(A response to this post.)

If you use prediction markets to make decisions, you might think they’ll generate EDT decisions: you’re asking for P(A|B), where you care about A, and B is something like “a decision … is taken”.

Okay, so say you want to use prediction markets to generate CDT decisions. You want to know P(A|do(B)).

There’s a very simple way to do that:

• Make a market on P(A|B).
• Commit to resolving the market to N/A^[1] with 99.9% chance.
• With 0.1% chance, transparently take a random decision among available decisions (the randomness is pre-set and independent of specific decisions/market data/etc.)

Now, 99.9% of the time, you can use freely use market data to make decisions, without impacting the market! You screened off everything upstream of the decision from the market. All you need for that is to make a random choice every now and then.

This really works to make CDT decisions! Try thinking through what the market would do in various decision-theoretic problems.

I claim that real markets actually do the exact same thing.

In the limit, you can imagine that when Tim Cook hires an awful designer, there’s no incentive for the markets to go down: because if they go down at all, Tim Cook would notice and fire them back.

But in reality, people and institutions are sometimes random! If there’s a chance Tim Cook doesn’t listen to the markets, the markets should down at least a tiny bit, to reflect that probability.

When Trump announced the tariffs, real markets showed only something like 10% of the reaction they would have if they thought the tariffs were to stay in place, because the markets correctly anticipated that the government would react to the market change.

But also, they went down, a lot, because they thought Trump might ignore them and not go back on the tariffs.

Even if you’re lower variance than Trump, to the extent you can be modeled well as mostly making a good choice but sometimes taking random decisions, the difference in conditional markets would already reflect at least some of the causal difference between different actions you can take.

And if that’s not enough, you can explicitly simulate the do() operator using the scheme above. It would require you to provide more liquidity when the market resolves, for traders to want to trade despite the overwhelming chance the market will resolve to N/A^[2]; yet, it enables you to make decisions based the markets estimating only their causal effects and not anything that might correlate.

(This is not new, related ideas have been proposed in 1, 2, and by many people in personal conversations. Trump and Apple examples were offered in a conversation with Nick Decker in a somewhat similar context.

(I want to mention I believe I know of two ways to structure prediction markets to generate FDT decisions, though I wasn’t able to come up with a single real-life situation where that could possibly be helpful and so it’s I consider it to be the realm of agent foundations- AIs might do something like that internally- and not futarchy.)

1. ^{^}

   Cancelling the market and reverting all transactions. This operation is not strictly necessary for running conditional prediction markets (instead of running a market on P(A|B) you could run a single market on all outcomes of A and B), but makes them a lot more straightforward.

2. ^{^}

   Imagine you want to spend $10k on subsidizing a market. (One way you can do that is to use Manifold-like AMM with a liquidity subsidy.)

   For people at a trading firm, it doesn't make sense to spend an hour of their time for 0.1% of eating even all of $10k.

   So you might want to provide $10m of subsidy that you get back if the market resolves to N/A: for the market participants, in expectation, this is $10k of subsidy that pays for information.

   Paying $10m with p=0.1% would naturally be a service costing $10.01k or something.

   So the cost of providing 1000x liquidity 1/1000 of the time is not noticeably higher than providing 1x of liquidity all the time.