I may have missed this, but do these scoring rules prevent agents from trying to make the environment more un-predictable? In other words, if you’re competing against other predictors, it may make sense to influence the world to be more random and harder to understand.
I think this prediction market type issue has been discussed elsewhere but I can’t find a name for it.
Good question! These scoring rules do also prevent agents from trying to make the environment more unpredictable. In the same way that making the environment more predictable benefits all agents equally and so cancels out, making the environment less predictable hurts all agents equally and so cancels out in a zero-sum competition.
If the predictors can influence the world in addition to making a prediction, they would also have an incentive to change the world in ways that make their predictions more accurate than their opponents right? For example, if everyone else thinks Bob is going to win the presidency, one of the predictors can bribe Bob to drop out and then bet on Alice winning the presidency.
Is there work on this? To be fair, it seems like every AI safety proposal has to deal with something like this.
Yes, if predictors can influence the world in addition to making a prediction, they can go make their predictions more accurate. The nice thing about working with predictive models is that by default the only action they can take is making predictions.
AI safety via market making, which Evan linked in another comment, touches on the analogy where agents are making predictions but can also influence the outcome. You might be interested in reading through it.
This is super cool stuff, thank you for posting!
I may have missed this, but do these scoring rules prevent agents from trying to make the environment more un-predictable? In other words, if you’re competing against other predictors, it may make sense to influence the world to be more random and harder to understand.
I think this prediction market type issue has been discussed elsewhere but I can’t find a name for it.
Good question! These scoring rules do also prevent agents from trying to make the environment more unpredictable. In the same way that making the environment more predictable benefits all agents equally and so cancels out, making the environment less predictable hurts all agents equally and so cancels out in a zero-sum competition.
Oh that makes sense!
If the predictors can influence the world in addition to making a prediction, they would also have an incentive to change the world in ways that make their predictions more accurate than their opponents right? For example, if everyone else thinks Bob is going to win the presidency, one of the predictors can bribe Bob to drop out and then bet on Alice winning the presidency.
Is there work on this? To be fair, it seems like every AI safety proposal has to deal with something like this.
Yes, if predictors can influence the world in addition to making a prediction, they can go make their predictions more accurate. The nice thing about working with predictive models is that by default the only action they can take is making predictions.
AI safety via market making, which Evan linked in another comment, touches on the analogy where agents are making predictions but can also influence the outcome. You might be interested in reading through it.