Research Initiative · 2023 – 2033

The Algoplexity Research Program

Decoding the physics of intelligence in complex adaptive systems through Algorithmic Cognitive Science.

Status: Active Field: Algorithmic Cognitive Science Framework: UAI · QCEA · NL · AID

🌌 Mission

Algoplexity is a long-term research initiative dedicated to establishing a new field of study: Algorithmic Cognitive Systems (ACS). We posit that complex adaptive systems—whether financial markets, biological swarms, or social institutions—are not merely stochastic processes. They are Distributed Computational Entities possessing system-level cognition.

Our mission is to develop the Theory, Instrumentation, and Agency required to understand and navigate the "Dancing Landscape" of these systems.

The Grand Synthesis — The Cybernetic Intelligence Protocol

Our work unifies seven foundational theories into a coherent engineering framework (The "Tower" Hypothesis):

AID

Algorithmic Information Dynamics

The physics of causal structure and complexity. (Zenil et al.)

UAI

Universal Artificial Intelligence

The mathematics of optimal general intelligence. (Hutter)

NL

Nested Learning Theory

Hierarchical optimization and continuum memory. (Behrouz)

QCEA

Quantum-Complex-Entropic-Adaptive

The thermodynamics of strategic viability. (Williams)

EV

Entropic Valuation

Value reconstruction based on Epistemic Fragility dH/dτ rather than temporal decay.

Λ/ηK

Coherence Theory

Rigorous condition for eco-evolutionary stability. Regime collapse as a deterministic threshold. (Williams)

GNCA

Graph Neural Cellular Automata

The topology of distributed computation. (Grattarola)

🗺️ 10-Year Roadmap (2023–2033)

We execute a "Reduction-Synthesis" cycle: zooming in from the macro-swarm to the atomic unit of cognition (The Agent) to solve Perception, then zooming out to model the distributed network (The Society).

Horizon Focus Cognitive Scale The Scientific Goal Artifact
Foundation Ontology The Swarm Existence Proof. Genetic Algorithms prove markets are algorithmic, not random. Master's Thesis
Horizon 0 Sensation The Nerve The Somatic Marker. Detecting Coherence Loss (Λ > ηK) via predictive error spikes. Coherence Meter
Horizon 1 Perception The Neuron The Cognitive EEG. Measuring Environmental Drift (Λ) by compressing the computational regime. Comp. Phase Transitions
Horizon 2 Current Agency The Agent The Reflective Mind. Implementing the Coherence Veto (System 0) when Λ ≫ ηK. QCEA-AIXI Agent
Horizon 3 Society The Hive Mind Collective Intelligence. Modeling Systemic Coherence Loss caused by synchronized update rules. Hive Mind

📚 Scientific Findings

Foundation — The Existence Proof (2023)

Financial markets contain discoverable algorithmic structures (Rule 131, Pair 35/115) invisible to standard econometrics. Method: Genetic Algorithms + MILS Encoding.

→ Master's Thesis

Horizon 0 — The Somatic Marker (2025a)

"Less is More." High-resolution multivariate models (VAR) fail to detect breaks due to parameter explosion. A simple univariate proxy (Predictive Error) works better.

→ SSRN Working Paper

Horizon 1 — The Cognitive EEG (2025b)

Taxonomy of Cognitive Failure: Cognitive Saturation (Rule 54 / Colliding Solitons) — the market "thinks itself into a corner"; Cognitive Overload (Rule 60 / Fractal Shattering) — the shock outruns the mixing time.

−29.95% early-warning lead time on out-of-sample data

Horizon 2 — The Reflective Physicist (Current)

Entropic Valuation & Plasticity. An agent adjusting its "Wingspan" (Uncertainty) based on the Physicist's diagnosis outperforms statistical baselines. Implements three nested systems: System 0 (Coherence Veto), System 1 (Iron Dome), System 2 (The Physicist).

+19.0% over statistical baselines

Horizon 3 — The Graph-Theoretic Future

Hypothesis: Systemic Risk is a GNCA. A market crash is the propagation of a specific computational state (Rule 54/Default) across the asset topology. Engineering Deep GNCA Titans — graph nodes that are self-referential learners.

→ Hive Mind Repository

📊 Open Datasets

All Horizons operate on immutable scientific benchmarks hosted on Hugging Face for unassailable reproducibility.

Structural Break Benchmark

1D Time Series corpus used across Horizon 0 and Horizon 1 experiments.

View on Hugging Face →

QCEA Adaptive Agent Benchmark

2D "Dancing Landscape" corpus for Horizon 2 agent evaluation.

View on Hugging Face →

🔧 Repositories

🔗 Citation

If you use the Algoplexity framework or datasets in your research, please cite the meta-program:

@misc{algoplexity_program,
  author    = {Mak, Yeu Wen},
  title     = {The Algoplexity Research Program: Foundations of
               Algorithmic Cognitive Systems},
  year      = {2025},
  publisher = {GitHub},
  journal   = {GitHub repository},
  howpublished = {\url{https://github.com/algoplexity/algoplexity}}
}