EA - Finding before funding: Why EA should probably invest more in research by Falk Lieder

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Finding before funding: Why EA should probably invest more in research, published by Falk Lieder on August 17, 2022 on The Effective Altruism Forum. EA grantmakers dramatically differ in how they split their funds between doing good with the tools and knowledge we already have versus improving them through scientific research. This raises the question of which proportion of its funds the EA community should invest in scientific research. Are we investing the correct percentage of our funds in scientific research? Should it be lower? Or should it be higher than it currently is? We recently found that funding scientific research that aims to enable or create improved interventions can be 2-5 times as cost-effective as investing in the interventions that already exist. This seems plausible given that scientific research gave us the tools and knowledge we can now use to improve the future of humanity. If previous generations had not funded scientific research and R&D projects, there would be no medicines for preventing malaria, no long-lasting insecticide treated bed nets, no supplements for preventing vitamin A deficiency, no deworming tablets, and no vaccines. Strategically funding relevant scientific research could generate even more cost-effective opportunities to do good. But the more money we invest in research the less money we will have left for applying our best interventions. To help funding agencies and the EA community navigate this dilemma, we investigated how the total investment into a cause should be split between scientific research that might enable us to do more good in the future and doing good with the tools and knowledge that happen to be already available today. Assumptions Our analysis makes the following three assumptions: On average, additional research and R&D projects produce interventions that are substantially less cost-effective than the best interventions that already exist. The cost-effectiveness of newly developed interventions is variable. Therefore, research occasionally succeeds in inventing superior interventions, and the cost-effectiveness of those interventions can be substantially higher. When a more cost-effective intervention is invented, then the funding that the best previous intervention would have received will be invested into the new intervention instead. We measure the cost-effectiveness of research in the same units as the cost-effectiveness of the existing interventions it might help us improve on. The expected increase in the amount of good we can do with the improved interventions is a lower bound on the value of research. Approach We performed more than 300 million computer simulations to determine which way of splitting a fixed budget between developing better interventions and deploying interventions would generate the most good in total in expectation across different scenarios. For each scenario, we estimated the total good that could be achieved by first funding 0, 1, 2, 3, ., or 1000 research projects and then optimally allocating the remaining funds between the best interventions that resulted from the research and the best intervention that were available prior to the research. We then derived the optimal size of the research budget from the number of funded research projects that maximized the total good that the funding agency accomplished on average across 10,000 simulations. To derive robust recommendations, we replicated the main findings of our simulations across a wide range of conservative assumptions about the effectiveness of scientific research, the variability in the cost-effectiveness of new interventions. Concretely, we varied the average cost-effectiveness of new interventions between 10% and 110% of the cost-effectiveness of the best existing interventions with a standard deviation between 1% ...