Introduction to IFs
International Futures (IFs) is a tool for thinking about long-term global futures. It assists with:
- Understanding the state of the world
- Exploring trends and considering where they might be taking us
- Learning about the dynamics of global systems
Thinking about the future we want to see:
- Clarifying goals/priorities
- Developing alternative scenarios (if-then statements) about the future
- Investigating the leverage various agent-classes have in shaping the future
Assumptions that underlie IFs development and use:
- Global issues are become more significant as the scope of human interaction and human impact on the broader environment grow
- Goals and priorities for human systems are becoming clearer and are more frequently and consistently enunciated
- Understanding of the dynamics of human systems is growing rapidly
- The domain of human choice and action is broadening
What can you investigate with IFs? Examples include:
- Environmental Sustainability: Atmospheric carbon dioxide levels, world forest area, fossil fuel usage
- Social/Political Change: Life expectancy, literacy rate, democracy level, status of women, value change
- Demographic Futures: Population levels and growth, fertility, mortality, migration
- Food and Agriculture: Land use and production levels, calorie availability, malnutrition rates
- Energy: Resource and production levels, demand patterns, renewable energy share
- Economics: Sectoral production, consumption, and trade patterns and structural change
- Global System: Country and regional power levels
IFs Issues and Modules: Visual RepresentationAmong the philosophical premises of the International Futures (IFs) project is that the model cannot be a "black box" to users and be truly useful. Model users must be able to examine the structures of IFs in order (1) to have confidence in them, and (2) learn from them.
Any computer simulation or other model will have some relationships and dynamics that dominate the behavior of the model and that therefore most heavily influence the analyses done with the model. Understanding these dominant relations will facilitate model use, particularly in the definition of key or framing scenarios.
The value added by more detailed specification of relationships in the model will lie partly in more probing analysis, often around specific policy options. Much of the value added by a more complete model specification will, however, lie in the dynamics of the full model.
For an introductory summary of dominant relations and dynamics by submodule:
- Dominant Relations: Agriculture
- Dominant Relations: Demography/Population
- Dominant Relations: Economics
- Dominant Relations: Education
- Dominant Relations: Energy
- Dominant Relations: Environment
- Dominant Relations: Governance
- Dominant Relations: Health
- Dominant Relations: Infrastructure
- Dominant Relations: Interstate Politics
- Dominant Relations: Socio-Political
Structure-Based and Agent-Class Driven Modeling
The Structure-Based, Agent-Class Driven approach has five key elements methodologically: organizing structures, stocks, flows, key aggregate relationships, and key agent-class behavioral relationships.
Organizing structures are well-recognized and theoretical and conceptual frameworks with an organizing character for important human systems: cohort-component structures for demographic systems, markets for economic systems, financial flows for socio-political-economic systems, and so on.
Stocks and flows remind us of systems dynamics. In demographic systems, the stocks are numbers of people in age- and sex-specific cohorts, while the flows are births, deaths, and migration. Systems dynamics would deal with the key relationships as auxiliaries, but econometrics would recognize them as equations that require empirical estimation.
Key Aggregate Relationships. Life expectancy or mortality is a key aggregate relationship, clearly a function of income, perhaps education, and certainly of technological change. Aggregate Relationships are often actually Agent-Class behaviors that have not yet been decomposed enough to represent in terms of a single agent class. For instance, life expectancy is a function of government and firm spending on R&D as well as household life-style choices; it could eventually be decomposed to the agent-class level.
Key Agent-Class Behavioral Relationships. For example, in the case of fertility, there is one primary agent-class, namely households, whose behavior, as a function again of income, education, and technology, will change over time.
Agent-classes versus micro agents. IFs is not agent-based in the sense of models that represent individual micro-agents following rules and generating structures through their behavior. Instead, IFs instead represents both existing macro-agent classes and existing structures (with complex historic path dependencies), attempting to represent some elements of how behavior of those agents can change and how the structures can evolve. Although building aggregate model behavior and structure upward from micro agent behavior is laudable in more narrowly-focused models, global systems and structures are far too numerous and well-developed for such efforts to succeed across the breadth of concerns in IFs.
In representing the behavior of agent classes and the structures of systems, IFs draws upon large bodies of insight in many theoretical and modeling literatures. Although IFs sometimes breaks new ground with respect to specific sub-systems, its strengths lie primarily in the integration and synthesis of much earlier work.
- Bremer, Stuart A. 1977. Simulated Worlds: A Computer Model of National Decision-Making. Princeton: Princeton University Press.
Variable names are shown in all capitals, as in the display functions of the model. Parameters are shown in lower case and boldface. Empirically-based initial conditions of variables are in capitals with boldface. Internal computed variables, which are not available for display, are shown in mixed upper and lower case.
At one time the project used a superscript of "t" to indicate time/year. Although it has mostly moved that to subscripts, it may sometimes still be found in project documentation. Superscripts other than "t" indicate exponentiation. Subscripts or superscripts with "t" indicate time, but will be omitted when a reference is contemporary to model year "t."
