It's Goodhart's Law in action, this time with deadly consequences:
"When a measure becomes a target, it ceases to be a good measure."[a]
"When a measure becomes a target, it ceases to be a good measure."[a]
Of course some people would just say the issue was solved, falsely (which, you know, it was a little easier to get caught so it was higher-risk, in addition to being probably less moral), and there was also the X factor of how much you liked the customer, considering the extreme amount of verbal abuse the job entailed.
If someone could get away with it at a 911 call center, it could happen anywhere... and might happen oftener than we think.
[1] - https://www.sfgate.com/news/article/911-dispatcher-sentenced...
In general its, simply a bad idea to be rude to the person you expect to provide assistance.
I'm reminded of https://xkcd.com/810/
It is far better when call length is not a KPI. I would never go back.
IMO, if call length is a KPI, it's a key indicator that the company isn't willing to hire enough agents and is trying to apply pressure to shorten calls for purposes of coverage.
If I recall correctly, Munger considers incentives to be the single most important concept to properly understand in order to drive successful business (and arguably life) outcomes.
Not surprisingly, incentives are also chronically underestimated or outright ignored, even in situations where there's a strong, profit-driven incentive (#meta) to get them right.
> From all business, my favorite case on incentives is Federal Express. The heart and soul of their system – which creates the integrity of the product – is having all their airplanes come to one place in the middle of the night and shift all the packages from plane to plane. If there are delays, the whole operation can’t deliver a product full of integrity to Federal Express customers. And it was always screwed up. They could never get it done on time. They tried everything – moral suasion, threats, you name it. And nothing worked. Finally, somebody got the idea to pay all these people not so much an hour, but so much a shift and when it’s all done, they can all go home. Well, their problems cleared up overnight.
It's about extrinsic vs intrinsic motivation. Extrinsic motivation comes from outside of the person and is what's typically used by organizations to try to motivate people: salary, praise, bonuses, etc. Intrinsic motivation comes from within.
[1] - https://www.amazon.com/Punished-Rewards-Trouble-Incentive-Pr...
1) cost as little as possible to the business
2) "optimize" their own pay/career
(There was a study saying that over 80% of sales people would deny their employer a million-dollar contract if it netted them $500 personally, so compared to that this is nothing)
Which makes sure that the incentives are not aligned with the business goals.
Now I guarantee that the best way to destroy intrinsic motivation, bar none, is to provide extrinsic motivation for things that aren't aligned with the business' goals.
So yes, could.
In this case they should have had a metric that took into account the estimated prior probability of a good outcome given an ordinary surgeon.
Survival is the intended effect, but that's much more of a binary thing.
Yeah, if you were given one operation where the patient has a 100% chance of dying without it, but 80% of you killing them if you operate + 20% chance of them living after, maybe you'd do this once and get lucky. But try doing this twice, you have a a 96% chance of killing a patient, three times makes a 99.2% chance of killing at least a single patient. This probability quickly approaches 100%. If you kill a patient you get dragged into court, branded as a murderer by the opposing lawyer, and your career which you have spent your youth, your 20s and a half a million dollars training for is down the drain. Taking risky cases, if that's not your niche, is is guaranteed to have you out of a job and in debt from legal fees.
To suggest that "bad hearts" are involved in this necessary calculation, is incredibly naive.
But aside from that, I completely disagree with your comment. The approach you are defending, even if legal, is completely unethical and immoral. It should be something we chastise, not condone.
The subtext is that the system won't improve; rather, something in the system will be sacrificed to improve the metric. The end result (usually quality) won't improve.
https://www.lesserwrong.com/posts/EbFABnst8LsidYs5Y/goodhart...
Not sure how much stock I can put into the rest of the article given this. Goodhart's Law can seemingly only apply if the variables are highly non-normal, or are negatively correlated (e.g. worse doctors are more likely to refuse the difficult operations than good doctors).
I don't think your statements contradict anything that it says. For reference, here are all the sentences in the "Quick Reference" part, which I'm assuming is the part you read.
Regressional Goodhart - When selecting for a proxy measure, you select not only for the true goal, but also for the difference between the proxy and the goal.
Model: When U is equal to V+X, where X is some noise, a point with a large U value will likely have a large V value, but also a large X value.
