This saying resonates deeply with me. It was first introduced by a statistician, and as a mathematician, I have built exactly the same kind of models. And indeed, they were all wrong. You cannot predict down to the second when people will order a meal. But knowing that between 5:00 PM and 7:00 PM there will likely be 30 to 50 orders, and between 7:00 PM and 9:00 PM there will likely be 20 to 30, is incredibly useful.
In this example, it is quite clear why the model is wrong. We are dealing with probabilities and uncertainties. The same applies to physics models. A model is often a simplified representation of reality. If air resistance accounts for only 0.01% of an equation and falls within the margin of error, it makes sense to leave it out. But does this also apply to Blueriq models?
At Blueriq, we model rules and policies. This is not about randomness or nature—it is a model of something that has already been designed by people. So a Blueriq model should simply follow the rules perfectly, right? Yes, a Blueriq model follows rules and policies exactly as they are meant to be interpreted. So have I found the exception? Is it possible that we have a model that is not wrong?
No, even Blueriq models are wrong. Blueriq models are inherently flawed because policies themselves are inherently flawed. You must meet certain requirements to apply for a mortgage because we want to ensure that you can afford it. Yet, there are still people who cannot pay their mortgage. You can only receive a subsidy if you meet the eligibility criteria, but that does not necessarily mean you truly need it. And if you do not qualify for a subsidy, that does not mean you do not need it.
So if a Blueriq model is wrong, is it still useful? Absolutely! Thanks to our solutions, millions of Dutch citizens know exactly what information is needed to automatically process their applications for mortgages, insurance, subsidies, and more. This saves knowledge workers valuable time in handling these applications. And these savings far outweigh the occasional mistake in determining who can afford a mortgage.
Still, we do not like being wrong about important life decisions. That is why our solutions always empower a knowledge worker to make the final decision. This way, they can decide to grant you a mortgage even if you are temporarily unemployed—because you already have three job offers lined up. Of course, this happens within the frameworks that allow it, including four-eye checks where necessary. This way, we correct our mistakes and ensure both efficiency where possible and humanity where needed.
