Melissa Bedinger

55Joined May 2021

Bio

I am a researcher at the University of Edinburgh. My research relates to climate change adaptation, human factors, and sustainable development - but I love learning about new areas.

I'm a longtime Nerdfighter and found effective altruism in early 2021. I'm especially interested in longtermism and x-risks.

I'm still relatively new to EA and wayfinding as to how I can have the most impact. If you have any ideas, suggestions, or feedback on what I should be working on (topics, skills, anything), let's chat! Or send them anonymously here.

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First - thank you for this. I currently research some aspects of resilience & adaptation and am asking myself some critical questions in this area. It also gave me something to build on and respond to, as a nudge to participate for the first time in the Forum, even if my thinking on this is underdeveloped!

On the post itself - I think the biggest contribution here is zooming out in the ending notes, to potential areas for EAs in a climate/longtermism space. What I took away was:

  • indirect x-risk from climate change is potentially important and neglected
  • as a group, some EAs interested in climate x longtermism should "pursue research ranking various climate interventions from a climate x-risk perspective" - I may have broadened this out to something like "more holistically assess how climate mitigation and adaptation solutions could indirectly impact total x-risk"

I have a lot of scattered further thoughts on this, but they're underdeveloped, I'm very uncertain about them, and it's likely I am missing some key EA literature/thinking already done on this. The central themes are that:

  • ranking climate problems/interventions for their indirect impacts on total x-risk is less tractable than addressing "direct" x-risk because it would require dealing with complexity i.e. a lot of feedback loops over time (and potentially space)
  • to my knowledge we (EA, but also humanity) don't have many formal tools to deal with complexity/feedback loops/emergence, especially not at a global scale with so many different types of flows
  • there seem to be a lot of skills/attitudes/expertise in the EA community that would make us (as a group) particularly good at developing methodologies to deal with ambiguity/complex problems;
  • some time could be spent to scope what we can reasonably incorporate into a methodology that could deal with that complexity. The result might be that we decide any methodology aimed at this would require too much effort to do in practice, for the added information it gains (if it gains any), and so we decide dealing with "direct" x-risk only is still the best strategy with updated confidence. The result might also be that we come up with an extra verification that we aren't missing something substantial when considering only direct risks - this could be as resource-intensive as detailed multi-modelling, or something 'simpler' like taking the GCR classification in Table 1 here and describing a set of timelines that test what happens when they interact with each other at a high level

In short I really appreciated the direction of your post! However I was less confident in how you got to those specific scenarios. I think progress in this area could include some standardised approach to generating them, and I think this might be important to establish before we're able to confidently rank problems/solutions for indirect x-risk. Again, it's likely I'm missing key EA thinking/literature on this and I would love for anyone to make recommendations/corrections.

Hi everyone,

I'm an academic researcher, and came across EA this year while reflecting on possible future research areas & career paths. I'm really enjoying combing through all these posts!

While exploring EA survey results (e.g. here) and similar community posts (e.g. here) I noticed contributors made solid attempts at transparency, but some links to source data or code are now invalid.

In the interests of EA transparency and posterity, it seems like there should be a single 'go-to' place to find and explore all the EA data (& code where relevant). I checked the Data (EA Community) tag but didn't find a guide. Perhaps I haven't gone deep enough yet, or perhaps there are GDPR barriers to this.

Anyone know if this - e.g. some master list of GitHub repositories or doi's - exists?

Please correct my newb mistake if this is not the right place to ask! Taking a shot here as I'm not yet confident enough with the rules to create a dedicated post.

Just because I'm excited this is being discussed here...this Kate Aronoff article might be good further reading.

Hi! I'm new and excited to be here. :)