Wei-Xing Zhou

h-index8
2papers

2 Papers

10.9SOC-PHApr 21
On the existence of Ulanowicz's optimal structural resilience in complex networks

Si-Yao Wei, Wei-Xing Zhou

This study provides a foundational theoretical investigation into the mathematical existence and asymptotic properties of Ulanowicz's structural resilience. While ecological evidence suggests that sustainable systems gravitate toward an optimal efficiency-redundancy balance at $α= 1/\mathrm{e}$, the mathematical attainability of this configuration across broader network topologies remains unverified. We rigorously prove that while optimal resilience is structurally unattainable in two-node networks, there exists at least one optimal flow configuration within the feasible probability space for any weighted and directed network with the network size $N_\mathcal{V} \geq 3$ and no self-loops. To make the derivations analytically tractable, we introduce a parameterized symmetric network model with uniform marginal distributions. Using this stylized ansatz, our analytical and numerical results reveal that maintaining the optimal state requires distinct asymptotic scaling behaviors as $N_\mathcal{V}$ increases: adjacent primary links scale as $O(N_\mathcal{V}^{-1})$, whereas non-adjacent background links exhibit a steeper quadratic decay of $O(N_\mathcal{V}^{-2})$ with specific logarithmic corrections. Rather than serving as an immediate engineering tool, this work establishes a rigorous mathematical boundary for the optimal resilience framework, demonstrating analytically how an optimally resilient system differentiates into high-throughput primary channels and sparse redundancy pathways.

RMMar 3, 2025
Dynamic spillovers and investment strategies across artificial intelligence ETFs, artificial intelligence tokens, and green markets

Ying-Hui Shao, Yan-Hong Yang, Han-Xian Zhou et al.

This paper investigates the risk spillovers among AI ETFs, AI tokens, and green markets using the R2 decomposition method. We reveal several key insights. First, the overall transmission connectedness index (TCI) closely aligns with the contemporaneous TCI, while the lagged TCI is significantly lower. Second, AI ETFs and clean energy act as risk transmitters, whereas AI tokens and green bond function as risk receivers. Third, AI tokens are difficult to hedge and provide limited hedging ability compared to AI ETFs and green assets. However, multivariate portfolios effectively reduce AI tokens investment risk. Among them, the minimum correlation portfolio outperforms the minimum variance and minimum connectedness portfolios.