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DOI: 10.14569/IJACSA.2015.060632
PDF

An Adaptive Learning Mechanism for Selection of Increasingly More Complex Systems

Author 1: Fouad Khan

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 6 Issue 6, 2015.

  • Abstract and Keywords
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Abstract: Recently it has been demonstrated that causal entropic forces can lead to the emergence of complex phenomena associated with human cognitive niche such as tool use and social cooperation. Here I show that even more fundamental traits associated with human cognition such as ‘self-awareness’ can easily be demonstrated to be arising out of merely a selection for ‘better regulators’; i.e. systems which respond comparatively better to threats to their existence which are internal to themselves. A simple model demonstrates how indeed the average self-awareness for a universe of systems continues to rise as less self-aware systems are eliminated. The model also demonstrates however that the maximum attainable self-awareness for any system is limited by the plasticity and energy availability for that typology of systems. I argue that this rise in self-awareness may be the reason why systems tend towards greater complexity.

Keywords: Adaptive Learning, Complexity, Self-awareness, Good regulator theorem, Adaptive Selection

Fouad Khan, “An Adaptive Learning Mechanism for Selection of Increasingly More Complex Systems” International Journal of Advanced Computer Science and Applications(IJACSA), 6(6), 2015. http://dx.doi.org/10.14569/IJACSA.2015.060632

@article{Khan2015,
title = {An Adaptive Learning Mechanism for Selection of Increasingly More Complex Systems},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2015.060632},
url = {http://dx.doi.org/10.14569/IJACSA.2015.060632},
year = {2015},
publisher = {The Science and Information Organization},
volume = {6},
number = {6},
author = {Fouad Khan}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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