A Physical Theory of National Resilience: Synthetic and Processed Data for Replication

Synthetic and processed empirical data supporting the manuscript "A Physical Theory of National Resilience." The dataset includes code and data to replicate key findings and figures, such as phase diagrams and early-warning signal analysis. It was authored by Feng Xie and archived on Harvard Dataverse in May 2026.

Use Cases

• Replicating phase diagrams based on the Sovereignty Strength Index (SSI) and Wealth-Guardian Entropy (H_g) variables.
• Analyzing synthetic nation trajectories based on the National Resilience Surplus (NRS) theory and its derivatives.
• Validating pre-crisis early-warning signals based on processed data for U.S. household debt-to-GDP and the S&P 500 index.

Strengths

• Includes all synthetic and processed data necessary for full replication of the manuscript's key findings.
• Contains core Python code implementing stochastic differential equations for the National Resilience Surplus theory.
• Archived under the Global Thought Laboratory Protocol, which suggests a focus on transparency and reproducibility.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The description mentions an active development repository on GitHub, indicating the Dataverse version may be a static snapshot.

Provenance

Source

Harvard Dataverse

Collection Method

Synthetic data generation via stochastic differential equations and processing of empirical financial data.

Time Range

Synthetic data covers 2000-2023; empirical analysis focuses on the 2008 financial crisis.

Freshness

Last updated 2026-05-13 16:13:31; freshness should be verified.

Geography

Empirical analysis includes U.S. data; synthetic data appears to be global in scope.