Zenil Hector
Algorithmic Dynamics Lab, Karolinska Institute, 171 77 Stockholm, Sweden.
Oxford Immune Algorithmics, Reading RG1 3EU, UK.
Entropy (Basel). 2020 May 30;22(6):612. doi: 10.3390/e22060612.
Some established and also novel techniques in the field of applications of algorithmic (Kolmogorov) complexity currently co-exist for the first time and are here reviewed, ranging from dominant ones such as statistical lossless compression to newer approaches that advance, complement and also pose new challenges and may exhibit their own limitations. Evidence suggesting that these different methods complement each other for different regimes is presented and despite their many challenges, some of these methods can be better motivated by and better grounded in the principles of algorithmic information theory. It will be explained how different approaches to algorithmic complexity can explore the relaxation of different necessary and sufficient conditions in their pursuit of numerical applicability, with some of these approaches entailing greater risks than others in exchange for greater relevance. We conclude with a discussion of possible directions that may or should be taken into consideration to advance the field and encourage methodological innovation, but more importantly, to contribute to scientific discovery. This paper also serves as a rebuttal of claims made in a previously published minireview by another author, and offers an alternative account.
算法（柯尔莫哥洛夫）复杂度应用领域中的一些既定技术和新颖技术目前首次同时存在，在此进行综述，范围从统计无损压缩等主流技术到推进、补充并带来新挑战且可能存在自身局限性的更新方法。文中给出了证据表明这些不同方法在不同情况下相互补充，尽管存在诸多挑战，但其中一些方法能更好地基于算法信息论的原理并以此为动机。将解释不同的算法复杂度方法如何在追求数值适用性时探索不同充要条件的放宽情况，其中一些方法比其他方法承担更大风险以换取更高的相关性。我们最后讨论了推进该领域、鼓励方法创新，更重要的是为科学发现做出贡献可能或应该考虑的方向。本文也是对另一位作者先前发表的一篇小型综述中提出的观点的反驳，并提供了另一种解释。
