Special Issue "Integrated Information Theory"
Deadline for manuscript submissions: closed (28 February 2019)
Dr. Larissa Albantakis
Department of Psychiatry, School of Medicine and Public Health, University of Wisconsin–Madison, 6001 Research Park Blvd, Madison, WI 53719, USA
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Interests: causation and causal analysis; information; complex system science; (artificial) neural networks; machine learning; computational neuroscience; cognition; decision-making; artificial life/intelligence
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
Manuscript Submission Information
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- physical information
- causal composition and higher order interactions
- identifying causal/informational boundaries
- informational/causal measures of autonomy
- causal exclusion and emergence
- practical approximations of integrated information