Causal tapestries for psychology and physics.

Area of Science:

• Complex Systems Modeling
• Formal Systems Theory
• Process Theory

Background:

• Archetypal dynamics offers a formal framework for understanding information flow and emergence in complex systems.
• This approach is built upon the Fundamental Triad: realization (system), interpretation (archetype), and representation (formal model).
• Tapestries serve as a crucial representational system within archetypal dynamics, illustrating information flow through multi-layered, recursive graphical structures.

Purpose of the Study:

• To present a detailed mathematical description of a specific tapestry model: the causal tapestry.
• To apply causal tapestries to the modeling of behaving systems within psychology and physics, utilizing Process Theory.
• To introduce a novel method for generating a Lorentz invariant transient 'now' and incorporating subjective elements into system dynamics.

Main Methods:

• Development of a formal mathematical description for causal tapestries.
• Integration of Process Theory concepts for system analysis.
• Formulation of a 'reality game' to generate dynamics and incorporate hidden variables.
• Representation of observables as 'tapestry informons'.

Main Results:

• A detailed mathematical framework for causal tapestries is established.
• Causal tapestries are shown to generate an explicit Lorentz invariant transient 'now'.
• A random graphical dynamical system is formulated as a specific application of causal tapestries.

Conclusions:

• Causal tapestries provide a robust mathematical tool for modeling complex systems and emergence.
• The framework effectively integrates objective observables with subjective/hidden components.
• This approach offers a novel perspective on dynamic systems in fields like physics and psychology.