In service to the mathematical underpinnings of the Information Integration Theory of Consciousness (IIT), we introduce four principled measures of integration based on the partial information decomposition framework. We compare our measures to current IIT practice in simple deterministic networks. Our measures are the first IIT-related state-dependent integration measures to naturally obey standard info-theoretic lower and upper bounds. Matching IIT4, the four measures are non-Shannon, but outside IIT they work just as well as standard Shannon-based measures of irreducibility in discrete dynamical systems.

翻译：为服务于意识信息整合理论（IIT）的数学基础，我们基于部分信息分解框架引入了四种基于原则的整合度量方法。我们将这些度量方法与简单确定性网络中的当前IIT实践进行了比较。我们的度量方法是首个与IIT相关的状态依赖型整合度量方法，天然满足标准信息论的下界和上界约束。与IIT4一致，这四种度量方法属于非香农型，但在IIT框架之外，它们与离散动态系统中基于香农的不可约性标准度量方法同样有效。