Tick Scheduling Model

The deterministic execution framework driving simulation updates.

The Tick Scheduling Model governs how and when subsystems update world state.
It establishes the order, timing, and execution rules that ensure consistency across the simulation.

This page provides a conceptual overview of the scheduling model.

Purpose

The purpose of the scheduling model is to coordinate subsystem updates so they occur:

in a predictable sequence
on the correct temporal clock
without conflicts
without hidden dependencies

This preserves simulation coherence.

Core Principles

Fixed Ordering — update flow remains stable across sessions.
Clock-Driven Execution — each tick selects which subsystems run.
Non-Interference — subsystems cannot disrupt each other’s updates.
Consistent Cadence — changes occur at regular, predictable intervals.
Transparency — the execution order is inspectable through tools.

System Model

Master Tick Loop

A central loop processes all active clocks each frame.

Clock Sequence

Clocks fire in a deterministic order:

slow
medium
fast
real-time

Subsystem Batching

Subsystems assigned to the same clock update together in known sequence.

Write Commit Phase

After subsystem updates, field changes commit cleanly to world state.

Overlay Update

Visual layers refresh after commit.

How It Interacts With Other Systems

Multi-Clock Structure defines update cadence.
Subsystems declare when they update.
Field System receives final writes each tick.
Overlays visualize post-commit state.
Application Layer controls tick flow.

What This Enables

coherent multi-domain simulation
long-term emergent world behavior
high-performance update batching
stable debugging and inspection
predictable educational scenarios

Visual Examples (Optional)

tick sequence diagrams
ordering visualization
subsystem batch timing reports

Public Extensibility Notes

The SDK will eventually allow developers to:

register custom tick phases
attach subsystems to bespoke schedules
build time-driven scenario logic

The scheduling model remains predictable and extensible.