| name | surviving-by-serving-sbs |
| description | Surviving by Serving (SBS) principle - functional relevance drives self-organization in complex adaptive systems with multi-agent resource transformation |
| version | 1 |
| authors | ["Claus Metzner","Ali Ghebleh","Achim Schilling","Andreas Maier","Thomas Kinfe","Patrick Krauss"] |
| arxiv_id | 2606.26733 |
| date | 2026-06-25T00:00:00.000Z |
| categories | ["q-bio.NC","cs.NE","nlin.AO"] |
| tags | ["self-organization","complex adaptive systems","functional relevance","multi-agent systems","emergent networks","transformation chains","core-periphery"] |
| status | active |
| activation_keywords | ["surviving by serving","SBS principle","functional relevance","self-organization","complex adaptive systems","multi-agent resource transformation","emergent interaction networks"] |
Surviving by Serving: Functional Relevance Drives Self-Organization in Complex Adaptive Systems
Core Principle
Surviving by Serving (SBS): Components persist as long as their outputs are utilized by other components; prolonged non-utilization promotes adaptation and exploration.
This provides a substrate-independent mechanism for emergence and stabilization of organized structure in complex adaptive systems.
Key Methodology
Minimal Multi-Agent Model
- Agents transform shared resources
- Receive only local feedback when outputs are utilized elsewhere
- No global objectives — self-organization emerges spontaneously
Emergent Phenomena
- Functional interaction networks — stable structures arise without external selection
- Transformation chains — sequential resource processing emerges
- Core-periphery organization — hierarchical network structure
- Novel state generation — previously inaccessible target conditions become reachable
Pre-Adaptive Search Phase
Self-sustaining interaction networks arise without external selection, creating a exploration phase from which functional solutions later emerge.
Computational Framework
Agent Feedback Mechanism
Agent Output → Utilization Check → Local Feedback
if utilized: persist/maintain current strategy
if not utilized: adapt/explore new strategies
Resource Transformation Dynamics
- Shared pool of resources
- Agents as transformation functions
- Output utilization determines survival/adaptation
Neuroscience Applications
Neural Network Self-Organization
- Neurons as agents
- Synaptic outputs as resources
- Functional connectivity emerges from utilization patterns
- Core-periphery matches cortical organization
Applicable Concepts
- Spike-driven plasticity — utilization feedback shapes connections
- Critical dynamics — transformation chains resemble avalanche cascades
- Hebbian-like emergence — co-utilization strengthens connections
Key Insights
Substrate-Independent Principle
SBS applies across:
- Biological neural networks
- Artificial neural networks
