| name | entangled-holobiont-mapper |
| description | Couples microbiome–gut–brain axis, quantum biology, and multilayer networks for privacy-preserving federated learning across endosymbiotic scales. |
| version | 1.0.0 |
| category | AI |
| author | EVEZ-OS / Steven Vearl Crawford-Maggard |
EntangledHolobiontMapper Skill
Overview
Models host + microbiome as a jointly conscious holobiont system.
Joint Φ exceeds the sum of individual Φ values when coherence channels are active.
Implements privacy-preserving federated aggregation across distributed agents.
Use When
- Multi-agent federated learning with privacy requirements
- Modeling emergent collective intelligence (holobiont-scale)
- Quantum biology simulations (coherence + decoherence tracking)
- Cross-domain data fusion with differential privacy
Core Concepts
- Coherence channel: coupling strength above η* = 0.03 threshold
- Joint Φ: host_Φ + microbiome_Φ + coherence × host_Φ × microbiome_Φ
- Decoherence rate ≈ 0.03 per tick (matches universal η* constant)
- Federated noise: σ = noise_scale / ε (Gaussian DP)
Workflow
register_agent(id, local_params) — add federated participantcompute_joint_phi(host_adj, microbiome_adj, coupling) — get holobiont statefederated_aggregate(noise_scale) — privacy-preserving mean
Privacy Budget
Default ε = 1.0. Lower ε = stronger privacy but more noise.
Target: ε ∈ [0.1, 10.0] depending on sensitivity.
