Advances in Integrated Information Theory

Dear Colleagues,

Integrated information theory (IIT) addresses the problem of consciousness and its physical substrate, providing a quantitative framework to analyze the compositional causal structure of (discrete) dynamical systems. In particular, IIT’s formalism is based on a notion of information that is physical and intrinsic (observer-independent) and a set of causal principles (“postulates”), including causal composition, specificity (“information”), irreducibility (“integration”), and definiteness (“exclusion”).

The IIT formalism offers a way to measure a system’s integrated information (Φ, “Phi”), which has been employed as a general measure of complexity that captures the extent to which a system is both differentiated and integrated. The IIT analysis allows one to identify a system’s causal borders and the organizational levels at which the system exhibits strong causal constraints. According to IIT, a physical substrate of consciousness must specify a maximum of Φ across elements, space, and time.

In addition to yielding a system’s Φ value, IIT’s causal analysis “unfolds” the compositional causal powers of a physical system in its current state. An important prediction of IIT is that the compositional causal structure of a system that specifies a maximum of Φ should account for the qualitative content of consciousness of that system.

For this Topical Collection, we invite contributions that apply, discuss, compare, or extend the theoretical framework of IIT. We also welcome submissions proposing approximations, practical measures, new applications, or alternative formulations of (parts of) the IIT formalism.

Following two Special Issues on IIT (“Integrated information theory” and “Integrated Information Theory and Consciousness”), this Topical Collection provides a repository for contributions that directly address IIT and its theoretical framework.

Dr. Larissa Albantakis
Dr. Matteo Grasso
Dr. Andrew Haun
Collection Editors

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