I generally agree. The whole point of insurance is to pool risk -- so cutting the population into ever-smaller pools seems missing the point. In the infinite limit we'd get seven billion single-person pools, each of which has that person pay in the exact same amount of money they get back out.
This is also known as "not having insurance".
The point of insurance is to replace a high variance distribution of potential losses with a low variance distribution, but with a similar expected loss. In other words, on average you pay a bit more, but you never pay a huge amount.
Now, in a perfect world, everyone would pay exactly what their expected loss is, plus a small premium. Since it's impossible to measure the expected loss exactly, in reality some people pay a bit more and some pay a bit less. Getting a better risk estimate doesn't get you closer to "not having insurance", but rather closer to "perfect insurance" where everyone gets a distribution that exactly matches their risk profile.
I still think this form of "perfect insurance" is bad - part of what I like about the insurance is exactly the dispersion of cost in the larger population. Should a chronically-ill person pay a very high insurance, or should the society cover the costs? I'm in the second camp, even though I'm not chronically-ill. It's part of the benefits of having an actual society - we don't need to stand alone.
The latter are necessarily the government's remit, and the political discussion on whether they are desirable can be had in an informed manner if costs are clear and they are not commingled with insurance elements.
what we call "health insurance" is really just a payment planning and collective bargaining service rolled into one. it doesn't really work like most other kinds of insurance.
The perfect insurance would know how likely it is that you are going to have to pay a catastrophically large amount, and would charge you amount*probability. If probability=1.0, then sure, they should charge you the whole amount.
As the data gathered increases, the probability eventually reaches 1.0. For instance, if an insurance company could legally gather all telemetry from your car at a high enough frequency and cancel your policy at will, then it could determine that your car was on an unavoidable collision course and eliminate losses.
This may sound like a silly thought experiment, but I think it demonstrates that in principle, there must be some point at which we draw the line on discrimination to have insurance, so if you find yourself defending all discrimination then you've gone wrong somewhere. Either there's a principle that has been missed, or it is valid to just set the rules in society that people are comfortable with.
There are, however, situations where the model is a reasonable fit - notably, some aspects of health care, where the approach to the limit proceeds over multiple billing periods. A society can choose to spread the cost around, but to do this under the guise of insurance leads to the sort of incongruities and inefficiencies that are seen in the US system.
Being prescient about getting the number 4 when you throw a dice means you can assign probability 1 to getting 4 when you throw that dice.
Having an accurate risk profile means knowing the odds you'll get 4 with that dice might be 1/6.4 because you precisely measured the shape and weight distribution of the dice and you can see how biased it is.
Let's assign a cost X for the unlucky event of rolling a 4. You're expected loss is X∙1/6.4. If the event only happens once, obviously you either pay full cost X or you don't pay anything.
If you pool with a large number (N) of people, each throwing once that same dice, then the expected loss of the pool is N∙X∙1/6.4, that is, if each member of the pool contributes their share, the pool is going to be able to pay off the individual losses. Unlucky people are better off because they saved X∙(1-1/6.4); lucky people are worse off because they had to pay X∙1/6.4. Pool members agree on those rules, because they don't know if they are going to win or lose, and their contribution to the pool is smaller than the worst case scenario.
So far so good. I deliberately used the "custom" probability number 1/6.4 to show how arbitrary that is.
Other people might have other dice, each biased their own way.
The expected loss for the pool would thus be X∙(P₁+P₂+P₃+....Pn).
Each person contributes to the pool backing proportionally to the contribution to the pooled expected loss, thus the system still makes sense even on the case of 1-sized buckets.
(Disclaimer: I have no idea what I'm talking about, this is not my field, just an average person with everyday math skills. Happy to learn why I'm wrong, in case I am)
(EDIT: formatting and typos)
There isn’t likely a perfect loss prediction to be had, e.g. predicting and understanding risk itself influences the likelihood of avoiding a loss favorably. An ideal scenario might be that people become aware of risky behavior then adjust and therefore change the future prediction. Health insurers do it all the time. Helping at risk customers avoid large losses actually helps the customer and the rest of the less risky customers in the pool (everyone’s premium should go down if the risky become less risky).
You say "Quite the opposite", and proceed to rephrase the same thing as GP said, except for the conclusion, which also is the same thing, yet you claim it isn't.
Not having insurance IS "perfect insurance", in that you pay exactly your own costs, with the added benefit of having no overhead. Most of us don't want that though, in the same way as we don't want children to have to pay their own healthcare, or chronically debilitated people to die to darwinism (whether we succeed here is another point). But a perfect "risk estimate" also happens to be the same thing as prescience, and if we had that we wouldn't need insurance for other reasons.
