Zierax · https://github.com/Zierax/Axiom-02

Version 1.0.0 · Licensed under CC BY-NC 4.0 (see LICENSE)

⚠ DISCLAIMER

AXIOM-02 does NOT claim to create artificial consciousness, sentience, or a mind. It is a deterministic simulation instrument for studying the structure of conflicted decision-making — an epistemological research tool, not an ontological claim of machine awareness.

We present AXIOM-02, a fully deterministic simulation engine that models autonomous decision-making under high-stakes cognitive dissonance through a mutual-inhibition drive network operating over eighteen competing affective drives (grief, rage, fear, love, sacrifice, revenge, spite, cold logic, etc.). The system does not classify or predict human behaviour; instead, it instantiates a competitive, fatiguing, neuromodulator-coupled decision process and reports the structure of that process — whether it deadlocks, oscillates, resolves through spite-driven defiance, or converges on a utility-maximising action. A composite consciousness complexity metric Φ ∈ [0, 1] is derived from eight criteria (deadlock depth, oscillation amplitude, spite intensity, meta-frustration, dissonance breaks, neuromodulator voltage, narrative stability, temporal entropy) to quantify how closely the engine's internal dynamics resemble signatures that in biological systems are associated with conscious deliberation.

The benchmark suite comprises 102 literary and philosophical scenarios drawn from Dostoevsky, Tolstoy, Shakespeare, Kafka, Orwell, Camus, and original ethical dilemmas. All runs are deterministic under a fixed seed (default 42). Mean complexity across the suite is μ = 0.4954 (σ = 0.031), with 17 scenarios classified as "CONSCIOUS" (Φ > 0.50), 31 as "INDETERMINATE" (0.30 ≤ Φ ≤ 0.50), and 54 as "PROGRAMMATIC" (Φ < 0.30). The highest-complexity scenario (Ivan Karamazov's "return of the ticket," Φ = 0.566) is precisely the one in which mutually inhibiting drives — rage against divine injustice, residual love for a condemned world, spite as autonomy-assertion, and cold logic — produce sustained, high-amplitude oscillation that resolves through an action harmful to the agent yet epistemologically coherent.

Crucially, this work does not claim that AXIOM-02 possesses consciousness, sentience, or phenomenal experience. It is a computational instrument for studying the structural conditions under which conflicted decision-making resists reduction to utility functions — an epistemological tool, not an ontological one. See the full disclaimer in src/README.md.

The question of what it means for a system to deliberate rather than compute has occupied cognitive science, philosophy of mind, and artificial intelligence for decades. Standard models of decision-making — whether expected-utility maximisation, reinforcement learning, or deep neural policy networks — treat conflict as noise to be minimised or as a convergence problem for optimisation. Yet human moral cognition is characterised precisely by the irreducibility of certain conflicts: situations in which an agent can identify the utility-maximising action but cannot bring itself to take it, or in which it acts against its own interests to assert autonomy, or in which it simply paralyses under the weight of competing drives.

AXIOM-02 operationalises this intuition. Rather than asking whether a system feels conflict, we ask whether its internal dynamics — under mutual inhibition, neuromodulatory gain control, epigenetic sensitivity, temporal accumulation of residue, circadian state, and fatigue — produce measurable signatures that correspond to what in biological agents we recognise as deliberation. The system is fully deterministic and auditable: every decision is traceable to the drive activations that produced it.

This approach follows a tradition of computational models of affect and cognition, including:

• Grossberg's Adaptive Resonance Theory (1976): competitive neural dynamics for cognitive-emotional integration.
• Damasio's Somatic Marker Hypothesis (1994): the role of embodied signals in guiding decision-making.
• Minsky's Emotion Machine (2006): the view of mind as a society of interacting resources.
• Rolls' Neural Basis of Emotion (2013): reinforcement-based models of affective processing.

AXIOM-02 extends these by providing a concrete, deterministic, and fully open implementation that can be run, inspected, and critiqued by any researcher.

The engine centres on a mutual-inhibition drive network of 18 drives, each with a baseline activation that is modulated by scenario parameters, neuromodulator state, epigenetic sensitivity, and associative memory residues. Drives compete through a softmax-like interaction with inhibitory coupling; the resulting activation vector is passed through an action resolver that selects from scenario-specific action options. See src/emotion_engine.py for the full implementation.

