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.

翻译：本文探讨了大数据和机器学习在社会中的普遍存在，并提出这凸显了进一步研究其根本局限性的必要性。我们将“信息过多往往表现得像信息过少”这一现象，推广至关于类律宇宙的形式化知识以及任意可计算生成的数据集集合。由此引出简单性泡沫问题，即一个配备形式化理论的学习算法可能被数据集欺骗，从而找到其视为全局最优的局部最优模型。在类律（可计算）宇宙和形式化学习系统的背景下，我们证明存在一个上限，一旦随机生成的数据相对于其联合复杂性足够庞大，形式化知识便无法进一步降低不祥发现的概率。我们还论证，这是一种认识论上的局限，可能在数字中介社会中引发不可预测的问题。