Or, perhaps in an even more fine-grained manner, which links between different variables are true.
Ideally, with many variables shared between different models.
Maybe or maybe not related: Latent variables for prediction markets: motivation, technical guide, and design considerations
Yeah this one has been pretty high on my list (or, a fairly similar cluster of ideas)
Hm. Galaxy-brained idea for how to use this as a springboard to make prediction markets go mainstream:
Convince friendly prominent alignment research institutions (e. g. MIRI, the AI Futures project) to submit their models to the platform.
Socially pressure AGI labs to submit their own official models there as well, e. g. starting from Anthropic. (This should be relatively low-cost for them; at least, inasmuch as they buy their own hype and safety assurances.)
Now you’ve got a bunch of high-profile organizations making implicit official endorsements of the platform.
Move beyond the domain of AI, similarly starting with friendly smaller organizations (EA orgs, etc.) then reaching out to bigger established ones.
Everyone in the world ends up prediction-market-pilled.
???
Civilizational sanity waterline rises!
(Note that it follows the standard advice for startup growth, where you start in a very niche market, gradually eat it all, then expand beyond this market, iterating until your reach is all-pervading.)