I work on computational phylogenetics. A practical problem is that while we have decent models of many things, gene transfer, hybridization, etc., practical met

I work on computational phylogenetics. A practical problem is that while we have decent models of many things, gene transfer, hybridization, etc., practical methods tend to focus on the simpler models. I recall that a popular reason why phylogenetic trees are AFAIK more popular than phylogenetic networks (despite networks being more powerful) is because simpler models lead to more effective algorithms that do well under the oversimplified-but-probably-good-enough models. Complicated models are much more intractable (well, simple models usually lead to NP-hard problems. Complicated models are... even more hairy). Also theory is much easier understood on simpler models. Just like big O notation abstracts over many things, simple models are much easier to reason about. There is a time and place for considering the memory hierarchy, how floating point division is really not the same level of speed as integer addition, but inferring the canine phylogeny under some easy-to-reason-about model of evolution is much simpler (both theoretically and practically), especially if you have already gathered data for five or six years and people are getting tired of no results.