I am a researcher at Rethink Priorities’ Worldview Investigations Team. I also do work for Oxford’s Global Priorities Institute. Previously I was a research analyst at the Forethought Foundation for Global Priorities Research. I took the role after completing the MPhil in Economics at Oxford University. Before that, I studied Mathematics and Philosophy at the University of St Andrews.
Find out more about me here.
Thank you very much Dan for your comments and for looking into the ins and outs of the work and highlighting various threads that could improve it.
There are two quite separate issues that you brought up here. First about infinite value, which can be recovered with new scenarios and, second, the specific parameter defaults used. The parameters the report used could be reasonable but also might seem over-optimistic or over-pessimistic, depending on your background views.
I totally agree that we should not anchor on any particular set of parameters, including the default ones. I think this is a good opportunity to emphasise one of the limitations in the concluding remarks saying that “we should be especially cautious about over-updating from specific quantitative conclusions”. As you hinted, one important reason for this is that the chosen parameters do not have enough data behind them and are not puzzles-free.
Some thoughts sparked by the comments in this thread:
You’re totally right to point out that the longer we survive in expectation the longer the simulation needs to be run for us to observe convergence.
I agree that risk is unlikely to be time-invariant for long eras, and I’m really excited about bringing in more realistic structures, like the one you suggest: an enriched Time of Perils with decaying risk. I’m hoping WIT or other interested researchers do more to spell out what these structures imply about the value of risk mitigation.
On the flip side of the default r_low seeming too high, if seen from the point of view of the start of a century, it’d imply a probability of surviving each century.
A tiny r_low might be more realistic, though I confess lacking strong intuitions either way about how risk will behave in the coming centuries, let alone millennia. In my mind, risk could decay or increase, and I do hope the patterns so far, for example these last 500 years, are nothing to go by.
Your point about conditional probabilities is a good way to introduce and think about thought experiments on risk profiles. It made me think that a civilisation like the one you describe surviving different hurdles could be modelled under Great Filters where you indeed use an r_low orders of magnitude smaller than the current default and you’d get something that fits the picture you’d suggest much better, even without introducing any modifications like the decaying risk. Let me know if you play around with the code to visualise this.
Thank you very much Roman!
I used blender, modelled the 3D spheres, rendered it and photoshop for the text.
Discrete-time was inherited from the previous framework (OAT). It can be simpler, but continuous is sometimes more tractable and better suited for models emphasising other features. For example, when modelling economic growth directly, or when thinking about utility, or when we want to express a hazard rate that is micro-founded on some risk mechanism, those models would generally be better expressed in continuous time. This recent paper is a good example of the typical setups economics papers use in continuous time.
Thank you very much for your words Vasco! And thank you for catching those formatting typos, I’ve corrected them now.
In order:
Two underscores seemed to have got lost in translation to markdown! Should be there now.
You’re right to point out that, in this context, but it isn’t exactly . I was using that approximation for the exposition but should have made that clearer, especially in the code. I’ve made minor corrections to reflect this.
I’ll also improve the phrasing to make the sentence you mentioned on clearer.
Thanks again!
Thank you very much Felix! Have added the podcast 📡 and yes, @saulius ’s list and feedback have been really helpful!
Thank you very much!
Hello! A quick question about deadlines.
Context: I’m considering applying for PhD funding (Economics PhD on EA-aligned research).
The EA fund’s official website says: “The Long-Term Future Fund will respond with a funding decision within six weeks, and typically in just three weeks. If you need to hear back sooner (e.g., within just a few days), you can let us know in the application form, and we will see what we can do.” (same for IF)
Whereas the EA forum apply now post says: “The upcoming application deadlines and funding decision dates are: 7 Mar 2021, decision by 2 Apr 2021 13 Jun 2021, decision by 9 Jul 2021 3 Oct 2021, decision by 29 Oct 2021 6 Feb 2022, decision by 4 Mar 2022 5 Jun 2022, decision by 1 Jul 2022 2 Oct 2022, decision by 28 Oct 2022″ (and the deadlines seem to apply to all three funds not just animal welfare)
The answer seems important in deciding whether I should send my application in by tomorrow.
Thank you for all the comments JWS, I found your excitement contagious.
Some thoughts on your thoughts:
I couldn’t agree more that there’d be a lot of value from laying out parameter configurations. We have some more work coming out as part of this sequence that aims to help fill this gap!
I think it’d be great to see some survey data on what the commonly assumed risk patterns and valued trajectories are in the EA community. I’ve made a push from my little corner to hopefully get some data on common views. Whichever they are, you’re right to point out the immense differences in what could they imply.
I’m really happy that you found the notebook useful. I’ll make sure to update the GitHub with any new features and code discussions.