I think people working on animal welfare have more incentives to post during debate week than people working on global health.
The animal space feels (when you are in it) very funding constrained, especially compared to working in the global health and development space (and I expect gets a higher % of funding from EA / EA-adjacent sources). So along comes debate week and all the animal folk are very motivated to post and make their case and hopefully shift a few $. This could somewhat bias the balance of the debate. (Of course the fact that one side of the debate feels they needs funding so much more is in itself relevant to the debate.)
What are the key leverage points to get these companies to listen to campaigners such as yourself? How does this differ from the animal right space and how will this affect your strategy? What do you have in terms of strategy documents or theory of change?
Some thoughts on my mind are:
To the best of my understanding the animal rights corporate campaigning space is unable to exert much or any influence on B2B (business to business) companies. Animal campaigns only appear to have influenced B2C (business to consumer) companies. An autonomous coding agent feels more B2B and by analogy having any influence here could be extremely difficult. That said I don't think this should be a huge problem as...
The leverage points for influencing companies in the AI space is very different to the animal space. In particular AI companies are probably much more concerned about losing employees to other companies than food companies. I expect they are also likely concerned about regulation that could restrict their actions. I expect there much less concerned about public image. As such..
This does suggest to somewhat different approach to corporate campaign. Potentially targeting employees more (although probably not picking on individuals) and greater focus on presenting the targeted company negatively to regulators/policymakers or to investors, more than to the public.
This is just quick thoughts and I might be wrong about much of this. I just wanted to flag as your post seemed to suggest that this work would be similar to work in the animal space and in many ways it is but I think there's a risk of not seeing the differences. I wish you all the best of luck with your campaigning.
Hi, Thank you. All good points. Fully agree with ongoing iterative improvement to our CEAs and hopefully you will see such improvements happening over the various research rounds (see also my reply to Nick). I also agree with picking up on specific cases where this might be a bigger issue (see my reply to Larks). I don’t think it is fair to say that we treat those two numbers as zero but it is fair to say we are currently using a fairly crude approximation to get at what those numbers are getting it in our lives saved calculations.
For a source on discounting see here: https://rethinkpriorities.org/publications/a-review-of-givewells-discount-rate#we-recommend-that-givewell-continue-discounting-health-at-a-lower-rate-than-consumption-but-we-are-uncertain-about-the-precise-discount-rate
"Discounting consumption vs. health benefits | Discount health benefits using only the temporal uncertainty component"
Hi Nick, Thank you very much for the comment. These are all good points.
I fully agree with you and Larks that where a specific intervention will have reduced impact due to long run health effects this should be included in our models and I will check this is happening.
I apologise for the defensiveness and made a few minor edits to the post trying to keep content the same.
That's not a reason not to continuously be improving models.
To be clear, we are always always improving our CEA models. This is an ongoing iterative process, and my hope is they get better year upon year. However, I guess I don't have confidence right now that a -10% change to this number is actually improving the model or affecting our decision making.
If we dive into these numbers just a bit, I immediately notice that the discount rate in the GBD data is higher than ours and that should suggest that, if we are adjusting these numbers, that probably we want a significant +increase not decrease. But that then raises the question of what discount rate we are using and why, which has a huge effect on some of the models – and this is something there are currently internal debates in the team about, and we are looking at changing. But this then raises a question about how to represent the uncertainty about these numbers in our models and ensure the decision makers and readers are more aware of the inherent estimations that can have big effect on CEA outputs – and improving this is probably towards the top of my list.
Thank you Larks. This is a very good point and I fully agree.
In any cases where this happens it should be incorporated into our current model. That said I will check this for our current research and make sure that in any such cases (such as say pulmonary rehabilitation for COPD where patients are expected to have a lower quality of life if they survive) this is accounted for.