Can self-balancing AI models be designed using fractal patterns and Taoist equilibrium principles?

Question

I'm investigating an alternative AI decision-making framework that draws inspiration from Taoist philosophy, fractal intelligence, and adaptive feedback loops. Unlike traditional AI models that depend on linear logic and strict optimization, this approach explores whether intelligence can emerge through self-organizing, dynamic systems instead.

Building on concepts from the I Ching (an ancient Chinese binary system) and fractal recursion,

For a deeper look into how the I Ching’s binary structure (1100 BCE) relates to computing, AI, and quantum mechanics, check out this video: Ancient Chinese Binary Code – https://youtu.be/OwF-sZHJSNk

I came across an experimental Python model designed to stabilize a system’s state using feedback loops and fractal harmonization:

Approach:

• The system continuously adjusts itself towards an equilibrium point (analogous to Taoist balance)
• A fractal recursion function ensures the system adapts at multiple scales rather than following a rigid decision tree.
• Feedback loops iteratively nudge the system toward self-correction, mimicking natural self-organizing behavior.

python def tao_grid(load):
balance_point = 0.7 # Equilibrium’s hum
return feedback_loop(nudge_load(balance_point, load), fractal_harmony(load))

def feedback_loop(action, state):
new_state = apply_action(action, state)
if is_harmonized(new_state):
return new_state
return adjust_for_harmony(new_state)

def nudge_load(balance_point, load):
if load > balance_point:
return load - (load - balance_point) * 0.4
return load + (balance_point - load) * 0.2

def fractal_harmony(state, level=2):
if level <= 0:
return state
return fractal_harmony(state / 2, level - 1) + fractal_harmony(state / 2, level - 1)

def is_harmonized(state):
return state <= 0.7

if name == "main":
load = 0.87 # Unstable system state
balanced_load = tao_grid(load)
print(f"Grid Load: {load:.2f} → {balanced_load:.2f}—Tao hums!")

Key Questions:

1. Are fractal-based decision models practical for AI self-balancing systems?
2. How could this approach be improved for real-world AI applications, such as power grids or adaptive networks?
3. Are there known AI architectures that already implement similar self-organizing equilibrium principles?

Any feedback or references to existing research on fractal intelligence, self-organizing AI, or alternative decision-making paradigms would be greatly appreciated!

Answer 1

Fractals are practical for self-similar systems requiring multi-scale adaptability, but current implementations are niche. Key applications include hierarchical reinforcement learning and FractalNet using fractal structures for deep learning showing robustness to dropped layers, self-organizing maps (SOMs) as unsupervised learning networks that adaptively cluster and organize data, and reservoir computing as Echo state networks with fractal-like recurrent architectures to maintain memory and adapt behavior. You can further read Larsson et al. (2017) "FRACTALNET: ULTRA-DEEP NEURAL NETWORKS WITHOUT RESIDUALS".

In experiments, fractal networks match the excellent performance of standard residual networks on both CIFAR and ImageNet classification tasks, thereby demonstrating that residual representations may not be fundamental to the success of extremely deep convolutional neural networks. Rather, the key may be the ability to transition, during training, from effectively shallow to deep. We note similarities with student-teacher behavior and develop drop-path, a natural extension of dropout, to regularize co-adaptation of subpaths in fractal architectures. Such regularization allows extraction of high performance fixed-depth subnetworks. Additionally, fractal networks exhibit an anytime property: shallow subnetworks provide a quick answer, while deeper subnetworks, with higher latency, provide a more accurate answer.

Another possibly relevant reference is Cerezo et al. (2018) "Fractal AI, A Fragile Theory of Intelligence", their company has a related website.

Fractal AI is a theory for general artificial intelligence. It allows to derive new mathematical tools that constitute the foundations for a new kind of stochastic calculus, by modelling information using cellular automaton-like structures instead of smooth functions. In the repository included we are presenting a new Agent, derived from the first principles of the theory, which is capable of solving Atari games several orders of magnitude more efficiently than other similar techniques, like Monte Carlo Tree Search... Among other things, Fractal AI makes it possible to generate a huge database of top performing examples with very little amount of computation required, transforming Reinforcement Learning into a supervised problem.