The fact that there is a lot of emotional/inept-philosophical baggage to the word does not mean it is irrational to use it.
If your goal is to engage another person in clear, careful, rational discussion, then it is not rational to use terminology that you know to have “a lot of emotional/inept-philosophical baggage”, because to do so would be counter-productive with respect to your goal. I assume that the purpose of a blog called “Overcoming Bias” is to engage in clear, careful, rational discussion.
(The other part of the “experimental evidence” comes from statisticians / computer scientists / Artificial Intelligence researchers, testing which definitions of “simplicity” let them construct computer programs that do empirically well at predicting future data from past data. Probably the Minimum Message Length paradigm has proven most productive here, because it is a very adaptable way to think about real-world problems.)
I once believed that simplicity is the key to induction (it was the topic of my PhD thesis), but I no longer believe this. I think most researchers in machine learning have come to the same conclusion. Here are some problems with the idea that simplicity is a guide to truth:
(1) Solomonoff/Gold/Chaitin complexity is not computable in any reasonable amount of time.
(2) The Minimum Message Length depends entirely on how a situation is represented. Different representations lead to radically different MML complexity measures. This is a general problem with any attempt to measure simplicity. How do you justify your choice of representation? For any two hypotheses, A and B, it is possible to find a representation X such that complexity(A) < complexity(B) and another representation Y such that complexity(A) > complexity(B).
(3) Simplicity is merely one type of bias. The No Free Lunch theorems show that there is no a prior reason to prefer one type of bias over another. Therefore there is nothing special about a bias towards simplicity. A bias towards complexity is equally valid a priori.
http://www.jair.org/papers/paper228.html http://en.wikipedia.org/wiki/No_free_lunch_in_search_and_optimization http://en.wikipedia.org/wiki/Inductive_bias