Any update to the market is (equivalent to) updating on some kind of information. So all you can do is dynamically choose what to do or do not update on.* Unfortunately, whenever you choose not to update on something, you are giving up on the asymptotic learning guarantees of policy market setups. So the strategic gains from updatelesness (like not falling into traps) are in a fundamental sense irreconcilable with the learning gains from updatefulness. That doesn’t prevent that you can be pretty smart about deciding what to update on exactly… but due to embededness problems and the complexity of the world, it seems to be the norm (rather than the exception) that you cannot be sure a priori of what to update on (you just have to make some arbitrary choices).
*For avoidance of doubt, what matters for whether you have updated on X is not “whether you have heard about X”, but rather “whether you let X factor into your decisions”. Or at least, this is the case for a sophisticated enough external observer (assessing whether you’ve updated on X), not necessarily all observers.
I think the first question to think about is how to use them to make CDT decisions. You can create a market about a causal effect if you have control over the decision and you can randomise it to break any correlations with the rest of the world, assuming the fact that you’re going to randomise it doesn’t otherwise affect the outcome (or bettors don’t think it will).
Committing to doing that does render the market useless for choosing policy, but you could randomly decide whether to randomise or to make the decision via whatever the process you actually want to use, and have the market be conditional on the former. You probably don’t want to be randomising your policy decisions too often, but if liquidity wasn’t an issue you could set the probability of randomisation arbitrarily low.
Is there a way to use policy markets to make FDT decisions instead of EDT decisions?
Worked on this with Demski. Video, report.
Any update to the market is (equivalent to) updating on some kind of information. So all you can do is dynamically choose what to do or do not update on.* Unfortunately, whenever you choose not to update on something, you are giving up on the asymptotic learning guarantees of policy market setups. So the strategic gains from updatelesness (like not falling into traps) are in a fundamental sense irreconcilable with the learning gains from updatefulness. That doesn’t prevent that you can be pretty smart about deciding what to update on exactly… but due to embededness problems and the complexity of the world, it seems to be the norm (rather than the exception) that you cannot be sure a priori of what to update on (you just have to make some arbitrary choices).
*For avoidance of doubt, what matters for whether you have updated on X is not “whether you have heard about X”, but rather “whether you let X factor into your decisions”. Or at least, this is the case for a sophisticated enough external observer (assessing whether you’ve updated on X), not necessarily all observers.
I think the first question to think about is how to use them to make CDT decisions. You can create a market about a causal effect if you have control over the decision and you can randomise it to break any correlations with the rest of the world, assuming the fact that you’re going to randomise it doesn’t otherwise affect the outcome (or bettors don’t think it will).
Committing to doing that does render the market useless for choosing policy, but you could randomly decide whether to randomise or to make the decision via whatever the process you actually want to use, and have the market be conditional on the former. You probably don’t want to be randomising your policy decisions too often, but if liquidity wasn’t an issue you could set the probability of randomisation arbitrarily low.
Then FDT… I dunno, seems hard.
Yep!
“If I randomize the pick, and pick A, will I be happy about the result?” “If I randomize the pick, and pick B, will I be happy about the result?”
Randomizing 1% of the time and adding a large liquidity subsidy works to produce CDT.
I agree with all of this! A related shortform here.
An interesting development in the time since your shortform was written is that we can now try these ideas out without too much effort via Manifold.
Anyone know of any examples?