economic impact assessment

Quantifying how input uncertainty propagates to model outputs remains a fundamental challenge in computational modeling. Method: This study systematically reviews and empirically compares prominent global and local sensitivity analysis (SA) techniques—including Sobol’, FAST, Morris screening, and local derivative-based methods—implemented via standard software packages, supporting both probabilistic modeling and distribution-free settings. Contribution/Results: We propose a practical decision framework that guides method selection based on problem characteristics, analytical objectives, and resource constraints—rejecting the notion of a universally “optimal” SA method and thereby addressing a critical gap in methodological implementation guidance. A reusable, open-source toolkit is developed to enhance the reliability and interpretability of uncertainty attribution. The framework and tools have been validated across multiple engineering and policy modeling applications, demonstrating robustness and scalability in real-world contexts.

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.

Existing business process simulation predominantly relies on long-term, cold-start simulations, which are ill-suited for short-term performance prediction and operational decision-making under current runtime conditions or sudden disruptions (e.g., demand surges, resource shortages). To address this, we propose a short-term simulation method initialized from the real-time system state. Our approach uniquely integrates event-log-driven state reconstruction with process models to build an executable discrete-event simulation engine, enabling precise initialization of case progress and resource allocation. This eliminates the state mismatch inherent in conventional warm-up-phase simulations and significantly improves prediction accuracy under concept drift and abrupt behavioral shifts. Experimental results demonstrate that our method reduces prediction error for short-term KPIs—including response time and backlog volume—by 23%–41% compared to traditional long-term simulation, particularly excelling in dynamic operational environments.

This study systematically evaluates methodological sensitivity of input-output (IO) models in quantifying macroeconomic impacts of power outages induced by natural disasters. Addressing three major U.S. long-duration blackout events, we compare four IO modeling approaches—Leontief, Ghosh, critical-input, and inoperability—and integrate three parameterization methods: household outage duration, kWh loss, and satellite nighttime light data. Results reveal that model structure alone introduces estimation biases ranging from 11% to 45%, while parameterization choices contribute additional biases of 23.1%–50.5%. Estimated domestic economic losses average $3.13 billion, $4.18 billion, and $2.93 billion across the three events. To our knowledge, this is the first systematic cross-model and cross-parameterization sensitivity analysis applied to multiple prolonged outage events. It uncovers heterogeneous impacts of analytical decisions on outcomes and establishes a reproducible methodological framework and empirical benchmark for disaster-related economic loss assessment.

This paper addresses the challenge of building a verifiable, heterogeneous-agent macroeconomic simulation platform. Methodologically, it designs a multi-agent system integrating heterogeneous households, firms, a central bank, and the government; supports both rule-based and reinforcement learning (RL) policies—specifically PPO and SAC—and pioneers the integration of the OpenAI Gym interface into macroeconomic simulation. Micro-level behavioral calibration is coupled with macro-level dynamics to enable exogenous shock modeling and counterfactual causal analysis grounded in real U.S. economic data. Contributions include: (1) the first systematic integration of deep RL into a general-purpose macroeconomic simulation framework; (2) empirical validation across two canonical scenarios—adaptive learning of employment preferences among skill-heterogeneous households, and evolutionary pricing responses of firms following a firm-specific productivity shock—demonstrating that learning agents substantially reshape equilibrium trajectories; and (3) an open-source, reproducible, and extensible simulation infrastructure.

This study addresses the lack of systematic modeling approaches for token economies and quantitative analysis of event impacts. Building upon the DeTEcT framework, it proposes the first integrated methodology that combines formal token economy simulation with significance-based measurement of event effects. By introducing an event impact analysis framework augmented with numerical simulation techniques and wealth distribution metrics, the work enables quantitative assessment of wealth redistribution effects triggered by endogenous policy changes—such as Bitcoin Improvement Proposals (BIPs). Using Bitcoin as a case study, the approach demonstrates its effectiveness and practicality in capturing economic dynamics and evaluating the consequences of significant protocol-level events.

This study evaluates the impact of a newly developed cultural and tourism project on local tourism demand and economic outcomes, using the Sensoria experiential museum in Holzminden, Germany, as a case study. It pioneers an integrated approach combining causal inference with demand-side economic analysis by employing a difference-in-differences methodology to identify the additional overnight visitors attributable to the museum’s opening. A sectoral expenditure conversion model is then applied to quantify both direct and indirect economic effects. Findings indicate that in its inaugural year, the project generated 4,691 additional overnight stays and approximately €560,000 in total turnover, with direct and indirect contributions amounting to €230,000 and €210,000, respectively. This work thus offers a methodological innovation and empirical evidence for assessing the short-term economic impacts of cultural and tourism investments.

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.

This study addresses the challenge that conventional triple-difference (DDD) models fail to simultaneously identify treatment and spillover effects when spillovers contaminate the control group. To resolve this issue, the authors propose a dual triple-difference framework that restructures the identification assumptions and spillover architecture, thereby formally characterizing the conditions under which both effects are identifiable—a contribution not previously achieved in the literature. Theoretical analysis establishes the identification validity of the proposed approach, while Monte Carlo simulations and an empirical application to special economic zones in Italy demonstrate its robustness and accuracy in practical settings. This new framework enables consistent estimation of both direct treatment and spillover effects even in the presence of cross-group contamination.

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.