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
