It's an intuition you build pretty early on in science courses in undergrad, somewhat related to Occam's Razor. It's why the optimal packing of 11 squares[1] is so unsatisfying. It flies in the face of all the intuition we've built that simple (and symmetric) solutions are more likely to be true. Machine learning practitioners use this intuition when doing model selection as well: the simpler model (i.e. with fewer free parameters) is less likely to be overfit to the data.

[1] https://en.wikipedia.org/wiki/Square_packing#/media/File:Pac...