Topical Collection "Advances in Integrated Information Theory"

Editors

Dr. Larissa Albantakis
E-Mail Website
Collection Editor
Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin–Madison, 6001 Research Park Blvd, Madison, WI 53719, USA
Interests: causation and causal analysis; information; complex system science; (artificial) neural networks; machine learning; computational neuroscience; cognition; decision-making; artificial life/intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Matteo Grasso
E-Mail Website
Collection Editor
Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin–Madison, 6001 Research Park Blvd, Madison, WI 53719, USA
Interests: computational neuroscience; artificial neural networks; philosophy of mind; metaphysics; neuroethics; consciousness; causation; concepts; free will; artificial consciousness
Dr. Andrew Haun
E-Mail Website
Collection Editor
Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin–Madison, 6001 Research Park Blvd, Madison, WI 53719, USA
Interests: perception; cognitive science; neuroimaging; phenomenology; conscious contents

Topical Collection Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the collection website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • causal composition and higher order interactions
  • identifying causal/informational boundaries
  • causal exclusion and emergence
  • practical approximations of integrated information
  • mappings between phenomenology and compositional causal structures
  • applications

Published Papers

This collection is now open for submission.
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