Department of Psychiatry, University of Wisconsin, Madison, Wisconsin, United States of America.
PLoS Comput Biol. 2024 Aug 5;20(8):e1012323. doi: 10.1371/journal.pcbi.1012323. eCollection 2024 Aug.
Originally developed as a theory of consciousness, integrated information theory provides a mathematical framework to quantify the causal irreducibility of systems and subsets of units in the system. Specifically, mechanism integrated information quantifies how much of the causal powers of a subset of units in a state, also referred to as a mechanism, cannot be accounted for by its parts. If the causal powers of the mechanism can be fully explained by its parts, it is reducible and its integrated information is zero. Here, we study the upper bound of this measure and how it is achieved. We study mechanisms in isolation, groups of mechanisms, and groups of causal relations among mechanisms. We put forward new theoretical results that show mechanisms that share parts with each other cannot all achieve their maximum. We also introduce techniques to design systems that can maximize the integrated information of a subset of their mechanisms or relations. Our results can potentially be used to exploit the symmetries and constraints to reduce the computations significantly and to compare different connectivity profiles in terms of their maximal achievable integrated information.
最初作为意识理论而发展起来的综合信息理论，为量化系统和系统中单位子集的因果不可还原性提供了一个数学框架。具体来说，机制综合信息量化了一个状态下的单位子集（也称为机制）的因果力有多少不能用其部分来解释。如果机制的因果力可以完全用其部分来解释，那么它就是可还原的，其综合信息为零。在这里，我们研究了这个度量的上限以及如何实现它。我们研究了孤立的机制、机制组以及机制之间的因果关系组。我们提出了新的理论结果，表明彼此共享部分的机制不可能都达到其最大值。我们还介绍了设计系统的技术，可以最大限度地提高其机制或关系子集的综合信息。我们的结果可能被用来利用对称性和约束来显著减少计算，并根据其最大可实现综合信息来比较不同的连接性配置文件。