Subscripts show dimensionality and there are a number of standard ones in the model:
- r for region/country r = 1,2,... (e.g., United States, European Union, Japan, Brazil...)
- c (sometimes the project uses j) for age cohort c/j = 1,2,...,22 (infant, 0-4 years,...95-99 years, 100+ years; abbreviated set for World Value Survey variables)
- s for economic sector s = 1,2,3,4,5,6 (agriculture, energy, materials, manufactures, services, ICT)
- f for food types f = 1,2 (crops, meat/fish)
- l for land types l = 1,2,3,4,5 (crop, grazing, forest, unused, urban/industrial)
- e for energy types e = 1,2,3,4,5,6,7 (oil, gas, coal, hydroelectric, nuclear, other renewable, unconventional oil)
- g for govt spending g = 1,2,3,4,5,6 (military, health, education, R&D, other, foreign aid)
- p for population sex p=1,2 (male, female)
- ss for safe water and sanitation ladder categories
- d for cause of death (15 in total)
- dg for cause of death group dg=1,2,3 (communicable, non-communicable, injuries/accidents)
- h for household types h=1,2 (unskilled, skilled)
Individual equations specify a range of dimensionality only if it differs from that above. <header><hgroup>
IFs Issues and Modules: Quick Survey
The population module:
- represents 22 age-sex cohorts to age 100+
- calculates change in fertility and mortality rates in response to income, income distribution, and analysis multipliers
- computes average life expectancy at birth, literacy rate, and overall measures of human development (HDI) and physical quality of life
- represents migration and HIV/AIDS
- includes a newly developing submodel of formal education across primary, secondary, and tertiary levels
The economic module:
- represents the economy in six sectors: agriculture, materials, energy, industry, services, and ICT (other sectors could be configured, using raw data from the GTAP project)
- computes and uses input-output matrices that change dynamically with development level
- is a general equilibrium-seeking model that does not assume exact equilibrium will exist in any given year; rather it uses inventories as buffer stocks and to provide price signals so that the model chases equilibrium over time
- contains an endogenous production function that represents contributions to growth in multifactor productivity from R&D, education, worker health, economic policies ("freedom"), and energy prices (the "quality" of capital)
- uses a Linear Expenditure System to represent changing consumption patterns
- utilizes a "pooled" rather than the bilateral trade approach for international trade
- is being imbedded during 2002 in a social accounting matrix (SAM) envelope that will tie economic production and consumption to intra-actor financial flows
The agricultural module:
- represents production, consumption and trade of crops and meat; it also carries ocean fish catch and aquaculture in less detail
- maintains land use in crop, grazing, forest, urban, and "other" categories
- represents demand for food, for livestock feed, and for industrial use of agricultural products
- is a partial equilibrium model in which food stocks buffer imbalances between production and consumption and determine price changes
- overrides the agricultural sector in the economic module unless the user chooses otherwise
The energy module:
- portrays production of six energy types: oil, gas, coal, nuclear, hydroelectric, and other renewable
- represents consumption and trade of energy in the aggregate
- represents known reserves and ultimate resources of the fossil fuels
- portrays changing capital costs of each energy type with technological change as well as with draw-downs of resources
- is a partial equilibrium model in which energy stocks buffer imbalances between production and consumption and determine price changes
- overrides the energy sector in the economic module unless the user chooses otherwise
The two socio-political sub-modules:
Within countries or geographic groupings
- represents fiscal policy through taxing and spending decisions
- shows six categories of government spending: military, health, education, R&D, foreign aid, and a residual category
- represents changes in social conditions of individuals (like fertility rates or literacy levels), attitudes of individuals (such as the level of materialism/postmaterialism of a society from the World Value Survey), and the social organization of people (such as the status of women)
- represents the evolution of democracy
- represents the prospects for state instability or failure
Between countries or groupings of countries
- traces changes in power balances across states and regions
- allows exploration of changes in the level of interstate threat
- represents possible action-reaction processes and arms races with associated potential for conflict among countries
The implicit environmental module:
- is distributed throughout the overall model
- allows tracking of remaining resources of fossil fuels, of the area of forested land, of water usage, and of atmospheric carbon dioxide emissions
The implicit technology module:
- is distributed throughout the overall model
- allows changes in assumptions about rates of technological advance in agriculture, energy, and the broader economy
- explicitly represents the extent of electronic networking of individuals in societies
- is tied to the governmental spending model with respect to R&D spending
International Futures (IFs) has evolved since 1980 through three "generations," with a fourth generation now taking form.
