THE DIGITAL GENOME AS A PLURIDIMENSIONAL CONSTRUCT IN POST-EPISTEMIC LEARNING: A D12 BASED MATHEMATICAL MODEL

THE DIGITAL GENOME AS A PLURIDIMENSIONAL CONSTRUCT IN POST-EPISTEMIC LEARNING: A D12-BASED MATHEMATICAL MODEL

Mapping digital identity through the d12 theorem: towards a quantized model of the learning self

    This paper introduces the concept of the digital genome as a mathematical and epistemological construct grounded in the d12 theorem, a theorem that I conceptualised for learning and reality as a pluridimensional, dynamic, and axilogical structure.

The digital genome represents the totality of a learner's epistemic imprints in digital environments as a quantum-like function evolving through interaction, reflection and choice, rather than fixed data trails. Using a multidimensional model inspired by quantum mechanics and d12 principles, the genome is formalised as a vector function G (x, t, u), where learning is interpreted through time (t), intention (u), and contextualised action (x). 

    The paper proposes a theoretical framework in which digital identity is mapped across 12 epistemological dimensions, allowing for new approaches in personalised learning, cognitive diagnostics, and effective transfer. The digital genome ceases to be a metaphor, becoming instead a structure of epistemic frequency which is readable, interpretable and transformable. 

    This redefinition has implications for postdigital pedagogy, AI ethics, and future-orientated educational research. The d12 model positions learning as a continuous function in quantum-axiological education and offers a differentiated view of the learner.

VECTORIAL DIAGRAM DIGITAL GENOME

import matplotlib.pyplot as plt

import matplotlib.patches as patches

import numpy as np

# Set up figure and axis

fig, ax = plt.subplots(figsize=(10, 5))

ax.set_xlim(0, 12)

ax.set_ylim(0, 1.5)

ax.axis('off')

# Example segments representing different "genes" or digital traits

segments = [

    {"start": 0, "end": 1, "label": "G1"},

    {"start": 1, "end": 2.5, "label": "G2"},

    {"start": 2.5, "end": 3, "label": "G3"},

    {"start": 3, "end": 4.2, "label": "G4"},

    {"start": 4.2, "end": 6, "label": "G5"},

    {"start": 6, "end": 7, "label": "G6"},

    {"start": 7, "end": 9.5, "label": "G7"},

    {"start": 9.5, "end": 10.5, "label": "G8"},

    {"start": 10.5, "end": 12, "label": "G9"}

]

# Draw genome line

ax.hlines(y=0.75, xmin=0, xmax=12, color="black", linewidth=2)

# Add segments and labels

colors = plt.cm.viridis(np.linspace(0, 1, len(segments)))

for i, seg in enumerate(segments):

    ax.add_patch(patches.FancyBboxPatch(

        (seg["start"], 0.65), seg["end"] - seg["start"], 0.2,

        boxstyle="round,pad=0.02", edgecolor="black", facecolor=colors[i], linewidth=1.5

    ))

    ax.text((seg["start"] + seg["end"]) / 2, 1.0, seg["label"],

            ha='center', va='bottom', fontsize=10, weight='bold')

plt.title("Diagramă vectorială a genomului digital (D12)", fontsize=14, weight='bold')

plt.tight_layout()

plt.show()

DIGITAL GENOME SEQUENCE 

Example Genome Structure (D12)

Segment	Code (Binary)	Function	Analogous to Human
G1	1101 0010	Cognitive capacity (logic)	Neural genes
G2	1010 1111	Emotional reactivity	Limbic response
G3	1001 0011	Memory formation	Hippocampal genes
G4	1110 1100	Visual processing	Occipital genes
G5	0111 0001	Language processing	Broca/Wernicke
G6	0001 1110	Decision thresholds	Frontal cortex
G7	1011 1010	Motor skills (control loops)	Cerebellum
G8	1100 1100	Behavioral patterns	Behavioral genes
G9	1000 0110	Sensory integration	Sensory neurons
G10	1111 0000	Adaptation/learning rate	Epigenetics
G11	0110 1010	Social interaction model	Social cognition
G12	0000 1111	Reward & motivation system	Dopaminergic path

Written today, June 18, 2025

in Madrid, Spain