The Layered Architecture of DAM
Structure is not static — it is rhythm, feedback, and flow.
From Axioms to Architecture
DAM is not built as a single block.
It emerges as a layered, evolving structure — each layer processing, responding, and feeding into the others.
This page introduces the core architecture:
how DAM embodies its axioms in dynamic layers of form and function.
The Six Core Layers of DAM
1. Entity Layer
“Who exists in the system?”
This layer defines the individual units or agents (Xᵢ) in the model.
They hold state, receive input, and express change.
Each is adaptive, and can evolve its own behavior over time.
- Example: neuron, trader, organism, policy
- Mathematical form:
Xᵢ(t)= state vector at time t
2. Relational Layer
“How are they connected?”
Entities don’t live in isolation.
This layer encodes the strength, direction, and type of interactions between them.
- Expressed via
θᵢⱼ(t)→ dynamic connection strength - Network topologies can evolve over time
- Adaptivity may change the graph structure itself
3. Temporal Layer
“What is the system’s rhythm of change?”
Time in DAM is not fixed.
Each entity has its own temporal density τᵢ(t), determined by entropy, feedback, and tension.
- Time can contract, dilate, or reorient locally
- Learning rates, responsiveness, and delay are linked to this layer
4. Energetic Layer
“What sustains or disrupts the system?”
This layer models energy, entropy, and cost dynamics — the thermodynamics of adaptation.
- Entities spend or accumulate energy
- System-wide stress increases entropy
- Optimization seeks low-energy/high-coherence states
5. Goal and Strategy Layer
“What does the system want?”
Each entity (or system as a whole) may have a shifting goal vector Hᵢ(t) — which guides learning, adaptation, and trajectory.
- Objectives may evolve
- Goals interact with environmental feedback
- Strategies are not fixed — they are learned over time
6. Meta-Evolution Layer
“How does the system update how it updates?”
The deepest layer.
This governs the evolution of the evolution rules.
It controls how the model modifies its own learning rates, adaptation thresholds, or architectural configurations.
R(t)defines the adaptive ruleset- Can be context-sensitive or error-driven
- This layer allows DAM to rewire its own logic
All Layers Are Interlinked
These are not separate modules — they are interpenetrating flows:
- Changing the entity state alters energy consumption
- Shifting goals reshape network dynamics
- High entropy can trigger meta-evolution
- Temporal irregularity feeds back into learning
DAM is not a stack — it is a loop of loops.
Visual Schema (coming)
A diagram showing:
[Entities] ↔ [Relations]
↓ ↑
[Time] ↔ [Energy]
↓ ↑
[Goals] ↔ [Meta-rules]
Explore Further
/organism/simulations→ Watch the architecture in motion/interface/systems-in-skin→ How structure translates into experience/atelier→ Prototype your own DAM instance