This suggests that binary questions more easily attract forecasts, which was my intuition already, and seems relevant to future efforts to write questions—if they can be turned into binary questions without too much loss of value, this might be preferable for getting more attention from forecasters.
Do you have a sense of why this is the case? Is it typically easier/faster to make binary than continuous forecasts? Are there any other mechanisms?
Do you have a sense of how strong that effect might tend to be? Like whether it can typically be expected to increase the number of forecasters by 50%, 100%, 200%, etc. relative to how many would’ve forecasted on the equivalent continuous questions?
It is definitely easier—the answer is more one dimensional, and for continuous questions there’s a lot more going back and forth between the cumulative distribution function and the probability density function, and thinking about corner cases.
E.g. For “When will the next supreme court vacancy arise” vs “will there be a vacancy by [year]”, in the former case you have to think about when a decision to retire might be timed, in the latter you just need to think about whether the judge will do it.
Other mechanisms—it’s possible the average binary question is more interesting or attention grabbing.
As for your second question, I looked at all the questions from 2019 and 2020 just now, and the median number of unique predictors on a binary question was 75, vs 38 for a continuous one. The mean was 97 vs 46. But this does not control for the questions being different. There were 942 continuous questions over the time window and 727 binary questions.
Do you have a sense of why this is the case? Is it typically easier/faster to make binary than continuous forecasts? Are there any other mechanisms?
Do you have a sense of how strong that effect might tend to be? Like whether it can typically be expected to increase the number of forecasters by 50%, 100%, 200%, etc. relative to how many would’ve forecasted on the equivalent continuous questions?
It is definitely easier—the answer is more one dimensional, and for continuous questions there’s a lot more going back and forth between the cumulative distribution function and the probability density function, and thinking about corner cases.
E.g. For “When will the next supreme court vacancy arise” vs “will there be a vacancy by [year]”, in the former case you have to think about when a decision to retire might be timed, in the latter you just need to think about whether the judge will do it.
Other mechanisms—it’s possible the average binary question is more interesting or attention grabbing.
As for your second question, I looked at all the questions from 2019 and 2020 just now, and the median number of unique predictors on a binary question was 75, vs 38 for a continuous one. The mean was 97 vs 46. But this does not control for the questions being different. There were 942 continuous questions over the time window and 727 binary questions.