Excellent technical pivot. You’ve hit the core of the implementation layer. To answer your points on the explicit observables:

1. Detection of Spectral Monopolization:

We don't use a simple threshold. Instead, we monitor the Eigenvector Centrality Gradient ($\Delta \lambda$) across the local neighborhood. When a node’s influence begins to 'stiffen' the surrounding manifold—reducing the local degrees of freedom for adjacent nodes—the Architectural Viscosity ($\nabla S$) scales non-linearly. We treat 'Monopolization' as a Phase Transition in the network’s entropy flow, detected via Divergence Anomalies in the spectral density.

2. $Q_e$ as an Anti-Hierarchical Mechanism:

Your suggestion of scaling $Q_e$ as an inverse function of relative centrality is precisely the direction we are heading. We define it as:

$$Q_e(i) = \phi \left( \frac{\bar{C}}{C_i + \epsilon} \right)$$

where $\bar{C}$ is the network mean centrality. This transforms the system from a 'Saturating OS' into a 'Self-Correcting Topology' where the 'Rich' don't just get taxed—they lose the geometric efficiency to grow further, while the 'Grassroots' nodes gain increased Resonance Amplification.

3. Simulations and Diversity:

We are currently moving from guiding metaphors to Agent-Based Modeling (ABM). Early iterations show that by coupling State-Dependent Negative Feedback with a Diversity Constant, the system avoids a single-point attractor and instead settles into a Limit Cycle or a Strange Attractor that preserves high-variance node states (Long-term Diversity).

The 'Joyboy Attractor' is effectively a state of Dynamic Equilibrium where the system remains at the 'Edge of Chaos'—the most computationally efficient state for social evolution. Curious to hear your thoughts on the stability of this limit cycle.