Solving Two-Sided Adverse Selection with Prediction Market Matchmaking

Awesome post!

I have absolutely no experience with prediction markets, but I’m instinctively nervous about incentives here. Maybe the real-world incentives of market participants could be greater than the play-money incentives? For example, if you’re trying to select people to represent your country at an international competition and the potential competitors have invested their lives into being on that international team and those potential competitors can sign up as market participants (maybe anonymously), then I could very easily imagine those people sabotaging their competitors’ markets and boosting their own with no regard for their post-selection in-market prediction winnings.

For personal stuff (friendship / dating), I have some additional concerns. Suppose person A is open for dating and person B really wants to date them. By punting the decision-making process to the public, person A restricts themselves to working with publicly available information about person B and simultaneously person B is put under pressure to publicly reveal all relevant information. I can imagine a lot of Goodharting on the part of person B. Also, if it was revealed that person C bet against A and B dating and person B found out, I can imagine some … uh … lasting negative emotions between B and C. That possibility could also mess with person C’s incentives. In other words, the market participants with the closest knowledge of A and B also have the most to lose by A and B being upset with their bets and thus face the most misaligned incentives. (Note: I also dislike dating apps and lots of people use those so I’m probably biased here.)

Finally, I can imagine circumstances where publicly revealing probabilities of things can cause negative externalities, especially on mental health. For example, colleges often don’t reveal students’ exact percentage scores on classes even if employers would be interested — the amount of stress that would induce on the student body could result in worse learning outcomes overall. In an example you listed, with therapists/patients, I feel like it might not be great to have someone suffering from anxiety watch their percentage chance of getting an appointment go up and down.

But for circumstances with low stakes (so play money incentives beat real-world incentives) and small negative externalities, such as gym partners, I could imagine this kind of system working really well! Super cool!

[-]Saul Munn2y20

Thanks for the response!

Re: concerns about bad incentives, I agree that you can depict the losses associated with manipulating conditional prediction markets as paying a "cost" — even though you'll probably lose a boatload of money, it might be worth it to lose a boatload of money to manipulate the markets. In the words of Scott Alexander, though:

If you’re wondering why people aren’t going to get an advantage in the economy by committing horrible crimes, the answer is probably the same combination of laws, ethics, and reputational concerns that works everywhere else.

I'm concerned about this, but it feels like a solvable problem.

Re: personal stuff & the negative externalities of publicly revealing probabilities, thanks for pointing these out. I hadn't thought of it. Added it to the post!

[-]lunatic_at_large2y10

So I've been thinking a little more about the real-world-incentives problem, and I still suspect that there are situations in which rules won't solve this. Suppose there's a prediction market question with a real-world outcome tied to the resulting market probability (i.e. a relevant actor says "I will do XYZ if the prediction market says ABC"). Let's say the prediction market participants' objective functions are of the form play_money_reward + real_world_outcome_reward. If there are just a couple people for whom real_world_outcome_reward is at least as significant as play_money_reward and if you can reliably identify those people (i.e. if you can reliably identify the people with a meaningful conflict of interest), then you can create rules preventing those people from betting on the prediction market.

However, I think that there are some questions where the number of people with real-world incentives is large and/or it's difficult to identify those people with rules. For example, suppose a sports team is trying to determine whether to hire a star player and they create a prediction market for whether the star athlete will achieve X performance if hired. There could be millions of fans of that athlete all over the world who would be willing to waste a little prediction market money to see that player get hired. It's difficult to predict who those people are without massive privacy violations -- in particular, they have no publicly verifiable connection to the entities named in the prediction market.

[-]SimonM2y31

I'm excited about the potential of conditional prediction markets to improve on them and solve two-sided adverse selection.

This applies to roughly the entire post, but I see an awful lot of magical thinking in this space. What is the actual mechanism by which you think prediction markets will solve these problems?

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 the markets. 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

[-]Saul Munn2y10

Thanks for the response!

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 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.

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 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.

[-]SimonM2y*30

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)

[-]Charlie Steiner2y20

Cool idea. I think in most cases on the list, you'll have some combination of information asymmetry and an illiquid market that make this not that useful.

Take used car sales. I put my car up for sale and three prospective buyers come to check it out. We are now basically the only four people in the world with inside knowledge about the car. If the car is an especially good deal, each buyer wants to get that information for themselves but not broadcast it either to me or to the other buyers. I dunno man, it all seems like a stretch to say that the four of us are going to find a prediction market worth it,.

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Solving Two-Sided Adverse Selection with Prediction Market Matchmaking

16

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0: Navigation

1: Love

2: Generalizing

3: Insert Two-Sided Adverse Selection Here

4: Caveats