For future searching, where/how did you come across that paper?
Anon-biosec-account
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Good find—thanks for sharing that paper which I hadn’t included. If I update the post I’ll add that.
I haven’t thought much about this so can’t add anything useful at the moment. If I think of / come across anything I’ll reply again.
Good point. This is similar to what I was trying to get at when talking about lack of willingness to engage in probabilistic reasoning.
Thanks a lot for the comment. I was a bit nervous to put my first post up so some positive feedback is very much appreciated.
Thanks a lot for the comment. I do think that what your gesturing at makes sense: if I understand correctly you are saying that certain physical interventions can have more predictable effects that ‘biological’ ones because we have a decent idea of exactly how they work. In some cases this is definitely true: as an extreme example, we don’t need RCTs of aeroplane safety as we have a very good understanding of the physical processes and are able to model them well. If we have an airborne pathogen, it’s hardly necessary to run an RCT to see whether or not there is an effect of a stay at home order: there will be one.
In many of the example questions I gave though, I think the fact that there is a large behavioural component pushes us closer to the situation we have with drugs than to the aeroplane. For example, although it could be demonstrated in a laboratory which of mask or shield is actually more effective at blocking exhaled particles, it would be harder to capture the different effects that each has on how often you touch your face, how often it is removed, or other aspects of compliance. These will differ a lot between people, so you’d need to test it on a large group, and the social setting might influence behaviour. I don’t think that we can decompose the often important behavioural component of these interventions in the same way that we can the physical components.
That said, the air filtration question I posed might not have been well chosen. As you point out, it seems reasonable that we can get a good understanding of whether that is likely to be helpful by applying what we know about the filters and viral transmission. Of the questions I posed, RCTs are likely to be the least useful there and may not be useful at all.
However, I do have some thoughts on why an RCT could still be worthwhile. I’m not saying these because I disagree with your points; I’m just providing some possible counterargument.
Learning: by introducing the filters not in an RCT, you are basically doing an experiment but losing the opportunity to learn from it. Even if it has been decided that filters should be introduced in all schools/offices (or whatever unit), it won’t normally be possible to install all of those in parallel. So there is a time where some offices have the filter and some don’t. As long as you can randomise this, you can take advantage of the differences in time for implementation in something like a stepped wedge cluster randomised trial. The effect could be analysed on an ongoing basis in a Bayesian analysis such that if there are large effects they would be detected early in the experiment and implementation of the remaining filters can be accelerated. If you are doing something like this across several interventions, this would help with deciding which to prioritise.
Cost-benefit: There are ~ 137,000 schools in the US. I don’t know how much it costs to install and maintain filtration systems, but I imagine it is not negligible. There are a lot more schools globally. Doing an RCT comparing e.g. air filtration to opening the windows could save quite a bit of money if it turns out that filtration systems don’t provide additional benefit.
Implementation and interaction with behaviour: even assuming that they do work, do people use them? Maybe the filtration is noisy so teachers turn it off; maybe they simply forget to turn it on. In medicine, even with drugs that demonstrably improve the patient’s condition, adherence is (to me) surprisingly low. Perhaps large rooms where people tend to congregate most cannot be adequately filtered, maybe the filtration system gives people a sense of security so they congregate more.
Overall, I think the areas where trials would be most useful are those where we can expect relatively modest effects and where there is a larger behavioural component. The combination of modest effects, if better understood, might be quite important.
Non-pharmaceutical interventions in pandemic preparedness and response
Some quick thoughts (there is certainly already research on these but they seem important, and I don’t know about reliability of existing research):
Scope insensitivity: e.g. why do people find it hard to care proportionally more about proportionally bigger things?
Probabilistic reasoning: e.g. how can decision makers be ‘taught’ to take seriously low probability, high impact events?
Decision-making under uncertainty: e.g. how can this be improved? Can be people be efficiently taught to become more bayesian?
Group decision making: e.g. do more diverse groups really make better decisions?
Meta: e.g. how can social science become more reliable?
I like this perspective. I’ve never really understood why people find the repugnant conclusion repugnant!
Not really answering your question, but there is some recent work attempting to forecast clinical trial results that may be relevant: Can Oncologists Predict the Efficacy of Treatments in Randomized Trials? Kimmelman (the senior author) is doing other work on the topic too (e.g. here). I’m not aware of much published work in this space in a biomedical context.
My guess is that key decision makers in medicine (e.g. funders of trials), would not be very open to paying attention to forecasts (even if shown to be accurate to some degree), as there is a very strong culture of relying on data and in particular on RCTs.
This may be less meta than you are hoping for, but may contain some useful advice/references: The dos and don’ts of influencing policy: a systematic review of advice to academics. Influencing policy is at least one way that academic ideas can travel to the wider world.
I expect another is producing accessible content on the topic in question (e.g. writing popular blog posts, books, documentaries). It seems like these can sometimes be a catalyst for ideas becoming more widely known in the public. Examples of books that might have had or could have a broad impact are Animal liberation (Peter Singer), Silent Spring (Rachel Carson), Doing Good Better (Will Macaskill) or Human Compatible (Stuart Russel).
As someone who did an undergraduate degree in biology, I think that as a computer scientist you probably already have many of the skills that you’d need to contribute to biology research directly. Welfare bio is a very new field so getting on top of the literature would likely not be too tricky, and most biologists would not have an in depth understanding of that particular sub-field anyway. There may be systematic reviews or modelling studies that you could contribute to, or you could look for existing datasets that could be reanalysed through a welfare bio ‘lens’.
In general I think it’s much easier to go from comp-sci/ maths/something quantitative to bio than the other way around, as bio is not particularly ‘linear’ (i.e. there isn’t necessarily a base of knowledge that everyone has and builds on over time).
Thanks a lot for this response. I would probably donate through EA funds, so yes that should work. It seems like doing that with GiftAid will be a better bet than GAYE in my case then. The tip about HMRC is really useful to know—I have a friend who is giving regularly through GAYE and paying the 4% fee who is in a higher tax bracket, so I’ve recommended that he try this instead.
The Precipice by Toby Ord would be high up on my list. It is accessible and covers a lot of ground, illustrating a diversity of possible career paths and study areas that are relevant to existential risk.
Thanks a lot for sharing this. I need to update the post to add this and other research that has been pointed out to me.