Hi David,

You may want to check out Joel Becker's financial life model. I have not played with it, but it seems quite comprehensive.

Assuming we know of both organizations above that their contributions were necessary, both can claim having helped 600,000 chickens, without needing to help 1,200,000 chickens in total.

This problem cannot be mitigated by thinking probabilistically. If there is probability p_s_A (p_s_B) of organisation A (B) being successful acting alone, p_s of organisations A and B being successful acting together, p_A (p_B) of organisation A (B) acting, and impact N given success, the expected counterfactual value of:

• A acting is CV_A = ((1 - p_B)*p_s_A + p_B*p_s - p_B*p_s_B)*N.
• B acting is CV_B = ((1 - p_A)*p_s_B + p_A*p_s - p_A*p_s_A)*N.

The sum of the expected counterfactual values of A and B is CV = CV_A + CV_B = ((1 - p_A - p_B)*(p_s_A + p_s_B) + (p_A + p_B)*p_s)*N. This can be as large as 2*N when A and B can never succeed alone (p_s_A, p_s_B = 0), A and B always succeed acting together (p_s = 1), and A and B are certain to act (p_A, p_B = 1).

The problem is solved using Shapley values. The expected Shapley value of:

• A acting is SV_A = ((1 - p_B)*p_s_A + p_B*p_s/2 - p_B*p_s_B)*N.
• B acting is SV_B = ((1 - p_A)*p_s_B + p_A*p_s/2 - p_A*p_s_A)*N.

The sum of the expected Shapley values of A and B is SV = SV_A + SV_B = ((1 - p_A - p_B)*(p_s_A + p_s_B) + (p_A + p_B)/2*p_s)*N. This can only be as large as N when A and B can never succeed alone (p_s_A, p_s_B = 0), A and B always succeed acting together (p_s = 1), and A and B are certain to act (p_A, p_B = 1).

Thanks, Vince!

their [Sinergia's] CEA covered a smaller percentage of their work.

I think this can indeed be important. I estimated Sinergia Animal's meal replacement program in 2023 was 0.107 % as cost-effective as their cage-free campaigns. So I would say that x % of their marginal funding going towards their meal replacement program would decrease their marginal cost-effectiveness by around x %. I think your CEAs should ideally refer to the expected additional funding caused by ACE's recommendations, not a fraction of the organisations' past work. GWWC's evaluation argued for this too if I recall correctly.

We think it's reasonable to support both a charity that we are more certain is highly cost-effective (such as ÇHKD) as well as one that we are more uncertain is extremely cost-effective (such as Sinergia).

Even if the organisation whose cost-effectiveness is more certain is way less cost-effective in expectation? If so, I encourage you to disclaim your recommendations are risk averse (as GiveWell does with respect to their Top Charities Fund), and clarify how much.

While ACE values plurality, we don't take a "best-in-class" approach and wouldn't rule out recommending multiple charities doing similar work.

Would you still recommend many organisations doing similar work if you thought their cost-effectiveness differed significantly? I would drop a recommendation whenever the reduction in impact linked to the recommended organisation receiving less funds was exceeded by the increase in impact linked to other organisations receiving more funds. For example, if you thought recommendation A was 10 % as cost-effective at the margin as recommendation B, and that dropping recommendation A would decrease the funds of A by 100 k$, increase the funds of B by 50 k$, increase the funds of roughly neutral (non-recommended) organisations by 40 k$, increase donations to your movement grants' fund by 10 k$, and believed this fund was 2 times as cost-effective at the marfin as recommendation B, dropping recommendation A would be as good as directing 60 k$ (= (-100*0.1 + 50 + 40*0 + 10*2)*10^3) to B. In this case, it would be worth dropping recommendation A. Have you considered reasoning along these lines to decide on whether to make a recommendation or not? I understand there is lots of uncertainty about comparisons between the marginal cost-effectiveness of organisations, and how dropping or adding a recommendation would influence the funding of your recommendations. However, you are already making judgements about these implicitly. I think being explicit about your assumptions would help you clarify them, and improve them in the future, thus eventually leading to better decisions.

@Mata'i Souchon, I have updated this paragraph. I agree more actors would be responsible for the impact linked to AWF granting more to organisations as cost-effective as SWP (me, AWF, their donors, and the organisations) than to that linked to me donating more to such organisations (me, and the organisations). My counterfactual value, which was I estimated in my post, is the same in both cases, but my Shapley value, which is what matters, is larger in the latter.

I have reverted the changes regarding the Shapley value. Thinking more about it, I realised what matters is not the number of necessary actors, but whether their actions are sufficiently independent from my decision about the offer, which I think they are.

Great points, Toby!

If the others have already committed to their part in a decision, the counterfactual value approach looks better.

More generally, some actors should maximise their counterfactual value if their actions are sufficiently independent from those of others. These need not have commited to some actions. The key is that the probability distribution of the actions of others is not much affected by those of the actors maximising counterfactual value.

Agreed. Great point, Toby!

Thanks for the context, Nicoll!

