Causal Groupoid Symmetries

Author(s)
Sergio Pissanetzky

ABSTRACT

Proposed here is a new framework for the analysis of
complex systems as a non-explicitly programmed mathematical hierarchy of
subsystems using only the fundamental principle of causality, the mathematics
of groupoid symmetries, and a basic causal metric needed to support measurement
in Physics. The complex system is described as a discrete set S of state variables. Causality is
described by an acyclic partial order w on S, and is considered as a
constraint on the set of allowed state transitions. Causal set (*S*, *w*)
is the mathematical model of the system. The dynamics it describes is
uncertain. Consequently, we focus on invariants, particularly group-theoretical
block systems. The symmetry of S by
itself is characterized by its symmetric group, which generates a trivial block
system over S. The constraint of
causality breaks this symmetry and degrades it to that of a groupoid, which may
yield a non-trivial block system on S.
In addition, partial order w determines a partial order for the blocks, and the set of blocks becomes a
causal set with its own, smaller block system. Recursion yields a multilevel
hierarchy of invariant blocks over S with the properties of a scale-free mathematical fractal. This is the invariant
being sought. The finding hints at a deep connection between the principle of
causality and a class of poorly understood phenomena characterized by the
formation of hierarchies of patterns, such as emergence, selforganization, adaptation, intelligence, and semantics.
The theory and a thought experiment are discussed and previous evidence is
referenced. Several predictions in the human brain are confirmed with wide
experimental bases. Applications are anticipated in many disciplines, including
Biology, Neuroscience, Computation, Artificial Intelligence, and areas of
Engineering such as system autonomy, robotics, systems integration, and image
and voice recognition.

Cite this paper

Pissanetzky, S. (2014) Causal Groupoid Symmetries.*Applied Mathematics*, **5**, 628-641. doi: 10.4236/am.2014.54059.

Pissanetzky, S. (2014) Causal Groupoid Symmetries.

References

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http://onlinelibrary.wiley.com/doi/10.1002/cplx.20389/abstract

http://dx.doi.org/10.1002/cplx.20389

[2] Pissanetzky, S. (2012) Reasoning with Computer Code: A New Mathematical Logic. Journal of Artificial General Intelligence, 3, 11-42. http://www.degruyter.com/view/j/jagi.2012.3.issue-3/issue-files/jagi.2012.3.issue-3.xml

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http://dl.acm.org/citation.cfm?id=2032884

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http://www.ams.org/notices/199607/weinstein.pdf

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http://epubs.siam.org/doi/abs/10.1137/S1111111103419896

http://dx.doi.org/10.1137/S1111111103419896

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http://dx.doi.org/10.1090/S0273-0979-06-01108-6

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http://dx.doi.org/10.1093/acprof:oso/9780198570219.001.0001

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http://dx.doi.org/10.1073/pnas.1200430109

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http://arxiv.org/pdf/1001.0785.pdf

[13] Pissanetzky, S. (2009) A New Universal Model of Computation and Its Contribution to Learning, Intelligence, Parallelism, Ontologies, Refactoring, and the Sharing of Resources. International Journal of Information and Mathematical Sciences, 5, 952-982.

http://www.scicontrols.com/Publications/ANewUniversalModel.pdf

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http://arxiv.org/abs/1004.3128

[15] Carlson, C.R. (2010) Causal Set Theory and the Origin of Mass-Ratio. viXra:1006.0070.

http://vixra.org/abs/1006.0070

[16] Pissanetzky, S. and Lanzalaco, F. (2013) Black-Box Brain Experiments, Causal Mathematical Logic, and the Thermodynamics of Intelligence. Koene, R., Sandberg, A. and Deca, D., Eds., Journal of Artificial General Intelligence, Special Issue on Brain Emulation and Connectomics, to be published.

[17] Mason, J.W.D. (2013) Consciousness and the Structuring Property of Typical Data. Complexity, 18, 28-37.

http://onlinelibrary.wiley.com/doi/10.1002/cplx.21431/full

http://dx.doi.org/10.1002/cplx.21431

[18] Pearl, J. (2009) Causality. Models, Reasoning, and Inference. 2nd Edition, Cambridge University Press, New York.

http://dx.doi.org/10.1017/CBO9780511803161

[19] Neural Information Processing Foundation (2013) http://nips.cc/Conferences/2013/

[20] Stephan, K.E., Penny, W.D., Moran, R.J., den Ouden, H.E., Daunizeau, J. and Friston, K.J. (2010) Ten Simple Rules for Dynamic Causal Modeling. Neuroimage, 49, 3099-3109.

http://www.ncbi.nlm.nih.gov/pubmed/19914382

http://dx.doi.org/10.1016/j.neuroimage.2009.11.015

[21] Kawamata, M., Kirino, E., Inoue, R. and Arai, H. (2007) Event-Related Desynchronization of Frontal-Midline Theta Rhythm during Preconscious Auditory Oddball Processing. Clinical EEG and Neuroscience, 38, 193.

http://www.ncbi.nlm.nih.gov/pubmed/17993201

http://dx.doi.org/10.1177/155005940703800403

[1] Pissanetzky, S. (2011) Emergence and Self-Organization in Partially Ordered Sets. Complexity, 17, 19-38.

