There is (generally) a disconnect between decision makers and forecasting platforms
Spot forecasts are not especially useful on their own
There are some good examples of decision makers at least looking at markets
Re 1: the disconnect between decision makers and forecasting platforms. I think the problem comes in two directions.
Decision makers don’t value the forecasts as much as they would cost to create (even if the value they would provide would be huge)
The incentives to make the forecasts are usually orthogonal to the people using them. (Prediction markets seem to most naturally arise from investing and gambling) which isn’t necessarily a strong enough incentive in and of itself. My understanding of the gambling industry is that most people are interested in short-term, volatile markets. (The boom in in-play sport vs pre-match odds; or Polymarket’s success in markets which are generally <1 month). Investing is a little different (and as I’ll say latter) I think people do take those forecasts fairly seriously.
Re 2: someone saying “X has a y% chance of happening” is not (usually) especially valuable to a decision maker. (Especially since the market is already accounting for what it expects the decision maker to do). Models (even fairly poor ones) often have more use to a decision maker, since they can see how their decision might affect the outcome. [Yes, there are ideas like counterfactual markets, but none of those ideas can really capture the full space of possibilities and will also just fragment liquidity]. The best you can really do is extract a model statistically (when indicator goes up, forecast goes down, so indicator might be saying something about event).
Re 3: It would take a while for me to summarise the evidence here, but I think there’s a pretty strong case that central banks (eg the Federal Reserve in the US) are increasingly looking at market indicators when setting monetary policy. I think CEOs and other decision makers in business look at market prices as indicators when deciding direction of their companes. (Although it’s hard to fully describe this as a prediction market as much as “looking at the competition” I think with some time I could articulate what I mean)
My general take on this space is:
There is (generally) a disconnect between decision makers and forecasting platforms
Spot forecasts are not especially useful on their own
There are some good examples of decision makers at least looking at markets
Re 1: the disconnect between decision makers and forecasting platforms. I think the problem comes in two directions.
Decision makers don’t value the forecasts as much as they would cost to create (even if the value they would provide would be huge)
The incentives to make the forecasts are usually orthogonal to the people using them. (Prediction markets seem to most naturally arise from investing and gambling) which isn’t necessarily a strong enough incentive in and of itself. My understanding of the gambling industry is that most people are interested in short-term, volatile markets. (The boom in in-play sport vs pre-match odds; or Polymarket’s success in markets which are generally <1 month). Investing is a little different (and as I’ll say latter) I think people do take those forecasts fairly seriously.
Re 2: someone saying “X has a y% chance of happening” is not (usually) especially valuable to a decision maker. (Especially since the market is already accounting for what it expects the decision maker to do). Models (even fairly poor ones) often have more use to a decision maker, since they can see how their decision might affect the outcome. [Yes, there are ideas like counterfactual markets, but none of those ideas can really capture the full space of possibilities and will also just fragment liquidity]. The best you can really do is extract a model statistically (when indicator goes up, forecast goes down, so indicator might be saying something about event).
Re 3: It would take a while for me to summarise the evidence here, but I think there’s a pretty strong case that central banks (eg the Federal Reserve in the US) are increasingly looking at market indicators when setting monetary policy. I think CEOs and other decision makers in business look at market prices as indicators when deciding direction of their companes. (Although it’s hard to fully describe this as a prediction market as much as “looking at the competition” I think with some time I could articulate what I mean)