Safety can be dangerous

In 2005, Hurricane Rita caused 111 deaths. 3 deaths were caused by the hurricane. 90 were caused by the mass evacuation. ... Exercise for the reader: Find other cases where cautionary measures are more dangerous than nothing.

The ratio of 90 deaths from the evacuation to 3 deaths from the hurricane looks bad, but is in fact irrelevant. The proper comparison would be 90 deaths from evacuating, to X deaths that would have resulted had those people stayed put, or performed some other action in preparation. While it's possible a proper estimate of the risk of staying near the hurricane's projected path would show that it was the less dangerous course, the abstract you linked doesn't suggest that is a topic covered by the paper.

Also, this is technically not correct:

The FDA is supposed to approve new drugs and procedures if the expected benefits outweigh the expected costs. If they actually did this, the number of people saved by new drugs would be roughly equal to the number killed by them

Actually, if the FDA really did this the marginal -- in this case, most-dangerous -- drug approved should kill as many people as it save. But since every drug before that would save more people as it killed, on net there should be more people saved than killed.

[libertarian alert]

I'm not sure the drug example is a safety problems per se, it looks more like an incentive problem to me.

If an FDA official approves a bad drug that kills 1000 people/year, he probably gets canned. If he rejects a good drug that would have saved 1000 lives/year...well, no one including him will actually know how many lives it would have saved, and he will take his paycheck home and sleep soundly at night.

Can you come up with an example that doesn't involve government?

Gerd Gigerenzer estimates that there were 1,600 excess road fatalities due to increased driving post 9/11.

Good point: we tend to act as if the worst-case scenario were a given, without regard for the relatively foreseeable negative consequences to safety measures.

This is more of a tradeoff of time and money than lives saved, but see our continued insistence that nobody can get on an airplane without taking their shoes off. It's a policy that has almost certainly done no good and never stood a serious chance of doing any good. Here a political scientist explains why it's very hard to walk back stupid policies like that even when virtually everyone agrees they should be walked back: in most cases, someone's got to take the initiative to walk it back, and in the event of a very-unlikely disaster, voters will disproportionately blame the walker-back, but will not blame him/her for the inconvenience they experience while the policy remains in place. There's no incentive to do the obviously intelligent thing.

All this said, in order to fully argue for the Rita example, you'd have to show that the people who ordered the evacuation were wrong to do so. Perhaps many more than 90 lives would have been lost had the 2.5 million evacuees remained in place. Perhaps they would not have, but the best available damage forecasting indicated that they probably would be. As the linked report says, the hurricane narrowly missed major population centers. If the best available forecasting indicated that there was a 10% chance that it would strike those population centers, and if it did there was a 90% chance that 2000 people would die, whereas if the evacuation were ordered there was a 90% chance that 100 people would die in the process of evacuating, then the evacuation was the right call. (Numbers pulled out of my ass, obviously, but just illustrating that even in the case of a seemingly "anti-safety" scenario like Hurricane Rita, it's hard to figure out, even in retrospect, what course of action is best.)

There are certainly a lot of cost-benefit considerations biased towards excessive safety, but what I find even more fascinating is the family of effects that are know under the monikers of Tullock effects, Peltzman effects, risk compensation, and risk homeostasis. The basic idea underlying all these is that regulations and interventions that forcibly lower certain risks motivate people to compensate by adopting other risky behaviors, so that the overall level of risk remains the same (though possibly redistributed), or even gets worse due to biases or externalities. A classic example is when safer cars lead to more reckless driving.

Where I live, there's a busy road with two car lanes and a bike lane in each direction. A while ago, the city government decided that the bike lane was too narrow, and for the safety of the cyclists, they moved the lane markers so as to widen the bike lane and make the car lanes narrower. A few days after this change, I saw a happy couple riding on the widened bike lane in parallel.

A somewhat different example (though one that appears to be based on a fairly limited amount of data):

Speed bumps slow down emergency vehicles, and that apparently costs more lives than are saved by reduced traffic collisions.

What if you have two drugs, one that saves 1000 lives per year, and the other that costs 1000 per year? I think you're saying that it's a wash whether you approve both or ban both. But if you approve both and eventually ban the deadly drug, it's a net win. The expected value of approving a drug is higher than the expected value of using it indefinitely. (I see that you address this in the comments. But I don't think this is "an additional complication" but a huge bias to this methodology.)

The continual push for "higher standards" leads to fewer drug approvals. It is hard to assess the quality of the marginally rejected drugs, but the other effect is to delay approval. It is easy to retrospectively measure the effect of delay. Gieringer showed that in the 50s, 60s, and 70s when the US and Europe had different standards, the lives lost to delays on one side of the Atlantic swamped the lives lost to drugs that were eventually banned. That's all drugs that were eventually banned, not just drugs that were not approved because of "higher standards." In fact, the most famous differential rejection, thalidomide, was rejected on a US whim, when the US generally had laxer standards at the time.

References:

• The risks of caution - Tim Tyler;

• The Risk of Excessive Caution - John C. Downen

• The Perils of Precaution - Max More

/waves

this is only relevant if you can give us data on the drugs that would kill thousands that the fda prevents from being marketed.

Possibly I shouldn't have mentioned the FDA. The Rita data is stronger and more interesting; but all the comments are on the FDA paragraph.

Are clinical trials submitted to the FDA/TGA/ETGA registered before the trials commence? I know that is best practice, but I am unclear whether it is regulated. If it is not, could there be a business in selling 'pre-registration accreditation', for pharmaceutical and medical device manufacturers to market their products for a higher standard of product?

On the note of software branching:

Over here in Google, we constantly build everything from head, to the point that every build invocation is essentially a dramatically optimized (and distributed, and limited to just transitive dependencies of your current target, but still..) "make world". Pretty much everything is in a single repository.

As a result, we occasionally have to deal with other people breaking out code. That happens.. maybe once every few months. And we don't need to manage divergent versions, or forget which branches we'd applied some patch to.

I'm pretty sure the non-branching workflow can only be called a stunning success.

Are there separate neural systems for risk-avoidance and award-seeking?