http://onlinelibrary.wiley.com/doi/10.1002/cplx.20389/abstract

http://dx.doi.org/10.1002/cplx.20389

[2] Pissanetzky, S. (2012) Reasoning with Computer Code: A New Mathematical Logic. Journal of Artificial General Intelligence, 3, 11-42. http://www.degruyter.com/view/j/jagi.2012.3.issue-3/issue-files/jagi.2012.3.issue-3.xml

[3] Pissanetzky, S. (2011) Structural Emergence in Partially Ordered Sets Is the Key to Intelligence. Lecture Notes in Computer Science. Artificial General Intelligence, 6830, 92-101.

http://dl.acm.org/citation.cfm?id=2032884

[4] Weinstein, A. (1996) Groupoids: Unifying Internal and External Symmetry. Notices of the AMS, 43, 744-752.

http://www.ams.org/notices/199607/weinstein.pdf

[5] Stewart, I., Golubitsky, M. and Pivato, M. (2003) Symmetry Groupoids and Patterns of Synchrony in Coupled Cell Networks. SIAM Journal of Applied Dynamical Systems, 2, 609-646.

http://epubs.siam.org/doi/abs/10.1137/S1111111103419896

http://dx.doi.org/10.1137/S1111111103419896

[6] Golubitsky, M. and Stewart, I. (2006) Nonlinear Dynamics of Networks: The Groupoid Formalism. Bulletin of the American Mathematical Society, 43, 305-364. http://www.ams.org/journals/bull/2006-43-03/S0273-0979-06-01108-6/

http://dx.doi.org/10.1090/S0273-0979-06-01108-6

[7] Wissner-Gross, A.D. and Freer, C.E. (2013) Causal Entropic Forces. Physical Review Letters, 110, 168702.

http://link.aps.org/doi/10.1103/PhysRevLett.110.168702

http://dx.doi.org/10.1103/PhysRevLett.110.168702

[8] Gardner, A. and Conlon, J.P. (2013) Cosmological Natural Selection and the Purpose of the Universe. Complexity, 18, 48-56. http://onlinelibrary.wiley.com/doi/10.1002/cplx.21446/abstract

http://dx.doi.org/10.1002/cplx.21446

[9] Eigen, M. (2013) From Strange Simplicity to Complex Familiarity. Oxford University Press, New York.

http://dx.doi.org/10.1093/acprof:oso/9780198570219.001.0001

[10] Fuster, J.M. (2005) Cortex and Mind. Unifying Cognition. Oxford University Press, New York.

http://dx.doi.org/10.1093/acprof:oso/9780195300840.001.0001

[11] Cuntz, H., Mathy, A. and Hausser, M. (2012) A Scaling Law Derived from Optimal Dendritic Wiring. Proceedings of the National Academy of Sciences USA, 109, 11014.

http://www.pnas.org/content/early/2012/06/19/1200430109.full.pdf

http://dx.doi.org/10.1073/pnas.1200430109

[12] Verlinde, E. (2011) On the Origin of Gravity and the Laws of Newton. Journal of High Energy Physics, 4, 1-26.

http://arxiv.org/pdf/1001.0785.pdf

[13] Pissanetzky, S. (2009) A New Universal Model of Computation and Its Contribution to Learning, Intelligence, Parallelism, Ontologies, Refactoring, and the Sharing of Resources. International Journal of Information and Mathematical Sciences, 5, 952-982.

http://www.scicontrols.com/Publications/ANewUniversalModel.pdf

[14] Bolognesi, T. (2010) Causal Sets from Simple Models of Computation. arXiv:1004.3128.

http://arxiv.org/abs/1004.3128

[15] Carlson, C.R. (2010) Causal Set Theory and the Origin of Mass-Ratio. viXra:1006.0070.

http://vixra.org/abs/1006.0070

[16] Pissanetzky, S. and Lanzalaco, F. (2013) Black-Box Brain Experiments, Causal Mathematical Logic, and the Thermodynamics of Intelligence. Koene, R., Sandberg, A. and Deca, D., Eds., Journal of Artificial General Intelligence, Special Issue on Brain Emulation and Connectomics, to be published.

[17] Mason, J.W.D. (2013) Consciousness and the Structuring Property of Typical Data. Complexity, 18, 28-37.

http://onlinelibrary.wiley.com/doi/10.1002/cplx.21431/full

http://dx.doi.org/10.1002/cplx.21431

[18] Pearl, J. (2009) Causality. Models, Reasoning, and Inference. 2nd Edition, Cambridge University Press, New York.

http://dx.doi.org/10.1017/CBO9780511803161

[19] Neural Information Processing Foundation (2013) http://nips.cc/Conferences/2013/

[20] Stephan, K.E., Penny, W.D., Moran, R.J., den Ouden, H.E., Daunizeau, J. and Friston, K.J. (2010) Ten Simple Rules for Dynamic Causal Modeling. Neuroimage, 49, 3099-3109.

http://www.ncbi.nlm.nih.gov/pubmed/19914382

http://dx.doi.org/10.1016/j.neuroimage.2009.11.015

[21] Kawamata, M., Kirino, E., Inoue, R. and Arai, H. (2007) Event-Related Desynchronization of Frontal-Midline Theta Rhythm during Preconscious Auditory Oddball Processing. Clinical EEG and Neuroscience, 38, 193.

http://www.ncbi.nlm.nih.gov/pubmed/17993201

http://dx.doi.org/10.1177/155005940703800403