The partial information decomposition (PID) framework is concerned with decomposing the information that a set of random variables has with respect to a target variable into three types of components: redundant, synergistic, and unique. Classical information theory alone does not provide a unique way to decompose information in this manner and additional assumptions have to be made. Inspired by Kolchinsky's recent proposal for measures of intersection information, we introduce three new measures based on well-known partial orders between communication channels and study some of their properties.

翻译：部分信息分解（PID）框架旨在将一组随机变量相对于目标变量的信息分解为三类成分：冗余信息、协同信息和特有信息。经典信息论本身无法唯一确定此类信息分解方式，必须引入额外假设。受Kolchinsky近期提出的交集信息测度方案的启发，我们基于通信信道之间已知的偏序关系引入三种新的测度，并研究其部分性质。