An Adaptive Learning Mechanism for Selection of Increasingly More Complex Systems

TL;DR

This paper proposes a model showing how systems evolve increased self-awareness through selection for better regulation, suggesting a link between self-awareness, complexity, and resource constraints.

Contribution

It introduces a simple model demonstrating the emergence of self-awareness from selection pressures and discusses its implications for system complexity.

Findings

Self-awareness increases as less self-aware systems are eliminated.

Maximum self-awareness is limited by system plasticity and energy availability.

The rise in self-awareness may drive systems toward greater complexity.

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.