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Special Issue "Integrated Information Theory"

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory".

Deadline for manuscript submissions: 30 November 2018

Special Issue Editors

Guest Editor
Dr. Larissa Albantakis

Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin–Madison, 6001 Research Park Blvd, Madison, WI 53719, USA
Website | E-Mail
Interests: causation and causal analysis; information; complex system science; (artificial) neural networks; machine learning; computational neuroscience; cognition; decision-making; artificial life/intelligence
Guest Editor
Prof. Giulio Tononi

Department of Psychiatry, School of Medicine, University of Wisconsin–Madison, 6001 Research Park Blvd, Madison, WI 53719, USA
Website | E-Mail
Interests: consciousness; sleep

Special Issue Information

Dear Colleagues,

Originally developed to address the problem of consciousness and its physical substrate, integrated information theory (IIT), in its latest version (“IIT 3.0”), provides 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 causal exclusion.
IIT’s main quantity, a system’s amount of integrated information (Φ, “Phi”), has been employed as a general measure of complexity that captures to what extent a system is both differentiated and integrated. What is more, the IIT analysis can reveal a system’s causal borders, and, applied across macro and micro spatiotemporal scales, allows identifying organizational levels at which the system exhibits strong causal constraints.

Applying IIT’s causal measures rigorously, however, is only possible for rather small, discrete or discretized systems, due to combinatorial explosion. Moreover, the proposed mathematical framework may not be unique as a translation of IIT’s causal postulates, and relations to other proposed measures of complexity, (macro) causation, and biological information often remain vague.

For this special issue, we invite contributions that apply, discuss, compare, or extend the theoretical framework of integrated information theory, specifically its latest version, IIT 3.0. Submissions proposing approximations, practical measures, or alternative formulations of (parts of) the IIT formalism are also welcome, as are studies addressing causal composition and physical, intrinsic information in general.

Dr. Larissa Albantakis
Prof. Giulio Tononi
Guest 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 papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue 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 1500 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

  • physical information
  • causal composition and higher order interactions
  • complexity
  • identifying causal/informational boundaries
  • informational/causal measures of autonomy
  • causal exclusion and emergence
  • practical approximations of integrated information
  • applications

Published Papers

This special issue is now open for submission.
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