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Abstract：
The formulation of the Partial Information Decomposition (PID) framework by Williams and Beer in 2010 attracted a significant amount of attention to the problem of defining redundant (or shared), unique and synergistic (or complementary) components of mutual information that a set of source variables provides about a target. This attention resulted in a number of measures proposed to capture these concepts, theoretical investigations into such measures, and applications to empirical data (in particular to datasets from neuroscience). In this Special Issue on \"Information Decomposition of Target Effects from Multi-Source Interactions\" at Entropy, we have gathered current work on such information decomposition approaches from many of the leading research groups in the field. We begin our editorial by providing the reader with a review of previous information decomposition research, including an overview of the variety of measures proposed, how they have been interpreted and applied to empirical investigations. We then introduce the articles included in the special issue one by one, providing a similar categorisation of these articles into: i. proposals of new measures; ii. theoretical investigations into properties and interpretations of such approaches, and iii. applications of these measures in empirical studies. We finish by providing an outlook on the future of the field.
摘要：
Williams和Beer在2010年制定的部分信息分解（PID）框架引起了人们对定义冗余（或共享）问题的大量关注，一组源变量提供的关于目标的互信息的独特和协同(或互补)成分。这种关注导致提出了一些措施来捕捉这些概念，对这些措施的理论研究，以及对经验数据(特别是神经科学数据集)的应用。在本期关于熵的“多源相互作用中目标效应的信息分解”的特刊中，我们已经从该领域的许多领先研究小组收集了有关此类信息分解方法的最新工作。我们通过向读者提供以前的信息分解研究的回顾来开始我们的社论，包括拟议的各种措施的概述，它们是如何被解释和应用于实证调查的。然后我们逐一介绍特刊中的文章，将这些条款类似地分类为：i.新措施的建议；ii.对这些方法的性质和解释的理论研究，andiii.这些措施在实证研究中的应用。最后，我们对该领域的未来进行了展望。
