The Self-Simulation Hypothesis Interpretation of Quantum Mechanics

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Abstract

We modify the simulation hypothesis to a self-simulation hypothesis, where the physical universe, as a strange loop, is a mental self-simulation that might exist as one of a broad class of possible code theoretic quantum gravity models of reality obeying the principle of efficient language axiom. This leads to ontological interpretations about quantum mechanics. We also discuss some implications of the self-simulation hypothesis such as an informational arrow of time.

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Most cited references 99

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A free energy principle for the brain.

Karl Friston, James M. Kilner, Lee C. Harrison (2006)

By formulating Helmholtz's ideas about perception, in terms of modern-day theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts: using constructs from statistical physics, the problems of inferring the causes of sensory input and learning the causal structure of their generation can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory input is caused. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of cortical organisation and responses. In this paper, we show these perceptual processes are just one aspect of emergent behaviours of systems that conform to a free energy principle. The free energy considered here measures the difference between the probability distribution of environmental quantities that act on the system and an arbitrary distribution encoded by its configuration. The system can minimise free energy by changing its configuration to affect the way it samples the environment or change the distribution it encodes. These changes correspond to action and perception respectively and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment assumes that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at the models entailed by the brain and how minimisation of its free energy can explain its dynamics and structure.

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Integrated information theory: from consciousness to its physical substrate.

Giulio Tononi, Melanie Boly, Marcello Massimini … (2016)

In this Opinion article, we discuss how integrated information theory accounts for several aspects of the relationship between consciousness and the brain. Integrated information theory starts from the essential properties of phenomenal experience, from which it derives the requirements for the physical substrate of consciousness. It argues that the physical substrate of consciousness must be a maximum of intrinsic cause-effect power and provides a means to determine, in principle, the quality and quantity of experience. The theory leads to some counterintuitive predictions and can be used to develop new tools for assessing consciousness in non-communicative patients.

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Author and article information

Journal

Journal ID (nlm-ta): Entropy (Basel)

Journal ID (iso-abbrev): Entropy (Basel)

Journal ID (publisher-id): entropy

Title: Entropy

Publisher: MDPI

ISSN (Electronic): 1099-4300

Publication date (Electronic): 21 February 2020

Publication date Collection: February 2020

Volume: 22

Issue: 2

Electronic Location Identifier: 247

Affiliations

Quantum Gravity Research, Los Angeles, CA 90290, USA; marcelo@ 123456quantumgravityresearch.org (M.A.); davidc@ 123456quantumgravityresearch.org (D.C.)

Author notes

[* ]Correspondence: Klee@ 123456QuantumGravityResearch.com

Author information

Klee Irwin https://orcid.org/0000-0003-2938-3941

Marcelo Amaral https://orcid.org/0000-0002-0637-1916

Article

Publisher ID: entropy-22-00247

DOI: 10.3390/e22020247

PMC ID: 7516678

SO-VID: e7e435cd-b27f-4873-8430-6e61b0ac5d1c

License:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

History

Date received : 11 January 2020

Date accepted : 17 February 2020

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