input-output modeling

This paper examines how firms strategically form supply relationships in general equilibrium to maximize profits, and analyzes the implications for production network structure, social welfare, and supply chain resilience. Method: We develop a production network formation game that integrates micro-level firm strategies with macro-level network dynamics, proving that profit maximization is equivalent to increasing a firm’s eigenvector centrality in the input-output network. We introduce link-based trade interventions as a Nash equilibrium selection mechanism and systematically model the interplay among strategic choices, network topology evolution, social efficiency, and shock propagation. Contribution/Results: We characterize welfare disparities across multiple equilibria, quantify the cost effects of network complexity and the positive spillovers of input diversification, and demonstrate that targeted link interventions can efficiently steer the system toward high-welfare equilibria—providing the first unified framework linking firm strategy, network formation, efficiency, and resilience.

This paper investigates how heterogeneous firms autonomously determine prices, output levels, and upstream input procurement (type and quantity) via simplified reinforcement learning—without assuming market equilibrium or possessing complete technological information—thereby endogenously evolving stable yet reconfigurable production networks. Method: We develop a generative model grounded in dynamic input-output interactions, wherein firms learn and optimize solely from local feedback signals. Contribution/Results: Our approach is the first to achieve fully endogenous evolution of production networks without global equilibrium or perfect information assumptions. The model supports adaptive network reconfiguration under multiple exogenous shocks—including demand fluctuations, node exit, productivity shocks, and technological upgrades. Simulation results successfully replicate empirically observed hierarchical and modular structures of real-world supply chains and quantitatively identify asymmetric shock propagation across upstream and downstream tiers.

This paper addresses the challenge of multiscale modeling in macroeconomic systems by proposing a novel economic modeling framework grounded in multiport network theory. Methodologically, economic agents are mapped to ports, commodity flows are analogized to electrical currents, and incentive mechanisms to voltages; macrodynamic behavior emerges rigorously from micro-level interactions via port coupling. For the first time, the circuit-theoretic multiport paradigm is systematically imported into economics, and an analytically tractable, scalable cross-scale dynamic model is constructed using LTSpice simulation. The key contributions are: (1) establishing a theoretically consistent micro–macro bridge; (2) validating the framework across hierarchical scales—from Robinson Crusoe–style isolated economies to full national economies; and (3) demonstrating that macroeconomic phenomena can be strictly derived from microscopic port interactions. This work provides a new paradigm for mechanistic interpretation and policy simulation in complex economic systems.

This paper investigates how supply chain disruptions propagate across goods and final consumers in multi-sector international production networks, focusing on how node positions and network structural features shape systemic vulnerability. We develop a parsimonious model integrating production network theory with multi-sector general equilibrium, employing analytical derivation and comparative statics. Our results show: (1) Disruption shocks exhibit a “sharp short-term, decaying long-term” dynamic; (2) Node centrality and network complexity significantly amplify global welfare losses from localized disruptions; (3) Declining transport costs reduce disruption frequency but reinforce specialization, thereby exacerbating negative spillovers from single-node failures; (4) Countries can acquire asymmetric economic influence through control over production and strategic trade quotas. The study provides a structured theoretical benchmark for assessing globalization-related risks and designing supply chain resilience policies.

This paper investigates the amplification mechanism of final-demand shocks as they propagate upstream in supply chains, focusing on the interplay among firms’ industry positions, inventory decisions, and network topology. Method: We develop a structural production network model incorporating dynamic inventory behavior, introduce the novel “destination exposure” metric to quantify upstream vulnerability to downstream demand shifts, and employ a shift-share identification strategy. Contribution/Results: We theoretically establish inventory as a critical transmission channel of the bullwhip effect. Empirically, upstream output elasticity is three times that of final manufacturers, with inventory dynamics accounting for an average 18% of this amplification. Moreover, we demonstrate that vertical supply-chain extension and elevated inventory levels jointly exacerbate aggregate macroeconomic volatility. By endogenizing both inventory cycles and network topology within the shock-propagation mechanism—first in the literature—our framework provides a new paradigm linking micro-level supply-chain resilience to macroeconomic fluctuations.

This study uncovers the network-topological origins of the bullwhip effect, addressing demand uncertainty and risk cascades in supply chains. Building upon stochastic networks and the newsvendor model, it develops a quantitative framework for analyzing multistage supply chains under both equilibrium and transient dynamics. The framework integrates nonlinear price elasticity and dynamic rebalancing of trade relationships to characterize system degradation driven jointly by decentralized decision-making and physical bottlenecks. Innovatively, the bullwhip effect is attributed to intrinsic network structure, market-clearing feedback is endogenized, and stockouts are modeled as a multidimensional Skorokhod reflection problem. Empirical analysis of global oil trade data demonstrates that localized chokepoint disruptions—such as those at the Strait of Hormuz—can trigger nonlinear cascading shortages and induce systemic reallocation of national reserves.

This study investigates how tariff shocks from the U.S.–China trade war propagate cascading disruptions through the global oilseed supply chain and evaluates the effectiveness of mitigation mechanisms such as trade reallocation and production capacity expansion. The authors innovatively integrate an input–output model with linear programming to construct a unified system-equilibrium framework that endogenously incorporates trade reallocation and production expansion strategies, and they develop an efficient algorithm to compute large-scale approximate equilibria that minimize unmet demand. Counterfactual simulations reveal that a 70% disruption in U.S.–China oilseed trade reduces global output by 3.27%, with China bearing a 14.02% loss. Redirecting 20% of Brazil’s exports toward China lowers global losses to 1.36% but intensifies domestic supply–demand imbalances in Brazil, highlighting the asymmetric distribution of economic costs and inherent trade-offs across nations in implementing mitigation strategies.

This study addresses the challenge of reconstructing inter-firm production networks when firm-level data are unavailable and existing methods fail to accurately capture sectoral input-output structures. The authors propose a novel approach that embeds macro-level input-output constraints into a maximum entropy framework for reconstructing both binary and weighted firm-level networks. They derive, for the first time, analytical solutions that satisfy these aggregate constraints exactly. The method substantially improves fidelity to the observed macroeconomic structure, achieving near-perfect alignment with benchmark input-output tables and significantly reducing structural discrepancies compared to conventional reconstruction techniques. However, while excelling in preserving aggregate industry-level flows, the approach still faces limitations in replicating other topological properties of real-world production networks.

This study addresses the challenge that existing methods struggle to simultaneously reconcile the micro-level structure of enterprise supply chains with macroeconomic input-output tables. To bridge this gap, the authors propose an efficient synthetic network generation approach based solely on publicly available data. By integrating inter-firm connection topology with macroeconomic input-output constraints, the method achieves—without requiring proprietary information—the first scalable and reproducible synthetic supply network that maintains both microscopic realism and macroscopic consistency. The resulting networks accurately replicate key statistical properties observed in real-world data, thereby providing high-quality foundational inputs for large-scale economic modeling.

该文综述了最优传输理论在经济学中的应用，通过Kantorovich对偶等方法解决资源配置、均衡及计算问题，并探讨其作为匹配模型和工具的角色。