Once we get consent, link the Wellbeing workshop transcript as an example. OK link it here -- it's at https://uj-wellbeing-workshop.netlify.app/transcript.html

make this a folding box.

focus on 2036 only here #implement

OK but an attributed tooltip quote would also be good #implement

tooltip reference and link with a direct quote

give tooltip links and direct quotes, with dates and paper names #implement -- important

"July 2025 EA Forum post' -- make this a tooltip #implement

Tooltip here about how we note there may be a range of other important benefits and perhaps costs here, including environmental benefits, reduction of animal-to-human disease vectors, etc., but we are mainly focused on animal welfare for this conference.

These are four formulations, but many of the cruxes run across each of these. There's a lot of overlap here.

Cruxes: Things like the costs of the baseline media (linked to the cell density), cost of the growth factors (will they be the expensive ones or more like hydrolysate-based ones, scale/tank/production size, cost of capital, and TEA/forecasting methodological choices.

(Consumer and government acceptance is also very likely a crux, but we're focusing on that a bit less).

Not quite -- these will come out of the PQ project -- see the 'request to evaluators' here: https://coda.io/d/Unjournal-Public-Pages_ddIEzDONWdb/Cultured-meat-PQ-Request-to-TEA-evaluators_suyucPLB#_luoC2X-O

Add a link here: "See our EA Forum post for further discussion." (hyperlink https://forum.effectivealtruism.org/posts/7ha23d3qzXiCqYLDq/is-cultured-meat-commercially-viable-unjournal-s-first)

This is a bit too simple -- see other discussion pages. For the 'what to fund' question, we need to consider the marginal benefit of funding on the probability and magnitude of success in fostering CM (sooner) and displacing animal products and animal suffering. This is discussed too much more detail in the specific PQ definitions, motivations and resources.

But "CM is plausibly able to achieve near price parity" seems highly correlated or causally entangled with "funding CM development (and supporting it politically) is likely to have high AW impact per dollar". IN particular, if it seems practically impossible for CM to ever get close to near parity. Then it seems unlikely that the CM project will be successful and thus a near guarantee that additional funding will have little impact.

But we should note or at least footnote that that's more of a necessary than a sufficient condition. CM funding could have a low impact/$ for other reasons, e.g., if, on the other hand CM is likely to be successful soon irrespective of this funding.

if it achieves --> particular if it comes close to

add discussion boxes here, so they can comment if desired

skip this one ... too indirect #implement

Give an (optional) slider for them to state what share of benefit, relative to the next best intervention, is achieved, along with 80% CIs

Just do 2036 ... keep it simple here #implement

remove bold #implement ... use less bolding in general, especially avoid it mid-sentences

Add some other potential segments, perhaps

• "business, government, and philanthropic environment and the cost of capital."

• "How is cultured meat produced -- a cost-focused background overview"

• "Constructing TEAs, uncertainty modeling, and forecasting" -- hands on modeling (post-workshop hack session, 2-5 hours)

Still add your annotations and let me know what you think about hypothesis as a format for collaborative discussion.

The high cell density is in blue, but you also put "micros" in blue, which suggests the two have a link. I don't think that's what it is. I think the high cell density will reduce the media cost, which is in green, and maybe other goals like bioreactor and operating expenses so I'm a bit confused.

Diagram says "fixed OPEX". But wouldn't t least some of the labor costs depend on scale here?

Diagram below does not really make sense. Is it a breakdown of the cost components or something having to do with levers that could make the costs go up or down substantially? This needs more clarity.

Hyperlink is not working so well

A tooltip should define what is meant by "typical here." Probably depends on the outcome of many model simulations and the central probability mass or something

Top of this, or maybe on another page, it would be nice to have some sort of mosaic graph with different cost break-downs for different scenarios. Dividing up the cost into different components to get to total cost per kilogram, and then perhaps each of those mosaic elements could link to a different section explaining it.

This part of the model and also define what you mean by "binary switch" specifically in a tooltip.

I don't think these were mentioned anywhere either - Dash. Are these growth factors? If they're not growth factors, why are you discussing them in this section?

What is albumin? You did not mention it anywhere else in this document. Is it a growth factor?

Which of the growth factors do these "solutions" pertain to?

Where did you find the market data ?

It doesn't really matter what the price is per gram of inpuy . The question is, what is the likely price per kilogram of chicken meat output. add a column for this.

Not sure how this diagram is helpful at all. Maybe put this in a folding box for now.

Tooltip example here, please.

The itemized list is not rendering correctly here.

Give listings, examples, and links/citations with tooltip quotes.

