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Historically, it's correct-but-too-vague to say computers were for "solving math problems". Historic computer problems should be divided into two types: business problems and scientific/engineering problems. Business problems include things like tabulation and accounting. Programmable digital computers go back at least as far as UNIVAC I, in 1951 (using programmable digital computers for science doesn't go back THAT MUCH farther).

Prior to the IBM/360 (1964), mainframes sold for business purposes generally had no support for floating point arithmetic. They used fixed-point arithmetic. At the hardware level I think this is just integer math (I think?), but at a compiler level you can have different data types which are seen to be fractions with fixed accuracy. I believe I've read that COBOL had this feature since I-don't-know-how-far-back.

This sort of software fixed-point is still standard in SQL and many other places. Some languages, and many application-specific frameworks, have pre-existing fixed-point support. So it's also not accurate to say that you necessarily need to roll your own, though certainly in some contexts you'll need to.

And for money, you very much do not want arbitrary rational numbers. The important thing with money is that results are predictable and not fudgable. The problem with .1 + .2 != .3 is not that anyone cares about 4E-17 dollars, it's that they freak out when the math isn't predictable. Using rationals might be more predictable than using floats, but fixed-point is better still. And that's fixed-point base-10, because it's what your customers use when they check your work.

Agree that rational isn't it. But "reproducing the existing quirks" seems like an accurate description. If you want to pay 7% APR on month-end balances, then that's a real-number calculation, but to match what customers expect you need in addition to specify when to round off to cents.

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