关联性与信息冗余
Contextuality and Informational Redundancy.
作者信息
Dzhafarov Ehtibar N, Kujala Janne V
机构信息
Department of Psychological Sciences, Purdue University, West Lafayette, IN 47907, USA.
Department of Mathematics and Statistics, University of Turku, FI-20014 Turun yliopisto, Finland.
出版信息
Entropy (Basel). 2022 Dec 21;25(1):6. doi: 10.3390/e25010006.
A noncontextual system of random variables may become contextual if one adds to it a set of new variables, even if each of them is obtained by the same context-wise function of the old variables. This fact follows from the definition of contextuality, and its demonstration is trivial for inconsistently connected systems (i.e., systems with disturbance). However, it also holds for consistently connected (and even strongly consistently connected) systems, provided one acknowledges that if a given property was not measured in a given context, this information can be used in defining functions among the random variables. Moreover, every inconsistently connected system can be presented as a (strongly) consistently connected system with essentially the same contextuality characteristics.
如果向一个非情境性的随机变量系统添加一组新变量，即使每个新变量都是通过旧变量的相同情境函数获得的，该系统也可能会变成情境性的。这一事实源于情境性的定义，对于非一致连接的系统（即有干扰的系统），其证明很简单。然而，对于一致连接（甚至是强一致连接）的系统，这一点同样成立，前提是要认识到，如果在给定情境中未测量给定属性，那么这一信息可用于定义随机变量之间的函数。此外，每个非一致连接的系统都可以表示为具有基本相同情境特征的（强）一致连接系统。
Zotero 插件