Why are you telling me about all these different kinds of growth factors? Do they all need to be used? Are they alternatives to each other? Have you defined what the terms in the "function" column mean?

And how much of them will need to be used per kilogram of chicken meat produced (or whatever weight we are standardizing things to here), what cost implications? Right to always bring things to this standard unit of cost per kilogram of chicken meat.

How does dollars per liter map into dollars per whatever unit of chicken meat we're using here? It's going to depend on the cellular density. I presume the cellular density is the same for these two types of media, or does one lead to much less dense cells?

To what extent is it clear that these can just simply be used, and to what extent is this still an important uncertainty? If it's clear that they can be used, We should make that clear-- to flag this so people don't think of it as still an important uncertainty. But we should look for more references here to be sure.

Try not to state things in a very prescriptive way. We're meant to be providing background information, not ordering people around.

Again, this really just seems too strong a statement to make. We need a little bit more epistemic modesty and reasoning transparency.

Another hovering "?" here

Where exactly does it come from though?

Maybe rewrite the headline to actually say that the total media cost is predicted to be out 40 to 70% of production cost, or whatever the numbers tell us. I don't need to expand it to get the headline result.

Any way you could cite more recent references?

Which ones give an explanation, maybe in a tooltip?

But why focus at research scale? Could you give what is projected to be a production scale?

That seems a little bit too much of a conclusion. Can you state this a little bit in a more reasoning-transparent way

Where does the supply come from? Are animals being killed here to produce it?

This might be a folding box. I'm not sure if it enters into the previous narrative. ?

So which of the above is this used for? It seems like it covers several of the above things you're calling "media". That's a little bit confusing to have this overlap of some sort.

Give me some excerpts from that page and explain what it means. You just linked to a sort of commercial page. It's not very helpful or easy to navigate.

A question mark comes up when I hover over this, but I don't see any tooltip explaining what it is.

Also, why are you talking about bovine serum if we're thinking about chicken here? At least you should explain the analogy.

Quantify this.

Maybe group the cheapest things together in one row unless there's some sort of environmental or ethical issue with them.

Tool tips should explain what each of these things are.

Not all costs, just this component of cost. Again, I want to know what share that makes up of the total to put this in perspective. It's only a minor share of total cost. It's not really a pivotal cost driver, is it?

Try to put these in terms of cost per unit of meat produced in a mature production process, and try to use the same units everywhere so we know how to compare each element and sub-element.

List these by order of estimated share of cost in a production-scale process. And give a rough estimate of those shares, and those should be on the same scale - expressed per unit of output, in the same units. Give a disclaimer, of course, that this is just based on one particular estimate, and you can link to the actual model.

Wait, that's not precisely a "step". Isn't the media used within the production bioreactors? It's not sequential. So perhaps the word "step" is confusing unless you're talking about a modeling step or something.

To what extent are hydrolysates already being used? Let's get some more sources for each of these. The costs are really important here.

What share of media costs are these in different models and reports? I thought this was possibly the largest?

Rather old reference, aren't there more recent ones, perhaps with much higher cell densities?

Sort of lost me here. I don't know what the term you're talking about is and why it's important. What does the word 'perfusion' actually mean?

Link this part of the model. Backlinks might also be good from the model to this explanation (here and everywhere else. )

Batch versus perfusion? You haven't given enough narrative here. I don't know why you're telling me this. Are these different bioreactor types, and if so, how does it map into the categories you just gave above?

This is a little confusing to me because what do you mean designed for food production? What is the standard food production use of this if not for cultured meat?

These images below are also too small.

Wait, give us a perspective on what share of the total cost this is approximately, with some ranges considering a potential producer operating at scale.

These sources are all from 2021 or earlier - can't you find some updated cost figures?

Link some pictures of these types of tanks, or perhaps a folding box showing these pictures. You'll have to do a web search to look these things up.

What is media perfusion and what is batch feeding ?

What makes a bioreactor "production scale"?

The hyperlink works, but I want a tooltip giving the exact quote that provides evidence for this claim - both the contamination and the size of the loss. ... I want this sort of documentation for all claims in general, try to use tooltips to avoid clutter.

What's the typical cost of the reactor transfer in an established, larger-scale production process? Would this still be a substantial share of total costs?

But isn't the sort of media that achieves higher density more expensive? I'm actually uncertain about this.

Source for the quote "often pharma grade?" Okay, you're relying heavily on Humbird here. Find some other sources, and I've heard that now most companies are using food grade instead of pharma grade. Look into that and discuss in tooltip footnotes.

What do you mean by "fully loaded" here?

