
TL;DR
This paper presents a theory of cognition in dynamical systems, explaining how agents form emergent patterns that encode knowledge, with implications across various complex systems and a new perspective on complexity.
Contribution
It introduces a novel framework for understanding cognition in dynamical systems through pattern formation and timescale separation, applicable to diverse real-world systems.
Findings
Patterns encode macro-level knowledge.
Coherent patterns are acausal and unpredictable.
The theory applies to neural, economic, and biological systems.
Abstract
Cognition is the process of knowing. As carried out by a dynamical system, it is the process by which the system absorbs information into its state. A complex network of agents cognizes knowledge about its environment, internal dynamics and initial state by forming emergent, macro-level patterns. Such patterns require each agent to find its place while partially aware of the whole pattern. Such partial awareness can be achieved by separating the system dynamics into two parts by timescale: the propagation dynamics and the pattern dynamics. The fast propagation dynamics describe the spread of signals across the network. If they converge to a fixed point for any quasi-static state of the slow pattern dynamics, that fixed point represents an aggregate of macro-level information. On longer timescales, agents coordinate via positive feedback to form patterns, which are defined using closed…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
