An Adaptive Learning Mechanism for Selection of Increasingly More Complex Systems
This addresses a foundational problem in understanding cognitive evolution and complexity for researchers in AI, cognitive science, and systems theory, but it appears incremental as it builds on prior work on causal entropic forces.
The paper tackles the problem of explaining the emergence of self-awareness and complexity in systems by proposing that selection for better regulators leads to increased self-awareness, with a model showing average self-awareness rises as less self-aware systems are eliminated, but maximum self-awareness is limited by plasticity and energy availability.
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.