arXiv subjects

Zebiao Li

Publications and source records attributed to Zebiao Li.

5 recordsLinked to original sources

arXiv2026

Mathematical Modeling of Common-Pool Resources: A Comprehensive Review of Bioeconomics, Strategic Interaction, and Complex Adaptive Systems

The governance of common-pool resources-resource systems characterized by high subtractability of yield and difficulty of exclusion-constitutes one of the most persistent and intricate challenges in the fields of economics, ecology, and applied mathematics. This comprehensive review delineates the historical and theoretical evolution of the mathematical frameworks developed to analyze, predict, and manage these systems. We trace the intellectual trajectory from the early, deterministic bioeconomic models of the mid-20th century, which established the fundamental tension between individual profit maximization and collective efficiency, to the contemporary era of complex coupled human-environment system models. Our analysis systematically dissects the formalization of the "Tragedy of the Commons" through the lens of classical cooperative and non-cooperative game theory, examining how the N-person Prisoner's Dilemma and Nash Equilibrium concepts provided the initial, albeit pessimistic, predictive baseline. We subsequently explore the "Ostrom Turn," which necessitated the integration of institutional realism-specifically monitoring, graduated sanctions, and communication-into formal game-theoretic structures. The review further investigates the relaxation of rationality assumptions via evolutionary game theory and behavioral economics, highlighting the destabilizing roles of prospect theory and hyperbolic discounting. Finally, we synthesize recent advances in stochastic differential equations and agent-based computational economics, which capture the critical roles of spatial heterogeneity, noise-induced regime shifts, and early warning signals of collapse. By unifying these diverse mathematical threads, this review elucidates the shifting paradigm from static optimization to dynamic resilience in the management of the commons.

econ.GN↗

arXiv2026

The Global Food Trade Network as a Complex Adaptive System: A Review of Structure, Evolution, and Resilience

The global food system has metamorphosed from a loose aggregation of bilateral exchanges into a highly intricate, interdependent Global Food Trade Network (FTN). This comprehensive review synthesizes the extant literature to examine the FTN through the rigorous lens of complex network science, moving beyond traditional economic trade models to quantify the system's topological architecture. We delineate the network's historical transition from a unipolar, efficiency-driven system dominated by Western hegemony to a multipolar, regionalized structure characterized by high clustering and scale-free heterogeneity. Special emphasis is placed on the dual nature of connectivity, which functions simultaneously as a buffer against local production variances and a conduit for global contagion. By conceptualizing the FTN as a multiplex system-distinguishing between the robust topology of wheat, the brittle regionalism of rice, and the polarized "dumbbell" structure of soy-we elucidate the distinct structural vulnerabilities inherent in modern food security. Furthermore, we analyze the impact of recent high-magnitude shocks, specifically the COVID-19 pandemic and the Russia-Ukraine conflict, illustrating the critical trade-off between logistical efficiency and systemic resilience. The review concludes by assessing the future trajectory of the network under anthropogenic climate change, predicting a poleward migration of comparative advantage that necessitates a paradigm shift from isolationist protectionism to cooperative network redundancy.

econ.TH↗

arXiv2026

Compressing Complexity: A Critical Synthesis of Structural, Analytical, and Data-Driven Dimensionality Reduction in Dynamical Networks

The contemporary scientific landscape is characterized by a "curse of dimensionality," where our capacity to collect high-dimensional network data frequently outstrips our ability to computationally simulate or intuitively comprehend the underlying dynamics. This review provides a comprehensive synthesis of the methodologies developed to resolve this paradox by extracting low-dimensional "macroscopic theories" from complex systems. We classify these approaches into three distinct methodological lineages: Structural Coarse-Graining, which utilizes spectral and topological renormalization to physically contract the network graph; Analytical-Based Reduction, which employs rigorous ansatzes (such as Watanabe-Strogatz and Ott-Antonsen) and moment closures to derive reduced differential equations ; and Data-Driven Reduction, which leverages manifold learning and operator-theoretic frameworks (e.g., Koopman analysis) to infer latent dynamics from observational trajectories. We posit that the selection of a reduction strategy is governed by a fundamental "No Free Lunch" theorem, establishing a Pareto frontier between computational tractability and physical fidelity. Furthermore, we identify a growing epistemological schism between equation-based derivations that preserve causal mechanisms and black-box inference that prioritizes prediction. We conclude by discussing emerging frontiers, specifically the necessity of Higher-Order Laplacian Renormalization for simplicial complexes and the development of hybrid "Scientific Machine Learning" architectures-such as Neural ODEs-that fuse analytical priors with deep learning to solve the closure problem.

physics.gen-ph↗

arXiv2021

One-step synthesis of mesoporous Cobalt sulfides (CoSx) on the metal substrate as an efficient bifunctional electrode for overall water splitting

Electrocatalysts based on transition metal sulfides are drawing accelerating concerns in renewable energy research because of their intrinsically excellent activities towards both hydrogen evolution reaction and oxygen evolution reaction. To date, considerable efforts are made to improve the performance of these catalysts, but ignoring the improper synthesis strategy would incur additional cost to the catalyst. Herein, a convenient, one-step anodization method is developed for fast construction of cobalt sulfides. Without any high-temperature or long-time treatment, mesoporous CoSx is self-grown on the metal substrate in minutes. As a result, as-anodic CoSx requires overpotentials of 102 mV for HER and 284 mV for OER to achieve a current density of 10 mA m-2 in alkaline solution. Moreover, the tandem bifunctional as-anodic CoSx exhibits a required cell voltage of 1.64 V for overall water splitting in alkaline solution, exceeding most of the documented Co-based electrocatalysts.

physics.app-ph↗

arXiv2019

Green low-cost carbon nanodots-polyurethane composites with novel anisotropic anti-quenching mechanism for strain sensing

A new type of nontoxic low-cost sensor is reported here, whose photoluminescence (PL) intensity is instantly responsive to the external strain applied over a large range (up to 250% strain). Highly stretchable fluorescent composites of carbon dots (CDs) and polyurethane (PU) are fabricated via a scalable green chemistry method by conveniently dispersing CDs in the aqueous solution of PU. It is discovered that, upon tensile deformation, the PL peak of the CD-PU film remains non-shifted but displays varied intensity. The observed PL responses to strain are ascribed to the enlarged inter-particle distance of CDs along the tensile direction (z axis), although a higher degree of aggregation is resulted in the other two axes. The PL-dependence on the anisotropic patterns of CDs in solid state points to a new mechanism to overcome aggregation-induced quenching by controlling the distribution behaviors of the fluorescent species.

physics.app-ph↗