  In this article, we discuss the ubiquity of Big Data and machine learning in
society and propose that it evinces the need of further investigation of their
fundamental limitations. We extend the "too much information tends to behave
like very little information" phenomenon to formal knowledge about lawlike
universes and arbitrary collections of computably generated datasets. This
gives rise to the simplicity bubble problem, which refers to a learning
algorithm equipped with a formal theory that can be deceived by a dataset to
find a locally optimal model which it deems to be the global one. In the
context of lawlike (computable) universes and formal learning systems, we show
that there is a ceiling above which formal knowledge cannot further decrease
the probability of zemblanitous findings, should the randomly generated data
made available to the formal learning system be sufficiently large in
comparison to their joint complexity. We also argue that this is an
epistemological limitation that may generate unpredictable problems in
digitally intermediated societies.
