In practice, I often use simple models–because they are less effort to fit and, especially, to understand. But I don’t kid myself that they’re better than more complicated efforts!
Parsimony is a prior, not an end goal. At least, that’s how it’s used in Solomonoff induction.
The reason the Solomonoff prior doesn’t apply to social sciences is because knowing the area of applicability gives you more information. Once you take that into account, as well as the fact that you don’t have the input data or computational power to recompute the cumulative process that spit humans out so the simple low level theories are out of reach, your prior is skewed towards more complex models.
Parsimony is a prior, not an end goal. At least, that’s how it’s used in Solomonoff induction.
The reason the Solomonoff prior doesn’t apply to social sciences is because knowing the area of applicability gives you more information. Once you take that into account, as well as the fact that you don’t have the input data or computational power to recompute the cumulative process that spit humans out so the simple low level theories are out of reach, your prior is skewed towards more complex models.
That doesn’t mean it doesn’t apply! “Knowing the area of applicability” is just some information you can update on after starting with a prior.
The Solomonoff prior doesn’t really apply to any kind of science, and it’s not even part of mainstream Bayesian statistics.