MOMA Simulation Engine

A library for creating and running dynamic systems, such as cellular automata, using the MOMA (Moving Origin Modular Arithmetic) framework as the core update rule.

This crate provides the tools to build simulations where the evolution of the system is governed by the complex, non-linear, and deterministic patterns generated by MOMA.

The Core Idea

Most simulations, like cellular automata, use simple, fixed rules to determine the next state of a cell (e.g., "if the left neighbor is black and the right is white, the new cell is black").

The MOMA Simulation Engine replaces these simple rules with a MomaRing. The state of a cell evolves based on the MOMA residue of its current state, where the "moving origin" is determined by the state of its neighbors.

This introduces a rich, complex, and deeply interconnected dynamic, allowing for the generation of intricate and emergent patterns that are unique to the chosen MOMA strategy.

Features

• CellularAutomaton: A simple 1D cellular automaton that uses MOMA for its update logic.
• Strategy-Driven Rules: The behavior of the simulation is determined by the OriginStrategy passed to it. This means you can create vastly different "universes" just by changing the strategy.
• Extensible: Designed to be a foundation for more complex simulations, such as 2D automata or dynamic graph-based systems.

Getting Started

To use this engine in your own project, add it to your Cargo.toml.

[dependencies]

moma_simulation_engine = "0.3.1" # Or the most recent version

or just use cargo add moma_simulation_engine from the command line.

Quick Start: Running a 1D Automaton

Here is a simple example of how to create, run, and display a MOMA-powered cellular automaton.

use moma::strategy;
use moma_simulation_engine::automaton::CellularAutomaton;
use std::{thread, time};

fn main() {
    println!("--- MOMA-Powered 1D Cellular Automaton ---");

    // --- Simulation Parameters ---
    let width = 100;         // Width of the automaton in cells.
    let steps = 200;         // Number of generations to simulate.
    let modulus = 10;        // The number of states for each cell (0-9).
    let delay_ms = 50;       // Delay between steps for visualization.

    // Choose a MOMA strategy to govern the rules.
    // Try changing this to `strategy::PrimeGap` to see a different universe!
    let strategy = strategy::CompositeMass;

    // --- Initialization ---
    let mut automaton = CellularAutomaton::new(width, modulus, strategy);
    println!("Initial State (Generation 0):\n{}\n", automaton.render());

    // --- Simulation Loop ---
    for i in 1..=steps {
        automaton.step();
        println!("Generation {}:", i);
        println!("{}", automaton.render());
        thread::sleep(time::Duration::from_millis(delay_ms));
    }

    println!("\n--- Simulation Complete ---");
}

Current Examples

This crate is the first step in exploring MOMA as a tool for modeling complex systems.

The examples directory includes:

• moma_automaton - 1D Cellular Automata.
• moma_conways_game_of_life - 2D Cellular Automata.
• Moma_pathfinder - A static Graph model
• moma_dynamic_pathfinder - A Dynamic Graph model where node states are updated via MOMA rules.
• moma_gower - An experimental simulation in Rust demonstrating a self-regulating AI agent. This project uses an A* pathfinding algorithm to navigate a dynamic, procedurally generated world. The agent's decision-making is governed by a MOMA (Multi-Objective Meta-Observer Architecture) feedback loop, which uses Gowers Uniformity Norms to analyze the geometric complexity of its own solution paths and adapt its strategy in real-time.
• moma_network_flow_manager - An experimental simulation in Rust demonstrating a self-regulating system that manages network flow. This project showcases an agent that uses a MOMA (Moving Origin Modular Arithmetic) inspired architecture to achieve a high-level, abstract goal. Instead of simply maximizing flow, the agent dynamically adjusts its strategy to maintain a target level of "resilience" or "structural complexity" in its chosen paths, a property measured quantitatively using Gowers Uniformity Norms.
• moma_agent_behavioural_analysis - This is an experiment wherein different MOMA Origin Strategies will produce quantitatively different "personalities" in the agent. We can measure this personality by observing the trade-offs it makes between path efficiency (length) and path complexity (Gowers norm). It was a necessary first step before attempting the moma_network_flow_manager.
• moma_quantum_simulator - A simple, educational quantum circuit simulator built in Rust from first principles. This project demonstrates the core concepts of quantum computing, including superposition, entanglement, and measurement, through a clean, step-by-step implementation.

Author

Neil Crago — experimental mathematician.

License

This project is licensed under either of:

• Apache License, Version 2.0
• MIT license

at your option.

Related Crates

This crate is part of a collection of crates by the same author: These include:-

• MOMA
• Fractal_Algebra
• tma_engine
• factorial_engine
• fa_slow_ai