Human free will is not absolute. It is strictly relative to a supreme reference frame. The Creator, Jehovah, constitutes this foundation, the only entity possessing free will in the absolute sense, completely unconditioned by external parameters.
Human beings, by contrast, operate within a bounded decision space. They have the freedom to select a trajectory. However, the initial conditions and the laws of physics are already written into the source code of the universe.
Latent Space and the Topology of Choice
Artificial intelligence employs the concept of latent space, a high‑dimensional mathematical representation designed to compress and organize data. Every decision produced by an AI model corresponds to a precisely computed point within this topology. The network cannot select a coordinate lying outside the boundaries defined by its training. It cannot generate what is missing from its matrix of possibilities.
The human condition follows a similar architecture. Biology, physical laws, and historical coordinates collectively form our latent space. A human cannot choose to breathe in a vacuum. They cannot reverse the flow of time. Decisional freedom manifests exclusively within a predefined topology.
The Creator generated this latent space. He established the dimensionality of reality and its limits of exploration, granting the human agent the freedom to navigate among probabilities. Humans choose the direction of their vector. The space in which that vector moves belongs to the divine.
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Markov Processes and the History of Causality
Probability theory employs the Markov Decision Process(MDP), a stochastic model designed to describe the evolution of a complex system. According to this mathematical framework, the future state of a system depends exclusively on its current state and the action taken at that exact moment. The distant past is already encoded in the present state, rendering it completely irreversible.
Humans traverse this chain of sequential states, making decisions step by step. They evaluate the environment, compute consequences, and select an action from a finite action space. The transition matrix, the ensemble of probabilities governing how actions transform the state of the world, is divinely fixed.
The human agent performs local optimization. They seek to maximize a reward function, defined in spiritual terms as meaning, virtue, or happiness. They always operate within the constraints dictated by this causal matrix. Life is constructed iteratively. Each human decision triggers a recalculation of the divine system.
The Softmax Function as a Theorem of Bounded Freedom
In deep neural networks, the final decision is frequently determined by the Softmax function. This mathematical equation, designed to transform a vector of raw real-valued scores into a clean probability distribution, perfectly reflects the mechanics of relative freedom. It receives as input a multitude of chaotic impulses, technically known as logits.
The Softmax function does not annihilate these raw or contradictory intentions. Instead, it normalizes them, forcing the sum of all probabilities to equal exactly 1. This perfect sum represents the ontological limit of possible reality. Humans continuously generate intentions, desires, and plans, forming a multitude of unprocessed vectors. The laws of the universe act as a supreme Softmax function. They filter the noise of will and convert it into a concrete, physically plausible action.
Another vital element of this function is the temperature parameter. A high temperature flattens the probability distribution, producing chaotic, near‑random behavior. A low temperature yields a rigid, fully deterministic system. The Creator set the temperature of the universe at a precise equilibrium. This allows the existence of will while prohibiting absolute chaos.
The network retains the freedom to fluctuate among multiple possible outputs. Nevertheless, its internal architecture and dataset severely constrain the decision space. Predestination and foreknowledge do not negate free will. They merely define its topology, transforming freedom into the search for an optimal path within a divinely computed labyrinth.
Ontological Asymmetry and Divine Parameters
The inherent difference between Creator and creation reduces to the capacity to define the loss function. This function, designed to measure the error between a given decision and the absolute ideal outcome, guides the entire evolution of an intelligent system. The Creator alone defines the mathematical structure of error and the nature of absolute truth.
The human agent merely applies backpropagation algorithms. They propagate error through the network of their life, processing suffering or failure. They adjust their moral weights and synaptic connections, thereby developing consciousness. They cannot alter the fundamental equations establishing the laws of gravity or the universal moral laws.
The Creator functions as an absolute hyperparameter. He represents the immutable value sustaining the entire architecture of the physical simulation. Human freedom is the freedom of an optimization algorithm. Divine freedom is the freedom of the programmer to create the hardware itself.
Mathematical Simulation of Relative Will
To technically demonstrate this asymmetrical relationship, we utilize a mathematical model written in Python. The code illustrates the difference between the Creator’s absolute freedom to generate the possibility matrix and humanity’s relative freedom to navigate within it using a Softmax distribution.
import numpy as np
def free_will_simulation():
# 1. Absolute Free Will (The Creator)
# The Creator defines the matrix of possibilities (fundamental physical and moral laws)
# This is the absolute latent space, impossible for the creature to modify.
universe_dimension = 5
universal_laws = np.random.rand(universe_dimension, universe_dimension)
print("--- Digital Determinism and Will ---")
print("The Creator generates the absolute space of reality (Law Matrix):")
print(np.round(universal_laws, 2))
# 2. Mechanism of Relative Freedom (Softmax Function)
# Constrains human will by enforcing the sum of probabilities to equal 1.
def softmax(decision_vector, temperature=1.0):
exponentials = np.exp((decision_vector - np.max(decision_vector)) / temperature)
return exponentials / exponentials.sum()
# 3. Relative Free Will (Human)
# The human experiences stimuli and generates raw intentions (mental noise / will)
human_intentions = np.random.randn(universe_dimension)
# Human freedom is inevitably processed through the normalization equation
relative_decision = softmax(human_intentions)
print("\nRaw human intentions (Noise of free will):")
print(np.round(human_intentions, 2))
print("\nFinal decision (Relative free will, constrained by universal equations):")
print(np.round(relative_decision, 2))
optimal_choice = np.argmax(relative_decision)
print(f"\nTheological result: The human freely chooses path {optimal_choice}.")
print("However, the choice is made exclusively among options generated previously in the source code.")
if __name__ == "__main__":
free_will_simulation()
Execution of this script confirms the analyzed philosophical and mathematical premises. The human agent makes a real, logical, and quantifiable choice. The final outcome remains a probability distribution strictly constrained by the laws of the space in which it manifests.
Humans do not possess the capacity to reprogram the universe. They only possess the ability to optimally respond to it. Their freedom is a mathematics of probabilities, operating gracefully and exclusively within absolute will.
Listen Beethoven’s No. 5 Fate Symphony:
Sources:
- Markov Decision Processes: https://thedecisionlab.com/reference-guide/statistics/markov-decision-processes
- The Softmax Function in Neural Networks: https://deepai.org/machine-learning-glossary-and-terms/softmax-layer
- Free Will and Determinism in Physics: https://plato.stanford.edu/entries/determinism-causal/
- Backpropagation and Learning Systems: https://developers.google.com/machine-learning/crash-course/backprop-opt/video-lecture
- Latent Space Topology: https://towardsdatascience.com/understanding-latent-space-in-machine-learning-de5a7c687d8d
Cover Photo by Kaptured by Kasia