The first generation had deep roots in the world models of the 1970s, including those of the Club of Rome. In particular, IFs drew on the Mesarovic-Pestel or World Integrated Model (Mesarovic and Pestel 1974). The author of IFs had contributed to that project, including the construction of the energy submodel. IFs consciously also drew on the Leontief World Model (Leontief et al. 1977), the Bariloche Foundation’s world model (Herrera et al. 1976), and Systems Analysis Research Unit Model (SARU 1977), following comparative analysis of those models by Hughes (1980). That generation was written in FORTRAN and available for use on main-frame computers through CONDUIT, an educational software distribution center at the University of Iowa. Although the primary use of that and subsequent generations was by students, IFs has always had some policy analysis capability that has appealed to specialists. For example, the U.S. Foreign Service Institute used the first generation of IFs in a mid-career training program.
The second generation of International Futures moved to early microcomputers in 1985, using the DOS platform. It was a very simplified version of the original IFs without regional or country differentiation.
The third generation, first available in 1993, became a full-scale microcomputer model. The third generation improved earlier representations of demographic, energy, and food systems, but added new environmental and socio-political content. It built upon the collaboration of the author with the GLOBUS project, and it adopted the economic submodel of GLOBUS (developed by the author). GLOBUS had been created with the inspiration of Karl Deutsch and under the leadership of Stuart Bremer (1987) at the Wissenschaftszentrum in Berlin.
The third generation has produced three editions/major releases of IFs, each accompanied by a book also called International Futures (Hughes 1993, 1996, 1999). The second edition moved to a Visual Basic platform that allowed a much improved menu-driven interface, running under Windows. The third edition incorporated an early global mapping capability and an initial ability to do cross-sectional and longitudinal data analysis.
The fourth generation has been taking shape since early 2000. It has been heavily influenced by the usage of the model in an increasingly policy-analysis mode by several important organizations. First, General Motors commissioned a specialized version of IFs named CoVaTrA (Consumer Values Trends Analysis) with a need for updated and extended demographic modeling and representation of value change. An alliance was established with the World Values Survey, directed by Ronald Inglehart, to create that version. Second, the Strategic Assessments Group of the Central Intelligence Agency commissioned a specialized version named IFs for SAG. The work involved in preparing that greatly extended and enhanced the socio-political representations of the model, both domestic and international. Third, the European Commission sponsored a project named TERRA which has led to a specialized version named IFs for TERRA. IFs for TERRA work led to enhancements across the model, including improved representation of economic sectors, updated IO matrices and a basic Social Accounting Matrix, GINI and Lorenz curves, and preparing for extended environmental impact representation (drawing upon the Advanced Sustainability Analysis framework of the Finland Futures Research Center).
Throughout this emergence of a fourth generation IFs (incorporating all of the above elements for additional users) there has been also a heavy emphasis on enhanced usability. Ideas from Robert Pestel in the TERRA project led to the creation of a new tree-structure for scenario creation and management. Ideas from Ronald Inglehart led to the development of the Guided Use structure and a somewhat more game-like character within that structure. Inglehart also help arrange funding to support the programming of Guided Use through the European Union Center of the University of Michigan.
The fifth version of IFs is currently in use and represents broad strides to improving the model and its usability. It is the first version of this software to be placed online due to the help of the National Intelligence Council (http://www.ifs.du.edu). Also, usability has been increased as Packaged Displays and Flex Packaged Displays were introduced that allowed for the creation of very specific lists of countries/regions, groups or Glists. A new education model has also been incorporated into the broader IFs model. New scenarios were created for UNEP (focusing on environmental change) and Pardee (focusing on poverty). Finally, one of the largest changes made was incorporating 182 countries into the Base-Case scenario used by IFs. Previous versions of IFs used broader regions to forecast global trends. This change also did away with the Student and Professional versions.
Geographic Representation of the World
186 countries underpin the functioning of IFs and these countries can be displayed separately or as parts of larger groups that users can determine.
Below is a visual representation of how different entities are organized into Countries/Regions, Groups or Glists:
*Note: In older versions of IFs, Regions were used as intermediaries between Countries and Groups. In the future, they, or some similarly named unit, will be a sub-unit of Countries. Regions, acting as a sub-unit of Countries, are currently not a feature of IFs. See the image loc'ated at the bottom of this Help topic.
When using IFs, there are many occasions where the user is asked whether or not they would like to display their results as a product of single countries, or larger groups. This is typically a toggle switch that moves between Country/Region and Groups, however, it might be a three-way-toggle that includes Country/Region, Group and Glist.
Countries/Regions are currently the smallest geographical unit that users can represent. The ability to split countries down into smaller regions, or states, is under development. There are 186 different countries/regions that users can display.
Groups are variably organized geographically or by memberships in international institutions/regimes. You can find out who is represented in each group and add or delete members by exploring the Managing Regionalization function.
Glists merge both Groups and Countries/Regions. These lists are mostly geographically bound. In the future, the Glist distinction will become more important as some users may want to place, for example, both the Indian state of Kerala in a Glist with Sri Lanka and Nepal.
IFs Time Horizon
Future Forecasts. IFs begins computation with data from 2000 and can dynamically calculate values for all variables annually through 2100.
Historical Analysis and "Forecasts." IFs also includes an extensive and growing historical data base starting in 1960. The data basis allows analysis of relationships among variables across countries and across time.