Thank you for putting this spreadsheet database together! This seemed like a non-trivial amount of work, and it’s pretty useful to have it all in one place. Similar to other comments, seeing this spreadsheet really made me want more consistent questions and forecasting formats such that all these people can make comparable predictions, and also to see the breakdowns of how people are thinking about these forecasts (I’m very excited about people structuring their forecasts more into specific assumptions and evidence).
I thought the 2008 GCR questions were really interesting, and plotted the median estimates here. I was surprised by / interested in:
How many more deaths were expected from wars than other disaster scenarios
For superintelligent AI, most of the probability mass was < 1M deaths, but there was a high probability (5%) on extinction
A natural pandemic was seen as more likely to cause > 1M deaths than an engineered pandemic (although less likely to cause > 1B deaths)
(I can’t figure out how to make the image bigger, but you can click the link here to see the full snapshot)
Also, your comment suggests you already saw my comment quoting Beard et al. on breaking down the thought process behind forecasts. You might also be interested in Baum’s great paper replying to and building on Beard et al.’s paper. I also commented on Baum’s paper here, and Beard et al. replied to it here.
Yes, I share that desire for more comparability in predictions and more breakdowns of what’s informing one’s predictions. Though I’d also highlight that the predictions are often not even very clear in what they’re about, let alone very comparable or clear in what’s informing them. So clarity in what’s being predicted might be the issue I’d target first. (Or one could target multiple issues at once.)
And your comments on and graph of the 2008 GCR conference results are interesting. Are the units on the y axis percentage points? E.g., is that indicating something like a 2.5% chance superintelligent AI kills 1 or fewer people? I feel like I wouldn’t want to extrapolate that from the predictions the GCR researchers made (which didn’t include predictions for 1 or fewer deaths); I’d guess they’d put more probability mass on 0.
The probability of an interval is the area under the graph! Currently, it’s set to 0% that any of the disaster scenarios kill < 1 people. I agree this is probably incorrect, but I didn’t want to make any other assumptions about points they didn’t specify. Here’s a version that explicitly states that.
The probability of an interval is the area under the graph!
It’s not obvious to me how to interpret this without specifying the units on the y axis (percentage points?), and when the x axis is logarithmic and in units of numbers of deaths. E.g., for the probability of superintelligent AI killing between 1 and 10 people, should I multiply ~2.5 (height along x axis) by ~10 (length along y axis) and get 25%? But then I’ll often be multiplying the height along the x axis by more than 100 and getting insane probabilities?
So at the moment I can make sense of which events are seen as more likely than other ones, but not the absolute likelihood they’re assigned.
I may be making some basic mistake. Also feel free to point me to a pre-written guide to interpreting Elicit graphs.
Thank you for putting this spreadsheet database together! This seemed like a non-trivial amount of work, and it’s pretty useful to have it all in one place. Similar to other comments, seeing this spreadsheet really made me want more consistent questions and forecasting formats such that all these people can make comparable predictions, and also to see the breakdowns of how people are thinking about these forecasts (I’m very excited about people structuring their forecasts more into specific assumptions and evidence).
I thought the 2008 GCR questions were really interesting, and plotted the median estimates here. I was surprised by / interested in:
How many more deaths were expected from wars than other disaster scenarios
For superintelligent AI, most of the probability mass was < 1M deaths, but there was a high probability (5%) on extinction
A natural pandemic was seen as more likely to cause > 1M deaths than an engineered pandemic (although less likely to cause > 1B deaths)
(I can’t figure out how to make the image bigger, but you can click the link here to see the full snapshot)
Also, your comment suggests you already saw my comment quoting Beard et al. on breaking down the thought process behind forecasts. You might also be interested in Baum’s great paper replying to and building on Beard et al.’s paper. I also commented on Baum’s paper here, and Beard et al. replied to it here.
Oh this looks really interesting, I’ll check it out, thanks for linking!
Thanks for your comment!
Yes, I share that desire for more comparability in predictions and more breakdowns of what’s informing one’s predictions. Though I’d also highlight that the predictions are often not even very clear in what they’re about, let alone very comparable or clear in what’s informing them. So clarity in what’s being predicted might be the issue I’d target first. (Or one could target multiple issues at once.)
And your comments on and graph of the 2008 GCR conference results are interesting. Are the units on the y axis percentage points? E.g., is that indicating something like a 2.5% chance superintelligent AI kills 1 or fewer people? I feel like I wouldn’t want to extrapolate that from the predictions the GCR researchers made (which didn’t include predictions for 1 or fewer deaths); I’d guess they’d put more probability mass on 0.
The probability of an interval is the area under the graph! Currently, it’s set to 0% that any of the disaster scenarios kill < 1 people. I agree this is probably incorrect, but I didn’t want to make any other assumptions about points they didn’t specify. Here’s a version that explicitly states that.
It’s not obvious to me how to interpret this without specifying the units on the y axis (percentage points?), and when the x axis is logarithmic and in units of numbers of deaths. E.g., for the probability of superintelligent AI killing between 1 and 10 people, should I multiply ~2.5 (height along x axis) by ~10 (length along y axis) and get 25%? But then I’ll often be multiplying the height along the x axis by more than 100 and getting insane probabilities?
So at the moment I can make sense of which events are seen as more likely than other ones, but not the absolute likelihood they’re assigned.
I may be making some basic mistake. Also feel free to point me to a pre-written guide to interpreting Elicit graphs.