Help During Use of IFs
The full Help System is always available to users of IFs from the Help option on the Main Menu. In addition, however, there are several types of Help that are available at key points of model use and that are generally more specific to the specific points of model use. The goals of the IFs system are user-friendliness with respect to the interface and transparency and openness with respect to the structure of the model.
Context Sensitive Help. The use of the F1 key will normally provide screens that provide information about the specific form or window being used at the time. These explain the interactive interface of the model.
Pop-up Menus with Help Options. Users also want Help, however, when dealing with variables and parameters in the model. They want to know longer names for the short ones sometimes used in the model and want to understand the causal linkages of variables to each other. The use of pop-up menus at many places when variables/parameters are being chosen or used for inputs and for display opens up a variety of options for understanding the names and linkages. For instance, when a table is displayed, a double-click on the body of the table with the right or left mouse button produces a pop-up menu with several options. Similarly, at the bottom of the Display Menu or the Variable Selection form are status boxes with short variable names selected by the user. A right or left mouse click brings up a similar pop-up menu.
The pop-up menu system was developed by Mohammod T. Irfan.
Understanding the Modeling Approach
There are many "routes to understanding" of a model: the general philosophy of the modeling approach, the key or dominant relationships and dynamics in the model, the primary causal linkages in the model (using flow charts or causal diagrams), the equations, the full model computer code, and the data used. In order to facilitate the search for understanding, this documentation provides each of these paths, more or less in the sequence of this listing.
The documentation groups most of the "routes to understanding" under issue modules (such as the energy module). The exception is model code, which is collected across modules because of its specialized character.
The IFs model is constantly evolving. In addition to the documentation here, there is stand-alone documentation on the Reports page of the IFs project web site. The model user would be advised, in particular, to look at the paper on "The Structure of International Futures (IFs)."
IFs Structure: Elements and Philosophy
A basic mental model helps frame the approach to modeling in International Futures:
Global human systems consist of classes of agents and larger structures within which those agents interact. Over time agents and the larger structures evolve in processes of mutual influence and determination.
That conceptualization shapes the methodological approach:
At one time global models were categorized as using either econometric or systems dynamics methodologies. IFs draws upon techniques found in both traditions, but reaches beyond them, especially in its structural representations.
Structural representations include cohort-component systems for population; markets for production, exchange, and consumption of goods and service; and social accounting matrices for financial flows.
This emergent IFs methodology is Structure-Based and Agent-Class Driven Modeling.
More detail is available on the manifestation of this modeling approach for the following structural systems of IFs:
- Structure and Agent System: Agriculture
- Structure and Agent System: Demographic
- Structure and Agent System: Economics
- Structure and Agent System: Education
- Structure and Agent System: Energy
- Structure and Agent System: Environment
- Structure and Agent System: Governance
- Structure and Agent System: Health
- Structure and Agent System: Infrastructure
- Structure and Agent System: Interstate Interaction
- Structure and Agent System: 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 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.
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
Understanding the Equations
As a general rule, the equations closely follow the computer code. Insofar as possible without confusion, variable and parameter names here are the same as those in the computer program, but in a few cases equation names differ to enhance readability. Computer code shows a single computed variable on the left and one or more input variables and parameters on the right. In fact, computer code frequently shows the computed variable on both the left and the right hand side, which is NOT standard mathematical equation form and a few traditional purists have difficulty understanding this (as well as preferring non-mnemonic single letter variable names and Greek symbols to much more intelligible computer-based variable names). As an appropriate accommodation, this documentation sometimes uses asterisks to distinguish different values of the same variable name on left and right-hand sides of equations.
IFs has multiple modules: population, economic, agriculture, energy, and socio-political. An environmental "module" is scattered across other modules, especially agriculture and energy. Equations are presented by module and cross references in the documentation of each module indicate linkages.
IFs is a recursive dynamic system and equation sequence is therefore important. This text presents equations in largely the same sequence as in the computer program. Program flow exits from and returns to each module up to three times each time step (year), however, and it would break up discussions of module too much if we were to follow computational flow slavishly. Moreover, to facilitate understanding this documentation sometimes presents the key equations of a module or a section of one first, with subsequent explanation of the compu"tational procedures for variables used therein (thereby deviating further from actual computation sequence). Equation form here is the same as in the computer program, including the presentation of a single "computed" variable on the left side of the equal sign.