In service to the mathematical underpinnings of the Information Integration Theory of Consciousness (IIT), we introduce four measures of integration based on the partial information decomposition framework. We compare our measures to current IIT practice in simple deterministic networks. We find synergy-based measures more suitable for IIT's use-case than current practice. Outside IIT, these measures could also be useful as measures of complexity for discrete dynamical systems.

翻译：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整合度量方法。我们将这些度量与当前IIT在简单确定性网络中的实践进行了比较。研究发现，基于协同效应的度量比当前方法更适用于IIT的应用场景。在IIT之外，这些度量也可作为离散动力系统复杂性的有效指标。