Not having insurance = paying for yourself = distribution by exact risk profile.
Not having insurance means losses follow your exact risk distribution, with a non-zero risk of a catastrophic loss. Having a perfect insurance means your losses follow a distribution with the same average as your risk profile suggests (+premium), but much lower variance and with zero density on the higher end, meaning you never have a catastrophic loss.
Quite the contrary.
> average
And that right there is where you are hiding all your assumptions. There is no such thing as "your risk profile", there is only your risk profile according to some risk model, which approaches what actually will happen to you individually the more detailed you make it and the better your predictive capabilities get, because as you do that, you are shrinking the pool of individuals that this average is calculcated over, approaching only you as one individual in the limit, at which point the average is the same as the actual thing.
> and with zero density on the higher end, meaning you never have a catastrophic loss.
Also, that's not a thing.
No, that is a fundamental misunderstanding of what odds are. Odds are not a property of a system, odds are a description of your knowledge about that system. There is no such thing as "odds of the system", the odds for the same system can be vastly different for different observers.
If you know how to control the coin, then the odds for you are almost 1:0 for your intended face. For the person next to you who doesn't know you, it's probably 0.5:0.5. For the person that has watched you for a while, it might be 0.6:0.4, if that's the ratio of the faces that you flip (intentionally). All for the exact same system at the exact same moment.
Also, a binary distinction between "having no clue" and "perfect predictions" is not useful. While perfect predictions are the limit of an ever refined model, there are many steps in between a useless model that gives random predictions and a model that makes no mistakes whatsoever.
To me, the aspect of redistribution is important in insurance. The acceptance that the world is chaotic and we can not predict all the bad luck that might happen to us. Hence, I pay for yours bad luck, if you pay for mine.
That is the same thing if you have produced a perfect risk profile. The perfect risk profile completely predicts reality, and all costs can be accounted for. If it doesn't completely predict reality, it's not perfect.
So with the perfect risk profile, insurance is a zero sum game. Instead of paying a small premium you can put your money in a bank account and instead have them pay you "a small premium" in the form of interest.
And no, I don't believe that there can ever be a perfect risk profile, but as risk assessment gets more fine grained, insurance approaches that zero sum game.
The utility of insurance is to reduce variance, it is not a zero-sum game.
Perhaps you understand the word "perfect" in a different sense than I do. A better risk estimate to me is (to me) one that is more accurate. A perfect risk estimate is one that can not be more accurate. If a better risk estimate brings you closer to perfect insurance (which the GP concludes), a perfect risk estimate should leave you as close to perfect insurance as is possible.
It would be cool if you could address my reasoning instead because now I have no idea what exactly you disagree with other than the conclusion.
Let's say you have a house worth $100,000 and statistically it burns down once in 1000 years. This means that the expected damage is $100 per year, but the variance is very high because once the house burns down, the damage is 1000 times greater than the expected damage. With insurance, however, the variance is zero because you pay the same amount every year, whether your house burns down that year or not.
I would argue that with a perfect insurance you would only pay the expected loss, i.e. $100 per year, and not more or less, because ideally the insurance has accurately assessed the expected loss and diversified the variance away. However, with less ideal risk assessment, you could pay more or less because other houses burn more or less often.
If the intent described were truly the case, explain Medicare and Medicaid.
The clean way to do it is to separate insurance (can be very efficient if privately provided) from redistribution (will lead to bad distortion unless done by the state).
You seem to be saying insurance companies must discriminate unfairly by saying that if insurance companies discriminate unfairly, they will lose customers.
Say I am male, and I pay more for car insurance because of that. Your argument is that if sex discrimination is illegal, then the market will collapse because women will stop buying insurance. But isn't it equally true that men who are low risk should "evaporate from the pool" in the present system? There doesn't seem to me a fundamental distinction between these two examples of discrimination.
Now, there may be a quantitative difference, a difference in economic impact, but if it's only quantitative, then why should one automatically be manageable and the other not, which is the assumption I think people implicitly make.
It's not actually a value judgement I'm giving. Whether it's unfair as in sex or race based is not part of the incentive mechanism. The mechanism still exists though. If you find out people of one color are bigger risks than another you have an awkward problem.
If you can restrict the companies from doing this you fix the problem, but of course there's games to be played regarding selection, as another commenter notes.
I'm using "unfair" to mean anything someone could call unfair, that is, charging someone as though they are a higher risk than they are, due to some factor.
The point is, it's illogical to say we can't* misprice risk based on ethics or social justice or whatever, when you assume we can misprice risk based on incomplete knowledge. It seems like special pleading.
*can't in the sense of it being a mortal threat to the industry in general