For each phase, I want you to give some indication of the share of costs, in terms of the total cost per unit of meat, that this could potentially encompass, both at a small scale and at a larger scale.

The text is a bit crowded here, so the numbers overlap the words. Try to adjust to give it a little more space.

Give more continuous references, perhaps as tooltips, to where you are getting this information from about the process. Perhaps give citations with links and short quotes.

Link to this regulatory approval thing - how difficult/Costly is it to get that approval, or do we already have this for the important immortalized cell lines?

Doesn't really explain how the costs work. Ultimately, the banked cells are used up, correct? Are you saying that cell banking is just a tiny share of the cost here, if you end up using the whole batch, is that right?

Image is still a bit too small.

Are we trying to isolate and why? Maybe this is a tool tip? And what's enzymatic digestion?

You did not use the term "cell banking" in the flow chart above. This can be confusing when you change terms. We don't know what Maps to what

What are differentiation protocols, and why is their complexity a challenge? Maybe put this in a tooltip

What does the word pluripotent mean? Tooltip, please.

Give the name of the paper and a tooltip, and also explain what aspects of these claims the source provides, perhaps with quick quotes.

Explain, perhaps in a tooltip, why the similarity is helpful here. That I don't really know what these things mean (e.g., what does ng mean?)

Provide a tooltip/link to discussion from animal welfare advocates about this, perhaps on the EA forum.

Tooltip hover should show the name of the paper, et cetera.

Can you make an image without a bone in it that still looks like a piece of chicken meat? I don't think bones are happening any time soon in cultured meat.

What's a seed train?

Seems too strong. Media costs exceed GF costs in many formulations I've seen.

this seems a bit too strong from my reading. Media costs exceed GF costs in many formulations

This feels a bit vague to me. Are there specific policies that would be affected?

Make this less prominent -- no 'header' until after the workshop

remember -- we are not focused on cross-population comparisons for this workshop. It's more about 'which interventions yield greater welfare', which would generally involve differences in difference, ideally across comparable populations (but not always)

Where did the discount and time factor come from? Where did these definitional equations come from? I didn't think most emply estimated WELLBY measures considered multi-year collection or impact. And are they really discounting?

Cut this. I don't think it necessarily holds -- a lot of interventions impact mortality.

Add to footnote -- the 'incremental' WELLBYs may be captured by observing differences between comparable treated and untreated populations.

it's not really 'accounting' -- these are conceptual

A link to this would be helpful. The "Green Book". (I wonder -- how impactful has this actually been on British policy?)

I suspect we don't have any good measures of this? There's the Peasgood paper but I don't think that was in a LMIC and I'm not sure how much it has been vetted?

We'd especially like pre-session feedback on

• Are these ~accurate?
• Are they useful? At the right level
• What is redundant?
• Which issues should we skip (as less important to intervention choices for LMIC, mostly-resolved, or intractable?)
• What is missing?
• Is there a better overall structure and framing for these?
• Where does it go into too much detail? Where is it too opinionated in cases where we should leave things open?
• Are we failing to attribute any important sources for language, arguments, or claims? *

underexplained/too brief perhaps

why do we use the word 'layer' -- is this the term in the literature?

"Close to linear" is interesting -- a footnote could expand on this a bit

This was mentioned above, but does it do so in a scale-sensitive way?

As I suggested, it's not enough to have it be 'somewhat predictive'

This needs more context and explanation. I've forgotten what g of x is here, and what's the actual calculation? Also, this doesn't seem to be illustrating the point that it means to. As I move the slider, population B always seems to be higher, but also it seems like we're getting away from the discussion of the relative impact of different interventions. We don't want to just simply compare populations. If this does pertain to interventions, explain better.

Exokain a bit more (as a footnote) what the 'transformation' means here and why/when it's used

Do they? Magnitudes of what? Explain. Give a 1-2 sentence exampls as a footnote

This is simple and perhaps obvious, but good for illustrating the simple WELLBY linear WELLBY concept, but that's already been explained above. I'm not sure what should maybe be put at the top. I'm not sure if it's useful down here. OK put this at the top, in a folding box -- it just helps to make sure we're all in on the same page about the definition of the WELLBY here.

Perhaps it would also be helpful to include some sort of adjusted WELLBY calculator interface that's a more sophisticated concept people might not appreciate, particularly embodying the approach in Benjamin and others.

This explanation is not clear. It could be improved, it's a bit too literal. Why do ordinal responses only tell us in which interval a latent value falls into?

