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Published on: December 4, 2017
Recurrent, nonequilibrium systems and the Markov blanket assumption.
Miguel Aguilera1, Christopher L Buckley1
1School of Engineering and Informatics, University of Sussex, Falmer, Brighton BN1 9QJ, UKsci@maguilera.nethttps://maguilera.net/ C.L.Buckley@sussex.ac.ukhttps://christopherlbuckley.com/.
Markov blankets, used to define system-environment boundaries in Bayesian cognition, are typically based on simple network assumptions. However, real living systems have complex interactions, making these blankets rare and specific.
Area of Science:
- Cognitive Science
- Theoretical Biology
- Statistical Physics
Background:
- Markov blankets are statistical tools defining system-environment boundaries.
- They are popular in Bayesian models of cognition.
- Their theoretical basis lies in acyclic, atemporal networks.
Purpose of the Study:
- To investigate the applicability of Markov blankets to living systems.
- To analyze the limitations of Markov blankets in the context of nonequilibrium dynamics.
Main Methods:
- Conceptual analysis of Markov blanket theory.
- Comparison with principles of nonequilibrium thermodynamics and complex systems.
Main Results:
- Living systems exhibit recurrent, nonequilibrium interactions.
- These interactions create pervasive system-environment couplings.
- This challenges the conventional assumptions underlying Markov blankets.
Conclusions:
- Markov blankets are not universally applicable to living systems.
- Their utility is restricted to specific, simplified cases.
- Further theoretical development is needed for complex biological systems.
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