Thus, when U is large, you can expect V to be predictably smaller than U.
Example: height is correlated with basketball ability, and does actually directly help, but the best player is only 6'3", and a random 7' person in their 20s would probably not be as good
Is any particular one of these sentences false?
> If U measures V plus some noise X, assuming V and X form a bivariate normal distribution, then the conditional expectation of V is maximized by selecting the greatest U.
The word "maximized" carries some assumptions. If the only thing you can do is select based on U, then that statement is correct. However, if you had some means of selecting directly on V, then it's extremely likely that this would do better than selecting on U.
Suppose you're selecting the top 10 people. Suppose X ranges 0-10 chosen by a die roll, and V ranges 0-10, and it happens there are ten people with V=10, a hundred people with V=9, and a thousand with V=8 (and a lot more with lower Vs). On average, you'll have ten V=9s who score U=19, ten V=9s and a hundred V=8s who score U=18, and so on; in order for selecting on U to perform as well as selecting on V, every one of the V=10s would have to roll X=9 or X=10, which is exceedingly unlikely. It is true that taking people with high U scores yields people with higher Vs than taking people at random, and it is further true that taking the U=19s will give you better results than taking the U=18s or U=12s. It is simultaneously true that, when you take the U=19s, you'll be getting people whose X was 9 or 10, much higher than if you selected people at random or if you selected directly for high V. The first two sentences from the text state exactly this. (One consequence of this observation is, e.g., if you're doing admissions based on some test score, and you're considering raising the required score by ∆U, you should know the effect will be to raise average V and to raise average X, with ∆V=∆U-∆X, and if ∆X is large, you may be disappointed in the results. This is simple regression to the mean.)
I imagine you know all these concepts; I think you're interpreting the text as a stronger statement than it is.
For example, if I target a high win percentage, is win percentage, then, not a good measure?
Mortality rates are a pretty good measure for surgeons.
The problem here is that they are inflating their measure by cherry picking.
edit:
Some measures should be targeted, the problem here is that it is done in an non genuine way
That's what the article is describing, surgeons won't even play the game, they won't even attempt to help certain people because it might hurt their "win percentage".
Mortality rates are one measure of many that can tell a story, but they're like incarceration rates in that they've become a perverse incentive that exacerbates an issue. If we say that putting more people in prison is a good thing, that's predicated on an assumption that only those that are being put in prisons are people worthy of being there, whose freedom is more costly to society than their imprisonment. When a district is rewarded for putting people in prisons, what types of things might you expect to happen to the legal systems and populations in those places? Would prosecutors then be incentivized to trump up charges and force people into serving prison time unnecessarily to make themselves look like bigger "winners"? Might police make more frivolous arrests or even stir up trouble in communities to paint a picture of rampant criminality so they can look to be "tough on crime"? Wouldn't you expect to see an all-causes decrease in long term crime?
The problem isn't just cherry picking, but if people can game a system built around limited and gameable measures, then it's going to encourage min-maxing for profit.
also, normalizing for the patient risk will likely just lead to people overstating patient risk, because once you reduce performance to an index, people will focus on what's measured before on what the intended goal for the measurement is.
(And besides that, although software estimation is notoriously inaccurate, it is still done and written into contracts all the time, because it serves a useful purpose that outweighs its inaccuracy)
For surgeons, the direct target is for the patient to get better - or at least live longer and with higher quality of life. Proxy is the measure of a mortality of a specific surgeon. It is not a direct target - as the surgeon can achieve zero mortality by not doing any surgery at all, but his patients would die of suffer from the lack of treatment.
I disagree with the downvotes your comment's received.
>Some measures should be targeted, the problem here is that it is done in an non genuine way
The "not in a genuine way" is exactly what he's talking about.
I don't want to be operated on some yahoo surgeon who unconditionally operates regardless of risk and has a high mortality track record as a result.
^ You're looking at this.
But on the other hand, I don't want to be treated by a doctor who cares more about his sellable stats than saving lives.
Essentially, the doctor has decided that he doesn't want to take the risk of killing you, even if you're willing to accept it. Why should that be the doctor's choice to make, and why is he being given this additional risk, when the benefit/loss should clearly be yours?
Your comment also shows that patients are just as much of an issue by focusing on the metric more than the cases involved.