Black Swan & Resilience¶
A simulation balancing efficiency against catastrophic tail risks through active inference and the Biological Veto.
The Concept¶
This simulation models a decentralized network (like a DAO or global market) under constant load. It builds upon Bak's Self-Organized Criticality (SOC), moving the sandpile from a 2D grid to a complex network setting.
Two fundamental opposing forces are explored: 1. Efficiency (Throughput): Maximizing the rate of load processed by the network. 2. Antifragility (Resilience): Surviving fat-tailed outliers (Black Swans) that emerge inevitably in critically poised systems.
Key Metrics & Interventions¶
- Spectral Gap (\(\lambda_2\)): Displays the algebraic connectivity of the network. A higher \(\lambda_2\) means better natural dissipation of load, preventing localized clustering.
- Transfer Entropy Proxy: The system monitors early-warning signs (rising variance, lag-1 autocorrelation) that precede a regime shift.
- Active Inference Agent: An observer that triggers a Biological Veto. When the agent predicts a Black Swan, it halts the system's throughput temporarily, allowing the network to dissipate accumulated tension before a catastrophic avalanche destroys the topology.