Let’s say that the election was decided by a coin flip, so there is no common cause between GDP and who is elected. Even in that case, I am still not sure why we’d think the prediction market would be useful for evaluating a counterfactual, such as “will GDP be higher under Biden or Trump?” The whole premise of prediction markets is that we evaluate them based on facts, not on counterfactuals. We might be able to use a forecaster’s ability to predict facts in order to decide how much to trust them when they make counterfactual claims. But I don’t see how we can evaluate a counterfactual claim via the prediction market mechanism directly. Can you elaborate on that?
Let’s say that the election was decided by a coin flip, so there is no common cause between GDP and who is elected. Even in that case, I am still not sure why we’d think the prediction market would be useful for evaluating a counterfactual, such as “will GDP be higher under Biden or Trump?” The whole premise of prediction markets is that we evaluate them based on facts, not on counterfactuals. We might be able to use a forecaster’s ability to predict facts in order to decide how much to trust them when they make counterfactual claims. But I don’t see how we can evaluate a counterfactual claim via the prediction market mechanism directly. Can you elaborate on that?