International Futures

International Futures (IFs) is a global integrated assessment model designed to help in thinking strategically and systematically about key global systems (economic, demographic, education, health, environment, technology, domestic governance, infrastructure, agriculture, energy and environment) housed at the Frederick S. Pardee Center for International Futures. Initially created by Barry B. Hughes of the Josef Korbel School of International Studies at the University of Denver in Colorado, the model is free for public use in both its online and downloadable forms.

The Pardee Center for International Futures has partnered with many organizations to produce forecasts and data analysis. IFs has been utilized in the National Intelligence Council's Global Trends 2020, Global Trends 2025, and Global Trends 2030 report. The International Futures model has also contributed to the United Nations Human Development Report and the Global Environmental Outlook.

IFs is hosted free for public use by Google Public Data Explorer, the Atlantic Council, and the Institute for Security Studies.

The model incorporates dynamically linked sub-models. They include: population, economic, agricultural, educational, energy, socio-political, international political, environmental, health, infrastructure and technology. IFs is a unique modeling tool because it endogenizes the impact of such a wide range of global systems for 183 countries.

The help system that accompanies the software provides an extensive overview of the model structure and computer code used to write the model. IFs has three main functions, all connected to its conceptual treatment of integrated assessment forecasts: data analysis, scenario analysis, and display.

The data analysis section of IFs represents a collection of over 2,000 data series from all major international data gatherers. It is constantly updated with new data series. This data forms the foundation of the model structure. Users can analyze historic data cross-sectionally, longitudinally or on a world map. Using cross-sectional analysis, users can select a variables and plot this against up to 5 independent variables. It is then possible to animate the map to see how the cross-sectional relationship changes across the 40+ years of data in the database. Longitudinally, users can plot the relationship between a dependent variable and time, from 1960 (for most data series) through the most recent data year available. A world map allows users to display data from any of these series using GIS options.

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