This applies to roughly the entire post, but I see an awful lot of magical thinking in this space.
Could you point to some specific areas of magical thinking in the post? and/or in the space?[1] (I’m not claiming that there aren’t any, I definitely think there are. I’m interested to know where I & the space are being overconfident/thinking magically, so that I/it can do less magical thinking.)
What is the actual mechanism by which you think prediction markets will solve these problems?
The mechanism that Manifold Love uses. In section 2, I put it as “run a bunch of conditional prediction markets on a bunch of key benchmarks for potential pairs between two sides that are normally caught in adverse selection.” I wrote this post to explain the actual mechanism by which I think (conditional) prediction markets mightsolve these problems, but I also want to note that I definitely do not think that (conditional) prediction markets will definitely for sure 100% totally completely solve these problems. I just think they have potential, and I’m excited to see people giving it a shot.
In order to get a good prediction from a market you need traders to put prices in the right places. This means you need to subsidise[2] the markets.
I agree! In order to get a good prediction from a market, you (probably, see the footnote) need participation to be positive-sum.[3] I think there are a few ways to get this:
Direct subsidies
Since prediction markets create valuable price information, it might make sense to have those who benefit from the price information directly pay. I could imagine this pretty clearly, actually: Manifold Love could charge users for (e.g.) more than 5 matches, and some part of the fee that the user pays goes toward market subsidies. As you pointed out, paying a 3rd party is currently the case for most of my examples — matchmakers, headhunters, real estate agents, etc — so it seems like this sort of thing aligns with the norms & users’ expectations.
Hedging
Some participants bet to hedge other off-market risks. These participants are likely to lose money on prediction markets, and know that ahead of time. That’s because they’re not betting their beliefs; they’re paying the equivalent to an insurance premium.
For prediction markets generally, this seems like the most viable path to getting money flowing into the market. I’m not sure how well it’d work for this sort of setup, though — mainly because the markets are so personal.
This requires finding markets on which participants would want to hedge, which seems like a difficult problem. I give an example before, but I’m pretty unsure what something like this would look like in a lot of the examples I listed in the original essay.
Continuing the example of the labor market from section 2: I could imagine (e.g.) a Google employee buying an ETF-type-thing that bets NO on whether all potential Google employees will remain at Google a year from their hiring date. This protects that Google empoyee against the risk of some huge round of layoffs — they’ve bought “insurance” against that outcome. In doing so, it provides the markets a way to become positive-sum for those participants who’re betting their beliefs.
New traders
This provides an inflow of money, but is (obviously) tied to new traders joining the market. I don’t like this at all, because it’s totally unsustainable and leads to community dynamics like those in crypto. Also, it’s a scheme that’s pyramid-shaped, a “pyramid scheme” if you will.
I’m mainly including this for completeness; I think relying on this is a terrible idea.
Whether or not a subsidised prediction market is going to be cheaper for the equivalent level of forecast than paying another 3rd party (as is currently the case in most of your examples) is very unclear to me
Agreed! It’s unclear to me too. This sort of question is answerable by trying the thing and seeing if it works — that’s why I’m excited about for people & companies to try it out and see if it works.
I’m comfortable working under this assumption for now, but I do want to be clear that I’m not fully convinced this is the case. The stock market is clearly negative-sum for the majority of traders, and yet… traders still join. It seems at least plausible that, as long as the market is positive-sum for some key market participants, the markets still can still provide valuable price information.
This post triggered me a bit, so I ended up writing one of my own.
I agree the entire thing is about how to subsidise the markets, but I think you’re overestimating how good markets are as a mechanism for subsidising forecasting (in general). Specifically for your examples:
Direct subsidies are expensive relative to the alternatives (the point of my post)
Hedging doesn’t apply in lots of markets, and in the ones where it does make sense those markets already exist. (Eg insurance)
New traders is a terrible idea as you say. It will work in some niches (eg where there’s lots of organic interest, but it wont work at scale for important things)
Thanks for the response!
Could you point to some specific areas of magical thinking in the post? and/or in the space?[1] (I’m not claiming that there aren’t any, I definitely think there are. I’m interested to know where I & the space are being overconfident/thinking magically, so that I/it can do less magical thinking.)
The mechanism that Manifold Love uses. In section 2, I put it as “run a bunch of conditional prediction markets on a bunch of key benchmarks for potential pairs between two sides that are normally caught in adverse selection.” I wrote this post to explain the actual mechanism by which I think (conditional) prediction markets might solve these problems, but I also want to note that I definitely do not think that (conditional) prediction markets will definitely for sure 100% totally completely solve these problems. I just think they have potential, and I’m excited to see people giving it a shot.
I agree! In order to get a good prediction from a market, you (probably, see the footnote) need participation to be positive-sum.[3] I think there are a few ways to get this:
Direct subsidies
Since prediction markets create valuable price information, it might make sense to have those who benefit from the price information directly pay. I could imagine this pretty clearly, actually: Manifold Love could charge users for (e.g.) more than 5 matches, and some part of the fee that the user pays goes toward market subsidies. As you pointed out, paying a 3rd party is currently the case for most of my examples — matchmakers, headhunters, real estate agents, etc — so it seems like this sort of thing aligns with the norms & users’ expectations.
Hedging
Some participants bet to hedge other off-market risks. These participants are likely to lose money on prediction markets, and know that ahead of time. That’s because they’re not betting their beliefs; they’re paying the equivalent to an insurance premium.
For prediction markets generally, this seems like the most viable path to getting money flowing into the market. I’m not sure how well it’d work for this sort of setup, though — mainly because the markets are so personal.
This requires finding markets on which participants would want to hedge, which seems like a difficult problem. I give an example before, but I’m pretty unsure what something like this would look like in a lot of the examples I listed in the original essay.
Continuing the example of the labor market from section 2: I could imagine (e.g.) a Google employee buying an ETF-type-thing that bets NO on whether all potential Google employees will remain at Google a year from their hiring date. This protects that Google empoyee against the risk of some huge round of layoffs — they’ve bought “insurance” against that outcome. In doing so, it provides the markets a way to become positive-sum for those participants who’re betting their beliefs.
New traders
This provides an inflow of money, but is (obviously) tied to new traders joining the market. I don’t like this at all, because it’s totally unsustainable and leads to community dynamics like those in crypto. Also, it’s a scheme that’s pyramid-shaped, a “pyramid scheme” if you will.
I’m mainly including this for completeness; I think relying on this is a terrible idea.
Agreed! It’s unclear to me too. This sort of question is answerable by trying the thing and seeing if it works — that’s why I’m excited about for people & companies to try it out and see if it works.
I’m assuming you mean the “prediction market/forecasting space,” so please let me know if that’s not the space to which you’re referring.
I’ll interpret “subsidize” more broadly as “money flowing into the market to make it positive-sum after fees, inflation, etc.”
I’m comfortable working under this assumption for now, but I do want to be clear that I’m not fully convinced this is the case. The stock market is clearly negative-sum for the majority of traders, and yet… traders still join. It seems at least plausible that, as long as the market is positive-sum for some key market participants, the markets still can still provide valuable price information.
This post triggered me a bit, so I ended up writing one of my own.
I agree the entire thing is about how to subsidise the markets, but I think you’re overestimating how good markets are as a mechanism for subsidising forecasting (in general). Specifically for your examples:
Direct subsidies are expensive relative to the alternatives (the point of my post)
Hedging doesn’t apply in lots of markets, and in the ones where it does make sense those markets already exist. (Eg insurance)
New traders is a terrible idea as you say. It will work in some niches (eg where there’s lots of organic interest, but it wont work at scale for important things)