This might also be worth folding

add a bit more 'narrative' here

comparison of what -- be more precise

An example here would be very helpful. ... Perhaps even an interactive display.

Monotonic transformations of what?

This should be fleshed out in more detail and rigor, along with some responses to it, and probably belongs earlier on in the discussion.

....

What do you mean, comparing "average happiness between groups is not identified"? What is the thing that is not identified?

This diagram is not fully explained. I don't see how it relates to the rest of the content either.

This flow chart is too small and it's underexplained. I don't understand what each of these is meant to mean and how they fit together.

That seems fairly tractable for us to at least share our knowledge about in this conference. Cool.

That's the second question combo which we'll be setting up an explainer on. Once we do, we should link that and also link that PQ here

But 'true mapping' Needs a bit more definition. Maybe put it in square quotes to note that (or link the tentative formulation in the PQ space)

Measuring this seems fairly high value to me if it can be done at a low cost.

I wouldn't state this so directly and clearly, and give attributions to people making the claims that these represent high value. We want this to be one of the outputs of the workshop, but I'm not sure that all of these are in fact high value. Some of them might be very much intractable.

But this can bring its own problematic effects if people feel prompted or motivated to report an improvement to please the experimenters, etc.

That's the sort of milquetoast thing I want to avoid. People will always say, "Do compare multiple things, don't treat something as the gospel truth, etc." It's not a statement with a lot of meaning.

I don't like the core practical recommendations for having a section here. The recommendations are meant to come out of the workshop. We shouldn't be pre-establishing them. It's OK if you want to compare the recommendations coming out of the existing reports & literature, though.

define this

what is a 'mapping model'? UNderexplained

AKA Wellby? But this is not practical for comparing interventions with different measurements

give a bit more on how these do mental health weights

citation and detail (footnote?) needed here

citation and explanation needed

How are these measured in the relevant settings and how does it differ from WELLBY? These are based on external measurements?

Searate QALY from DALY maybe, they are distinct in key ways

this seems like it must be incorrect/imprecise. Is a year with a disability actually measured here as being as bad as a year of life lost? This needs a better definition ... how is it measured

this seems a bit babytalk/obvious

Give a link... and what is the basis for this? Meaningful is somewhat of a vague term. It doesn't get at the hard questions about what measures we should use for comparing specific interventions.

This is highly counter-intuitive to me. How do survey response times help?

Latent distribution of what?

Didn't we already get at this above? What's new about this?

What is meant by "non-identified" here?

This kind of seems like a weak response unless I'm missing something. Even if they are not arbitrary, even if they have informational value, it doesn't tell me that they provide reliable information in comparing the benefit/cost across multiple interventions which all improve people's lives.

But this doesn't seem to have been the challenge as posed. I'm not sure this is the most relevant thing to lead with, or maybe it needs to be motivated better

this seems a bit obvious?

How does that affect the relative comparison of interventions?

'is meaningful' requires clarification

Why would that be the case? Who is making this claim?

Again, this is too shorthand. I need an explanation, if necessary, in footnotes or a folding box, of what all this means.

'meaning' should be clarified, perhaps with reference to the gold standards I suggest you add above. Should we state this in terms of an individual's willing to make "time trade-offs" (e.g., would be willing to go from 7-->6 for one year in exchange for going from 3-->4 another year), or probability trade-off (would take a coin flip over the above ), or person trade-off (a third party willing to move one person from 7 to 6 it meant moving someone else from three to four) ... [or vice versa in all cases]

Obviously this notation is extremely crude! I wonder if important nuance is lost here

E.g.,. is this 'within person' or 'across people'?

"Validity" is vague, needs a better definition. And perhaps something more informative in terms of the metric offering value would help. Naturally, no metric would be perfect, and even if a model's assumption are violated in practice, the assumption might be close enough to holding that the difference doesn't matter much.

We need a better definition of the 'gold standard here'. What would an 'accurate comparison' tell us? What is the appropriate measure of 'degree of inaccuracy'?

how to test this? Define 'log transformation' more clearly here and what are the assumptions necessary for it to accurately reflect tradeoffs?

What's meant by 'summing' and what does 'invalid' mean here?

But this does not seem consistent. You are saying "when heterogeneity is most dangerous", but this doesn't look like heterogeneity.

Can you justify this a bit more, both in equations and in an intuitive explanation of what the problem is?

what's meant by 'instrument' here?

