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Chaos in coupled optimizers.

Annals of the New York Academy of Sciences

|January 1, 1987

Summary

This summary is machine-generated.

Autonomous optimizers, a type of dynamical system, bridge artificial intelligence and deductive biology. Their interactions can create complex attractors, potentially linking humanistic theories to scientific measurement problems.

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Area of Science:

Explores the intersection of artificial intelligence, dynamical systems, and deductive biology.
Investigates the emergent properties of autonomous optimizers.

Background:

Autonomous optimizers are a subclass of dynamical systems.
Classical models involve coupled systems exhibiting complex behaviors.
The study considers advanced optimizers with internal simulation capabilities.

Purpose of the Study:

To analyze the behavior of coupled autonomous optimizers.
To explore the potential for complex attractors (chaotic and cloud).
To investigate the implications for humanistic theories and the physical measurement problem.

Main Methods:

Modeling coupled autonomous optimizers using principles from dynamical systems.
Analyzing the emergent properties of these systems, including attractor formation.

Related Experiment Videos

Considering the role of internal simulation in more complex optimizers.

Main Results:

Symmetrically coupled simple optimizers generate chaotic and potentially cloud attractors.
More complex autonomous optimizers exhibit more intricate interactional function changes.
Suggests a potential scientific framework for humanistic concepts.

Conclusions:

Autonomous optimizers offer a novel framework for studying complex systems.
The study bridges concepts from AI, dynamical systems, and biology.
Opens new perspectives on the relationship between the physical sciences and humanities.