A 'good' regulator may provide a world model for intelligent systems.

Area of Science:

• Cybernetics
• Artificial Intelligence
• Machine Learning

Background:

• The Every Good Regulator Theorem (EGRT) is a foundational concept in cybernetics.
• EGRT defines conditions for an agent (regulator) to accurately control a system's dynamics.
• Modern intelligent systems require adaptable regulatory frameworks.

Purpose of the Study:

• To re-evaluate and modernize the EGRT for contemporary applications.
• To explore the use of compressed global representations (world models) in autonomous learning systems.
• To investigate how physical phenomena can inform regulatory relationships.

Main Methods:

• Reinterpreting the EGRT in a modern context.
• Analyzing one-to-one mappings between regulators (R) and systems (S) for reduced representations.
• Extending EGRT to second-order cybernetics with internal models (M).
• Applying concepts from statistical mechanics (temporal criticality, non-normal denoising) to behavior.

Main Results:

• Demonstrated how compressed world models can achieve good regulation.
• Showcased the extension of EGRT to second-order cybernetics.
• Illustrated how physical phenomena can be recast as statistical mechanics to yield regulatory insights.
• Identified challenges to tightly coupled regulation with non-uniform and out-of-distribution data.

Conclusions:

• The EGRT offers a potential framework for developing adaptive world models.
• Recasting EGRT provides a pathway for intelligent systems to manage diverse task environments.
• Integrating cybernetic principles with physical phenomena enhances understanding of autonomous regulation.