Thanks for all of the hard work you put into developing and maintaining it!
MathiasKB🔸
Karma: 6,485
Thanks, this was quite informative. Would love to read the full report, but $1000 is a bit steep!
Without having read the letter yet, why do you find it questionable?
Good question, not sure how I get it into my email actually, I can’t find it on the website either
edit: I think it’s through the forecasting newsletter
I can highly recommend following Sentinel’s weekly minutes, a weekly update from superforecasters on the likelihood of any events which plausibly could cause worldwide catastrophe.
Perhaps the weekly newsletter I look the most forward to at this point. Read previous issues here:
Jeff, your notes on NAO are fascinating to read! I have nothing to add other than that I hope you keep posting them
Hi Ian,
Thanks for the question! I’ve been meaning to write down my thoughts on this for a while, so here is a longer perspective:
In 2015 USAID teamed up with Givewell to cash-benchmark one of its programs. The evidence came back showing that cash-transfers outperformed the program on every metric. What gets brought up less often is that the programme got its funding renewed shortly after anyways! The cash-benchmark alone was not sufficient, you also need some policy to require programs worse than cash should be wound down.
This is a sentiment I’m fully behind. But what exactly that policy should look like is where it gets tricky.
How should the ministry cash benchmark a music festival in Mali?[1] What is the cash-benchmark for a programme to monitor the Senegalese election to ensure a fair election? If the cash-benchmark should only be for certain types of programming amenable to cash comparisons, such as global health, how will that shift funding?
I worry that instituting a selective high bar will move funding from away broadly cost-effective areas which can be benchmarked against cash, to broadly ineffective areas which can’t be easily benchmarked against cash.
But even within areas amenable to cash-benchmarking, it’s unclear what the policy should look like. How should the ministry cash-benchmark its funding to a large multilateral which will go to fund a thousand programmes across the world?
The answer to this, which many arrive at is: “Cleary we need to move from demanding literal cash-arms to just making estimates of how impactful programmes and organizations are compared to cash-transfers. That way we still get the nice hurdle-rate that programmes must be compared against, which is what we were really after anyways”
But that development ministries should systematically estimate and compare the impact of projects is what development economists have been shouting for decades!
To an extent, the ministry’s lack of systematic measurement and comparison is a feature not a bug. Almost any instantiation of cash-benchmarking removes wriggle room to fund projects which are valuable for reasons you didn’t want to state out loud. From a ministers perspective, cash-benchmarking doesn’t solve any problems, it creates one!
- ^
This is not a facetious example, but a real project funded by the Norwegian government.
- ^
As a side note, one thing I find amusing is just how much it sucks to announce your org’s shut down after Maternal Health Initiative set the bar so ridiculously high.
Even at shutting down they have us beat!
Any ideas for what we can do to improve it?
The whole manifund debacle has left me quite demotivated. It really sucks that people are more interested debating contentious community drama, than seemingly anything else this forum has to offer.
Why are seitan products so expensive?
I strongly believe in the price, taste, convenience hypothesis. If/when non-animal foods are cheaper and tastier, I expect the west to undergo a moral cascade where factory farming in a very short timespan will go from being common place to illegal. I know that in the animal welfare space, this view point is often considered naive, but I remain convinced its true.
My mother buys the expensive vegan mayonnaise because it’s much tastier than the regular mayonnaise. I still eat dairy and eggs because the vegan alternatives suck.
What I don’t understand is why vegan alternatives have proven so difficult to make cheap and tasty. Are there any good write ups on this?
Like when I go to a supermarket in Copenhagen, every seitan product will charge a significant markup over the raw cost of the ingredients (Amazon will sell you kilos of seitan flour at very little cost).
Do consumers have sufficiently inelastic preferences that a small market high-markup is the most profitable strategy? Is the final market too small for producers to reach economies of scale for seitan, or is it just difficult to bootstrap?
I would love to better understand what the demand curves look like for various categories of vegan products, as I really can’t wrap my mind around how the current equilibrium came about
I’m keeping an eye out for Sentinel’s analyses: https://forecasting.substack.com/p/alert-minutes-for-week-172024
I’m worried too!
Does anyone know if there been any research into creating engaging television for factory farmed animals? Google scholar didn’t get much outside of using TV to induce feeding in chickens. I know there have been evaluations of branched chains as a way to improve the conditions for pigs, but I haven’t seen any evaluation of television.
There’s 24⁄7 television made for house cats, why couldn’t something similar exist for chickens?
I’m not going to find time to look into this myself, so if somebody finds the idea intriguing, don’t hesitate with starting!
at giveffektivt.dk we cover transaction costs of donating. Similar to donation matching, it’s likely the money we spend on transactions would be donated anyways.
I think it’s fine to do this, but i’m unsure where the line should be drawn. We find that many people who donate worry far too much about transaction and overhead costs. By alleviating one of those we make it much more attractive to donate (though I don’t think we’ve A/B tested this actually).
But following this logic should we say that “5 dollars could save a life” if we thought this would increase total donations? Despite this sentence being literally true, it feels highly misleading and I would have mixed feelings about such a message. (In practice I don’t think stating this would increase donations—if anything the opposite)
My own belief is that this type of messaging often brings its benefits in the short term, but incurs its costs in the long term, if a donor feels deceived and becomes less inclined to donate going forward.
This ultimately is the heuristic I go by. If someone were to read up on a claim after donating, would they feel deceived? If yes, then don’t make the claim.
I don’t personally think I would feel deceived about donor matching, so my intutition is that its fine, but maybe others feel different.
Thanks for carrying out this analysis! Do you have a spreadsheet with the calculations? Personally, I find it’s much easier for me to understand the calculations and assumptions in that format. Being able to make a copy and play around with the input values to see which inputs drive the end line result is also super helpful.
Thanks! Very kind of you to say that
Gene drives remove the need to continuously rear and drop screwworms. Counting insect welfare I would wager advocacy for using gene drives looks better not worse.
Thanks, I’m looking forward to that post!
I’ll be honest, to me the numbers sound too good to be true, so I’m curious to understand the assumptions that go into those numbers and whether I agree with them.
If the numbers really are that good that’s quite exciting!
https://forum.effectivealtruism.org/topics/corporate-animal-welfare-campaigns
According to research by the Welfare Footprint Project, both of these asks substantially decrease hours in pain experienced by farmed chickens,[2][3] decreasing chicken suffering by an estimated 30%–60%.[4][5]
According to estimates by Šimčikas,[6] corporate campaigns between 2015 and the end of 2018 will improve the welfare of 9 to 120 years of chicken life per dollar spent.
EDIT: Someone on lesswrong linked a great report by Epoch which tries to answer exactly this.
With the release of openAI o1, I want to ask a question I’ve been wondering about for a few months.
Like the chinchilla paper, which estimated the optimal ratio of data to compute, are there any similar estimates for the optimal ratio of compute to spend on inference vs training?
In the release they show this chart:
The chart somewhat gets at what I want to know, but doesn’t answer it completely. How much additional inference compute would I need a 1e25 o1-like model to perform as well as a one shotted 1e26?
Additionally, for some x number of queries, what is the optimal ratio of compute to spend on training versus inference? How does that change for different values of x?
Are there any public attempts at estimating this stuff? If so, where can I read about it?