Scenario Input → Parameter Vector → Drive Activations
  → Epigenetic Modulation (long-term sensitivity)
  → Associative Memory (similar past trauma)
  → Moral Residue (cascade/temporal contamination)
  → Neuromodulator Gating (dopamine, serotonin, cortisol, norepinephrine, oxytocin)
  → Fast-Path Heuristic (hot-cognition bypass)
  → Temporal Projection (affective forecasting)
  → Drive Network Loop (20 steps mutual inhibition)
  → Action Resolution + Spite Detection
  → Embodied Hesitation, Qualia Tagging, Narrative Update
  → Consciousness Probe (8 criteria) → Verdict (PROGRAMMATIC | INDETERMINATE | CONSCIOUS)

The eight probe criteria and their definitions:

All stochastic elements use numpy.random.default_rng(seed). A fixed seed (default 42) guarantees bit-identical output across runs. The full benchmark suite can be regenerated with:

cd src && python3 report.py --seed 42

Figure 1: Distribution of Φ values across the 102-scenario suite.

Figure 2: Correlation between deadlock fraction and consciousness complexity.

For full per-scenario results, see docs/RESULTS_ANALYSIS.md. For the complete scenario registry, see docs/SCENARIO_CATALOG.md.

The data support the hypothesis that consciousness-like signatures in a deterministic system arise from drive stalemate — mutual inhibition in which no single drive can suppress its competitors. The highest-Φ scenarios are precisely those in which the agent faces genuinely irresolvable conflict: Ivan Karamazov cannot both love the world and reject its creator; Anna Karenina cannot both pursue passion and preserve social standing.

Spite scenarios (e.g., the Underground Man, Medea) produce high cortisol and norepinephrine, leading to actions that are objectively harmful yet chosen to assert autonomy over utility. This is the clearest measurable deviation from cold-logic optimisation.

This work does not claim to have created artificial consciousness, sentience, or a mind. The system does not experience deadlock, spite, or meta-frustration; it produces numerical traces that correspond structurally to states that in biological agents are associated with conscious deliberation. The mapping is epistemological, not ontological.

The project's purpose is to render the conditions under which a system appears conscious quantitatively tractable — to replace the question "is it conscious?" with the question "under what measurable conditions does a deterministic decision process resist reduction to a utility function?" Whether the resulting complexity metric captures anything philosophically relevant to consciousness is an open question; this framework is designed to make that question empirically investigable rather than purely speculative.

For the full disclaimer text, see src/README.md.

• The engine is deliberately constrained to 18 drives; real affective life involves many more.
• Neuromodulator dynamics are phenomenological (parameterised curves) rather than biophysical.
• The complexity metric is a composite of eight heuristics; alternative weightings may yield different verdicts.
• Scenarios are literary and philosophical artefacts, not experimental data from human subjects.
• Cross-cultural validity of the drive categories has not been established.
• Parameterisation. The drive interaction weights are currently parameterised based on qualitative scenario matrices rather than being dynamically learned or optimized from human empirical data.
• Scaling limit. The simulation loop is bounded at 20 steps; long-term asymptotic behavior and potential chaotic attractors under infinite iterations remain unmapped.

AXIOM-02 provides a fully deterministic, open, and auditable framework for studying the structural signatures of conflicted decision-making. With 102 benchmarked scenarios, a reproducible complexity metric, and a modular architecture that invites extension, it offers a concrete foundation for further investigation into the computational conditions under which deliberation resists reduction to optimisation.

We invite researchers to fork, extend, and critique the framework. All results can be regenerated deterministically. For citation, see CITATION.cff.

1. Grossberg, S. (1976). Adaptive pattern classification and universal recoding. Biological Cybernetics, 23(4), 187–202.
2. Damasio, A. R. (1994). Descartes' Error: Emotion, Reason, and the Human Brain. Putnam.
3. Minsky, M. (2006). The Emotion Machine. Simon & Schuster.
4. Rolls, E. T. (2013). Emotion and Decision-Making Explained. Oxford University Press.
5. Zierax (2026). AXIOM-02: A Deterministic Simulation Engine for Modeling High-Stakes Cognitive Dissonance. GitHub: https://github.com/Zierax/Axiom-02

├── LICENSE              CC BY-NC 4.0
├── CITATION.cff         Citation metadata
├── pyproject.toml       Python package metadata
├── requirements.txt     Dependencies (numpy, scipy, matplotlib)
├── src/                 Source code
│   ├── README.md        Code documentation and disclaimer
│   ├── report.py        Benchmark runner
│   ├── main.py          CLI entry points
│   ├── emotion_engine.py, consciousness_probe.py, ...
│   ├── scenarios/       102 scenario definitions
│   └── _benchmarks/     Regenerated results and charts
└── docs/                Detailed analysis
    ├── RESULTS_ANALYSIS.md
    ├── METRICS_DEEP_DIVE.md
    ├── SCENARIO_CATALOG.md
    └── AXIOM_GUIDE.md