Should effective altruists care about the US gov't shutdown and can we do anything?

Ishaan

For those who haven't heard, NIH and NSF are no longer processing grants, leading to many negative downstream effects.

I've been directing my attention elsewhere lately and don't have anything informative to say about this. However, my uninformed intuition is that people who care about effective altruism (research in general, infrastructure development, X-risk mitigation, life-extension...basically everything, actually) or have transhumanist leanings should be very concerned.

The consequences have already been pretty disastrous. To provide just one, immediate example, the article says that the Center for Disease Control and Prevention has shut down. I think that this is almost certain to directly cause a nontrivial number of deaths. Each additional day that this continues could have huge negative impact down the line, perhaps delaying some key future discoveries by years. This event *might* be a small window of opportunity to prevent a lot of harm very cheaply.

So the question is:

1) Can we do anything to remedy the situation?

2) If so, is it worth doing it? (Opportunity costs, etc)

Either the p-values in the papers are worthless in the sense of not reflecting the probability that the observed effect is real

p-values do not reflect the probability that the observed effect is real but the inverse, and no one has ever claimed that, so we can safely dismiss this entire line of thought.

Or the p-values, while not perfect, do reflect the probability the effect is real

p-values can, with some assumptions and choices, be used to calculate other things like positive predictive value/PPV, which are more meaningful. However, the issue still stands. Suppose a field's studies have a PPV of 20%. Is this good or bad? I don't know - it depends on the uses you intend to put it to and the loss function on the results.

Maybe it would be helpful if I put it in Bayesian terms where the terms are more meaningful & easier to understand. Suppose an experiment turns in a posterior with 80% of the distribution >0. Subsequent experiments or additional data collection will agree with and 'replicate' this result the obvious amount.

Now, was this experiment 'underpowered' (it collected too little data and is bad) or 'overpowered' (too much and inefficient/unethical) or just right? Was this field too tolerant of shoddy research practices in producing that result?

Well, if the associated loss function has a high penalty on true values being <0 (because the cancer drugs have nasty side-effects and are expensive and only somewhat improve on the other drugs) then it was probably underpowered; if it has a small loss function (because it was a website A/B test and you lose little if it was a worse variant) then it was probably overpowered because you spent more traffic/samples than you had to to choose a variant.

The 'replication crises' are a 'crisis' in part because people are basing meaningful decisions on the results to an extent that cannot be justified if one were to explicitly go through a Bayesian & decision theory analysis with informative data. eg pharmacorps probably should not be spending millions of dollars to buy and do preliminary trials on research which is not much distinguishable from noise, as they have learned to their intense frustration & financial cost, to say nothing of diet research. If the results did not matter to anyone, then it would not be a big deal if the PPV were 5% rather than 50%: the researchers would cope, and other people would not make costly suboptimal decisions.

There is no single replication rate which is ideal for cancer trials and GWASes and individual differences psychology research and taxonomy and ecology and schizophrenia trials and...