Note: I am not a malaria expert. This is my best-faith attempt at answering a question that was bothering me, but this field is a large and complex field, and I’ve almost certainly misunderstood something somewhere along the way. Summary While the world made incredible progress in reducing malaria cases from 2000 to 2015, the past 10 years have seen malaria cases stop declining and start rising. I investigated potential reasons behind this increase through reading the existing literature and looking at publicly available data, and I identified three key factors explaining the rise: 1. Population Growth: Africa's population has increased by approximately 75% since 2000. This alone explains most of the increase in absolute case numbers, while cases per capita have remained relatively flat since 2015. 2. Stagnant Funding: After rapid growth starting in 2000, funding for malaria prevention plateaued around 2010. 3. Insecticide Resistance: Mosquitoes have become increasingly resistant to the insecticides used in bednets over the past 20 years. This has made older models of bednets less effective, although they still have some effect. Newer models of bednets developed in response to insecticide resistance are more effective but still not widely deployed.  I very crudely estimate that without any of these factors, there would be 55% fewer malaria cases in the world than what we see today. I think all three of these factors are roughly equally important in explaining the difference.  Alternative explanations like removal of PFAS, climate change, or invasive mosquito species don't appear to be major contributors.  Overall this investigation made me more convinced that bednets are an effective global health intervention.  Introduction In 2015, malaria rates were down, and EAs were celebrating. Giving What We Can posted this incredible gif showing the decrease in malaria cases across Africa since 2000: Giving What We Can said that > The reduction in malaria has be

> How the dismal science can help us end the dismal treatment of farm animals By Martin Gould ---------------------------------------- Note: This post was crossposted from the Open Philanthropy Farm Animal Welfare Research Newsletter by the Forum team, with the author's permission. The author may not see or respond to comments on this post. ---------------------------------------- This year we’ll be sharing a few notes from my colleagues on their areas of expertise. The first is from Martin. I’ll be back next month. - Lewis In 2024, Denmark announced plans to introduce the world’s first carbon tax on cow, sheep, and pig farming. Climate advocates celebrated, but animal advocates should be much more cautious. When Denmark’s Aarhus municipality tested a similar tax in 2022, beef purchases dropped by 40% while demand for chicken and pork increased. Beef is the most emissions-intensive meat, so carbon taxes hit it hardest — and Denmark’s policies don’t even cover chicken or fish. When the price of beef rises, consumers mostly shift to other meats like chicken. And replacing beef with chicken means more animals suffer in worse conditions — about 190 chickens are needed to match the meat from one cow, and chickens are raised in much worse conditions. It may be possible to design carbon taxes which avoid this outcome; a recent paper argues that a broad carbon tax would reduce all meat production (although it omits impacts on egg or dairy production). But with cows ten times more emissions-intensive than chicken per kilogram of meat, other governments may follow Denmark’s lead — focusing taxes on the highest emitters while ignoring the welfare implications. Beef is easily the most emissions-intensive meat, but also requires the fewest animals for a given amount. The graph shows climate emissions per tonne of meat on the right-hand side, and the number of animals needed to produce a kilogram of meat on the left. The fish “lives lost” number varies significantly by

TL;DR Having a good research track record is some evidence of good big-picture takes, but it's weak evidence. Strategic thinking is hard, and requires different skills. But people often conflate these skills, leading to excessive deference to researchers in the field, without evidence that that person is good at strategic thinking specifically. I certainly try to have good strategic takes, but it's hard, and you shouldn't assume I succeed! Introduction I often find myself giving talks or Q&As about mechanistic interpretability research. But inevitably, I'll get questions about the big picture: "What's the theory of change for interpretability?", "Is this really going to help with alignment?", "Does any of this matter if we can’t ensure all labs take alignment seriously?". And I think people take my answers to these way too seriously. These are great questions, and I'm happy to try answering them. But I've noticed a bit of a pathology: people seem to assume that because I'm (hopefully!) good at the research, I'm automatically well-qualified to answer these broader strategic questions. I think this is a mistake, a form of undue deference that is both incorrect and unhelpful. I certainly try to have good strategic takes, and I think this makes me better at my job, but this is far from sufficient. Being good at research and being good at high level strategic thinking are just fairly different skillsets! But isn’t someone being good at research strong evidence they’re also good at strategic thinking? I personally think it’s moderate evidence, but far from sufficient. One key factor is that a very hard part of strategic thinking is the lack of feedback. Your reasoning about confusing long-term factors need to extrapolate from past trends and make analogies from things you do understand better, and it can be quite hard to tell if what you're saying is complete bullshit or not. In an empirical science like mechanistic interpretability, however, you can get a lot more fe