Entangled Holobiont Mapper

Couples microbiome–gut–brain axis, quantum biology, and multilayer networks for privacy-preserving federated learning across endosymbiotic scales.

EvezArt c8a77b2 1.4 KB Updated

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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

  1. register_agent(id, local_params) — add federated participant
  2. compute_joint_phi(host_adj, microbiome_adj, coupling) — get holobiont state
  3. federated_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.

EvezArt/evez-skills/tree/main/skills/entangled-holobiont-mapper commit c8a77b2f33

Frequently asked questions

npx skillmds@latest add evezart/entangled-holobiont-mapper