Version: 1.1.1
Author: Michael Fillippini

The Relational Systems Python Library provides tools for modeling, analyzing, and visualizing complex relational systems using tensors, attributes, and dynamic interactions. The library is designed to handle evolving relationships and emergent behaviors in various contexts, such as social networks, biological systems, or multi-agent simulations.

A key feature of this library is the support for Nested Relational Tensors, which enable hierarchical modeling of systems, allowing for parent-child relationships between tensors. This capability is ideal for representing multi-level systems like organizations, ecosystems, or multi-layered networks.

Key features include:

• Nested Relational Tensors: Enabling hierarchical modeling of multi-level systems.
• Reconciliatory Mechanisms: Dynamically resolving conflicts between competing system goals.
• Sensory Mechanisms with UCF/GUTT Wave Function: Incorporating sensory input based on the Unified Computational Framework (UCF) and Grand Unified Tensor Theory (GUTT) wave function for advanced adaptive behavior.

These features enable the library to model complex, hierarchical, and adaptive systems capable of responding to internal dynamics and external stimuli.

tensors.py:

• Manages RelationalTensor objects for storing and manipulating data in multi-dimensional space.
• Supports nested tensors for hierarchical modeling.
• Provides methods for tensor aggregation and dynamic evolution.

tensor_operations.py:

• Implements advanced tensor operations like divergence, contraction, and gradient computations.

entities.py:

• Models RelationalEntity objects with emergent attributes and relationships.
• Manages spheres of influence using nested tensors.

attributes.py:

• Provides classes for relational attributes such as strength, direction, distance, and time.

utils.py:

• Includes general-purpose utility functions for weighting, grouping, and aggregation.

variability.py:

• Adds controlled variability to tensors for simulating emergent behaviors.

interactions.py:

• Defines interaction functions for evolving tensors based on random noise, perspectives, feedback, and thresholds.
• Supports interactions that influence nested tensor hierarchies.

resilience.py:

• Manages resilience and entropy to model system stability and uncertainty.

goal_hierarchy.py:

• Handles dynamic goal prioritization and reconciliation for systems with competing objectives.
• Reconciliatory Mechanism: Resolves conflicts between competing goals using customizable strategies (e.g., severity-based adjustments).

sensory.py:

• Introduced as functions or components (depending on implementation) to process external inputs such as environmental states or external signals.
• Helps integrate external stimuli into internal system dynamics, influencing attributes, goals, or resilience.

A note: Sensory Mechanism input is still in development, but based upon the UCF/GUTT wave function

visualization.py:

• Visualizes tensor evolution over time.
• Renders hierarchical nested tensor relationships as graphs using Matplotlib and NetworkX.