Hi Kyle, 

This is a very interesting post! One quick and very small technical detail: Rethink Priorities' welfare ranges aren't capped at 1 for non-human animals. (It just happens that, when we adjusted for probability of sentience, they all happened to have 50th percentile estimates that fall below 1). They're instead a reflection of the difference between the best and worst states that the non-human animal can experience relative to the difference between the best and worst states that a human can experience (which is normalized to 1). In theory, this relative difference could be greater than 1 if the range in intensity of experiences that a non-human animal can experience is wider than that of humans. 

In fact, one of our welfare range models (the undiluted experiences mode) that feeds into the aggregate estimates tends to produce sentience-adjusted welfare range estimates greater than 1 under the theory that less cognitively complex organisms may not be able to dampen negative experiences by contextualizing them. As such, a few animals have 95th percentile estimates for their welfare ranges that are above 1 (octopuses, pigs, and shrimp). Here are some more details about the models and distributions: https://docs.google.com/document/d/1xUvMKRkEOJQcc6V7VJqcLLGAJ2SsdZno0jTIUb61D8k/edit?usp=sharing As well as the spreadsheet of results from all models: https://docs.google.com/spreadsheets/d/1SpbrcfmBoC50PTxlizF5HzBIq4p-17m3JduYXZCH2Og/edit?usp=sharing 

Again, this is a really thought-provoking and sobering post, thanks for writing it :)

Oh I see! Thanks for the clarification!

This is a really interesting project and way of approaching the topic!

One thing to note: welfare ranges don’t factor in the lifespans of animals, so we’d also need to factor in the typical time a farmed animal lives and then weight by welfare range to get a moral weight-adjusted sense of per calorie animal impacts.

But again, approaching this from a per calorie perspective is really interesting!

Hi Henry! While the 90% confidence intervals for the RP welfare ranges are indeed wide, this is because they’re coming from a mixture of several theories/models of welfare. The uncertainty within a given theory/model of welfare is much lower, and you might have more or less credence in any individual model.

Additionally, if we exclude the neuron count model, the welfare ranges from the mixture of all the other models have narrower distributions.

Here’s a document that explains the different theories/models used: https://docs.google.com/document/d/1xUvMKRkEOJQcc6V7VJqcLLGAJ2SsdZno0jTIUb61D8k/edit

And here’s a spreadsheet with all the confidence intervals from each theory/model individually (after adjusting for probability of sentience): https://docs.google.com/spreadsheets/d/1SpbrcfmBoC50PTxlizF5HzBIq4p-17m3JduYXZCH2Og/edit

Thanks so much, Jakub! 

Fascinating, I hadn't thought about that with respect to Congress. One thing I wonder about with ag-gag laws is whether they run afoul of the First Amendment. Do you know if there's a strong legal case to be made that they're unconstitutional? 

My gut instinct here would be that it's probably somewhat harder to pass Congressional legislation that both is constitutional and effectively limits corporate campaigns (because it's private entities choosing what kinds of products to sell). Am I wrong here? (I am really interested in this topic, so I would love to be corrected)

One consideration that Peter Wildeford made me think of is that, with the initiatives that do fall under Congress’ Interstate Commerce Clause authority, we might expect the longevity to be reduced. For example, if every five years a Congressperson puts into the Farm Bill a proposal to ban states from having Prop 12-style regulations, there’s some chance this passes eventually.

Does your research include any initiatives that do fall under Congressional authority?

Hi Vasco, thanks for the comment! I really appreciate it when people dig into the modelling choices :)

EDIT: I just saw the end of your comment. I'm not aware of any research into the intensity of pain across types, and would be keen to hear from others who are.

I think your ordering (r1 < r2 < r3 and r4 >> 500x annoying) would be totally reasonable, and I haven't read those posts, so thanks for bringing them up! The choice to use the ratios previously used by Šimčikas was rather arbitrary and meant to be consistent with his results. I get why one might expect excruciating pain to be much worse than 500x annoying pain, and I think we do need more research on this to be able to better aggregate the duration and intensity of pain. 

These are the reasons why I allow users to input their own pain weights in the model, so I definitely encourage you and others to try out using alternative weights! (here) (When you enter the weights, 1 is the benchmark for "equivalent to suffering", and you might want disabling pain to be greater than 1 if down-weighting the significance of hurtful pain relative to disabling.)

Because of such methodological choices, I am more confident in the results about the animal-years improved (which look pretty good). 

One thing to note would be that excruciating pain is rather rare across hens' lifespans, and Welfare Footprint didn't find statistically significant differences between the amount of excruciating pain experienced by the average hen in conventional cages, enriched cages, and cage-free aviaries. 
From the "Total Time in Pain" tab on the display at the bottom of this Welfare Footprint page, the average time a hen spends in excruciating pain in her life, by cage type, is:
- Conventional: 0.05 (0.03 - 0.07) hours/hen 
- Furnished/Enriched: 0.038 (0.018 - 0.058) hours/hen
- Cage-free: 0.04 (0.02 - 0.06) hours/hen
Due to the lack of statistically significant differences between the time spent in excruciating pain, it may be that changing the weights drastically wouldn't lead to discernible/actionable differences in the results

However, I think that if we weighted the difference between Hurtful --> Disabling pain (r2) higher than the Annoying --> Hurtful difference we would get meaningfully different results on suffering reduction. (As we would if we chose a different benchmark category for the definition of "suffering".)

Again, I encourage you and others to try this out--I hope the model is useful and accessible to lots of people. Thanks again for the comment and feedback
 

Thanks for the comment, Ben! 

And thanks so much to everyone doing direct work to improve animal welfare!

I'll also note that I think the counterfactual impact period was one of the model decisions I struggled the most with, which is why you can change it in the model here and see how the results change! https://my.causal.app/models/165404?token=6e8998626d0643db9c86482475aecc2c