Harré Michael S
Complex Systems Research Group, Faculty of Engineering, The University of Sydney, Sydney 2006, Australia.
Entropy (Basel). 2022 Jan 28;24(2):210. doi: 10.3390/e24020210.
Information theory is a well-established method for the study of many phenomena and more than 70 years after Claude Shannon first described it in A Mathematical Theory of Communication it has been extended well beyond Shannon's initial vision. It is now an interdisciplinary tool that is used from 'causal' information flow to inferring complex computational processes and it is common to see it play an important role in fields as diverse as neuroscience, artificial intelligence, quantum mechanics, and astrophysics. In this article, I provide a selective review of a specific aspect of information theory that has received less attention than many of the others: as a tool for understanding, modelling, and detecting non-linear phenomena in finance and economics. Although some progress has been made in this area, it is still an under-developed area that I argue has considerable scope for further development.
信息论是一种研究众多现象的成熟方法，在克劳德·香农首次在《通信的数学理论》中描述它70多年后，它已远远超出了香农最初的设想。如今，它是一种跨学科工具，用于从“因果”信息流推断复杂的计算过程，并且在神经科学、人工智能、量子力学和天体物理学等众多不同领域发挥重要作用已很常见。在本文中，我对信息论的一个特定方面进行了选择性回顾，该方面比其他许多方面受到的关注更少：作为理解、建模和检测金融与经济中的非线性现象的工具。尽管在这一领域已经取得了一些进展，但它仍然是一个欠发达的领域，我认为该领域有相当大的进一步发展空间。