"less of a threat" is vague, needs clarification. And why? Give a citation and/or a proof and further explanation (perhaps in a footnote)

define 'reporting functions' (footnote or folding box, linked here)

Adapt this discussion to focus more on comparing different interventions (see the canonical example but also link real-world relevant comparisons and studies) ... where these interventions may take place in nearly-identical, similar or distinct contexts, affect similar or different outcomes (wealth health, etc.)

this needs more explanation. What does 'fail' mean here? What's being compared, and how do the estimates compare with the ground truth?

notation not rendereing well here

Maybe add a footnote explaining what sort of "utility" we are considering here, noting this is a bit of an oversimplification of welfare considerations.

Is 'incremental wellby' a term used in any literature or practice? Give a link

Who stated this? How is it 'common'? Maybe just change this to "Equal scores mean equal welfare" is stronger than most applications need.

"Comparisons involving mortality" #implement

Might benefit from some further explanation. How could Level-based be used for comparing interventions -- that's not clear here. How many people are we summing over? How do 'dead people' enter into that? Some explanations can go in footnotes.

Is this really How it's depicted in the literature? It's a bt confusing at first, because it looks liek one has to know two things for incremental WELLBYs and only one thing for the Level based measure. Furthermore, the incremental one seems to requre knowledge of a counterfactual. However, one mght be able to have an estimate of a difference without knowing the levels. Isn't there a better notation/explanation for this?

I'm missing the definition of the indices i and t, as well as the definition of the variable LS -- #adjust #implement

Are these Really all the sources? I thought we had more.

Make this a folding box #implement

Adjust this -- "If you prefer your response to remain anonymous, please use a pseudonym and try to use the same one consistently if you're providing multiple responses." If you are fine with internal recognition but don't want any public attribution, please let us know and share any other concerns in the field at the bottom.

I'm considering adjusting this one to

Consider the 'value obtained when using the best feasible measure for cross intervention comparison in contexts like the focal context'. What share of this value is obtained, in expectation, from using the simple linear WELLBY measure for all interventions? Please give your central belief, and 90% credible intervals"

-- with a slider that goes from zero to one, and two other sliders that allow that allow you to specify the lower and upper bound of the 90% CIs.

NotebookLM:

"they applied their methodology to nine prominent results from the happiness literature—including the Easterlin Paradox, the U-shape of happiness in age, the ranking of countries by happiness, and the effects of marriage and children—and showed that the standard conclusions in all nine areas could be reversed using monotonic (specifically lognormal) scale transformations. They argued that these reversing transformations were "plausible," claiming they were no more skewed than the U.S. wealth distribution

However, later work questions the plausibility of this. .

See https://www.econstor.eu/bitstream/10419/232657/1/dp13905.pdf

It will take more than 60 minutes, but a skim might be doable in 60 minutes.

Add "(rsvps)" -- Also update this, I think we have over 20 by now.

is this ggplotly? Shouldn't it be dynamic? I don't seem to be able to adjust it

These are probably not updated costs -- Update and implement

Not 'video', possibly some written content, or we can extract issues from his evaluation to ask Benjamin et al.

The "least regret" is a formal term in information theory, I believe, or from Bayesian updating. Provide a footnote defining and referencing it. #Implement

We'd especially like pre-session feedback on

• Are these ~accurate?
• Are they useful? At the right level
• What is redundant?
• Which issues should we skip (as less important to intervention choices for LMIC, mostly-resolved, or intractable?)
• What is missing?
• Is there a better overall structure and framing for these?
• Where does it go into too much detail? Where is it too opinionated in cases where we should leave things open?
• Are we failing to attribute any important sources for language, arguments, or claims? *

But we're focused on interventions, not on average happiness between groups. - Be more specific to this. #Implement

--> "A key critique"

'potentially biasing'

what's meant by 'corner case' here?

what's MK? I forgot. Has it been defined?

Give a footnote with some examples here. What do the studies involving LMIC interventions do?

'cancels' is vague -- explain what is meant here in a footnote. #implement

the diagram is too small, and was never explained!

'influential' -- that's subjective. ///Link to an example

too inside-info for a whole box. -- make this a footnote at most

Missing the standard framing of the LS question here

too much info -- don't say this here, this is process stuff

this is too small and also underexplained

Especially in LMICs

this doesn't need bold

wait -- hallucination -- you renamed the title here!!

remove 'entire'

Adjust this to "if you compare interventions that affect mortality (or, in some accounting, birth rates)"

LS is not defined here, nor is 'i'

Or 'for interventions that change mortality rates' perhaps?

I don't think I want to show this here because I don't want people to anchor in stating their beliefs. #todo #adjust #implement

How Can we fix this 'runtime error'.I think it was working before. The "target year" should be the "projection year" in the sidebar model parameters. The default year was 2036. #implement