MEL can contribute to building an evidence base for interventions and to know when to pivot or scale. It is therefore important for The Mission Motor to not only support interventions that are assessed as being cost-effective and impactful now, but also to help collect data on existing, or novel interventions without a firm evidence base yet, that have the potential to be impactful.

I very much agree with the 1st sentence above. On the other hand, I think the vast majority of animal welfare organisations lacks the potential to become 10 % as cost-effective as SWP. So I believe being highly selective about which organisations to work with would still be good.

Thanks for the comment, @PreciousPig! I strongly upvoted it. I am tagging you because my initial comment only included the 2 sentences before this one.

Dear Team of Veganuary,

I am tagging @Toni Vernelli such that Veganuary's team knows about your comment (only the author of the post is notified of new comments by default).

I think there are some incredibly promising metrics about Veganuary’s success, for examnple this 2019 survey on why people go Vegan in which 12800 people participated, out of them 369 (a little under 3%) said that Veganuary was the first thing that seriously made them consider going Vegan.

"369 people [2.88 % (= 369/12,814) of the vegans surveyed] succesfully took part in Veganuary [being vegan for 1 month]". It does not follow that Veganuary caused these people to become vegan. Moreover, even if everyone who successfully took part in Veganuary became permanently vegan, they could have become so a little later anyway without Veganuary.

Nitpick. I wonder whether you are using "for example" to present your strongest argument for Veganuary being cost-effective. If yes, I think "crucially" would convey your views more faithfully than "for example".

If we assume this metric is accurate and still holds true today, and that roughly 1% of the people in Europe and North America are Vegan (or very roughly 10 milllion people out of 1 billion people), as many as 280 000 people might be Vegan today because of Veganuary. If we further assume each Vegan person saves on average one animal’s live a day, that would be 100 000 000 (100 million) animals spared each year due to Veganuary. 

The survey you linked to looked into 0.128 % (= 12.8*10^3/(10*10^6)) as many vegans as those you estimated exist in Europe and North America. So here is room for huge selection bias. The number of people successfully participating in Veganuary as a fraction of the number of vegans could be as low as 3.69*10^-5 (= 0.00128*0.0288) even if the data from the survey was 100 % reliable (athough highly selected).

Besides continuing to share the number of Email signups, here are some ways how I think you could increase / confirm the accuracy of the YouGov survey:

I would ask questions like these to random (representative) samples of the general population in the target countries:

• How much more or less poultry meat would you have consumed in January without Veganuary?
• How much more or less poultry meat would you have consumed in January without Plantuary?

The possible answers could be like these:

• I consumed 100 % less poultry meat (I did not consume poultry meat).
• I consumed 80 % less poultry meat.
• ...
• I consumed roughly the same poultry meat.
• I consumed 20 % more poultry meat.
• ...
• I consumed 100 % more poultry meat (I doubled my consumption of poultry meat).
• I more than doubled my consumption of poultry meat.

I would have similar questions for eggs, fish, and seafood besides fish. My suggested questions:

• Focus on the animal-based products linked to the vast majority of animal suffering. They do not ask about whether people participated in Veganuary because what ultimately matters is whether they reduced their consumption of animal-based foods.
• Include control questions about Plantuary, which sounds similar to Veganuary, but does not exist. The effect of Veganuary should be measured relative to that of the control campaign. I expect people will report reducing their consumption of animal-based products because of Plantuary, in the same way that 4 % of Americans report believing lizardmen are running the Earth.
• Have continuous answers which offer more information. I am wary of questions which can only be answered as yes or no. Social desirability bias will prompt people to report decreasing the consumption of animal-based foods even if the decrease was negligible.

I think it would be great if Veganuary partnered with Faunalytics or the Humane and Sustainable Food Lab to run a rigorous survey.

Thanks for the update, Nicoll and Tom!

From mid-2023 to 2024, we worked with 27 organizations, providing both short-term support (19 organizations, up to 3 months) and longer-term support (8 organizations, 4+ months).

Are you aiming to work with the most cost-effective animal welfare organisations? I think you would have to spend more to increase by 1 % their cost-effectiveness than that of a random organisation helping farmed animals. However, I believe the most cost-effective animal welfare organisations are way more cost-effective, such that you would have to spend less to achieve the same absolute increase in cost-effectiveness (which is the product between the initial cost-effectiveness, and relative increase in it you caused). I would say there is lots of variation in the cost-effectiveness of animal welfare organisations:
 

• I estimated the Shrimp Welfare Project (SWP) has been 412 and 173 times as cost-effective as broiler welfare and cage-free campaigns.
• I estimated Veganuary in 2024 and School Plates in 2023 were 1.20 % and 19.4 % as cost-effective as cage-free campaigns.
• I estimated the Fish Welfare Initiative's (FWI's) farm program from January to September 2024 was 1.55 % as cost-effective as cage-free campaigns.
• I estimated Sinergia Animal's meal replacement program in 2023 was 0.107 % as cost-effective as their cage-free campaigns.

Thanks for the post!