Understand Scenarios in IFs

Introduction to Scenarios

A scenario is a story or story outline. Thinking about the future normally involves creating alternative scenarios, or stories, about the possible evolution of drivers. Some such scenarios are exploratory and consider the possible unfolding of different futures around key uncertainties, such as the rate of some aspect of technological advance or the fragility of some element in the global environment. Other scenarios are normative and develop stories about preferred futures, such as a global transformation to sustainability.

Scenarios in a large integrated model typically are built from multiple interventions that collectively help build a coherent story about the future. Often, but somewhat imprecisely, the word scenario is used more loosely to refer to any intervention (such as the change of a fertility rate for a country or an alternative assumption about oil resources).

Scenarios or interventions with respect to what? When IFs or other computer simulations are "run", without making any changes to parameters or initial conditions specified as the default values, they generate a forecast that is typically called the base case (sometimes reference run). The IFs base case, always available when a model session is initiated, is itself a scenario. Sometimes the base case is incorrectly referred to as a trend extrapolation or a "business as usual" scenario. More accurately, however, the base case of IFs is a computation that involves the full dynamics of the model and therefore has very nonlinear behavior, often quite different from trends. It is a good starting point for scenario analysis for two reasons. First, it is built from initial conditions of all variables that have been given reasonable values from data or other analysis. These initial conditions and parameters make up the package of interventions that constitute the base case scenario. Second, the base case is periodically analyzed relative to the forecasts of many other projects across the range of issue areas covered by IFs and sometimes "tuned" to reproduce the behavior of respected forecasters.

Creating Scenarios in IFs

Change initial conditions and parameters using the  Quick Scenario Tree  to create scenarios beyond the base case. Adjust parameters to make specific intended interventions, for example use the Government Spending by Destination and Sector multiplier parameter gdsm to increase government spending in education. A detailed guide of the different parameters and their potential uses can be found in the Guide to Scenario Analysis.

Files and Run Files

Use the  Quick Scenario Tree to create and save two different kinds of files: Scenario Files (.sce) and Run Files (.run). The Scenario Files represent changes that were made to parameters in IFs but that have not yet run through IFs. Scenario Files will be saved with .sce as the file extension. The Run Files are files that hold forecast results after the IFs software has run. Run Files will be saved with .run as the file extension. The running of a Scenario Files through the IFs software, will include those parameter changes in the scenario file while the model runs and will produce a Run File. The most recent Run File produced by running the model is called the Working-File. By default the Working-File is the base case until the model is run.

In addition to the base case, some versions of IFs will include a number of other previously-run scenarios, perhaps the set of scenarios for the National Intelligence Council’s (NIC) 2020 Project or those for the five Shared Socioeconomic Pathways. In the Scenarios drop down in Flex Displays, a list of previously-run scenarios is shown before any new scenarios are run. Because those have already been run, based on a set of interventions constituting their foundations, their results can already be displayed.

Parameter Types

Parameters are numbers that determine relationships among variables in the equations of IFs. Parameters are often set to a single value across time and they therefore do not always "vary" as do "real" variables. Many parameters are "policy handles," the value of which are set in order to determine the behavior of the model. In IFs parameters are written in lower case form such as endemm and variables are written in upper case such as ENDEM. There are several types of parameters that include:

Multiplier: An equation result parameter that multiplies results (of variable calculations) by the value of the parameter. These parameters are 1 by default, thereby leaving what is multiplied unchanged. Examples of multipliers include: enpm (energy production multiplier), or tfrm (total fertility rate multiplier). Note that multipliers typically end with the letter "m".

Additive Factor: Also, an equation result parameter. Changes results by adding the value of the parameter to the results. These parameters are 0 by default, thereby leaving what is added unchanged. Some examples are: mfpadd (an additive factor on multi-factor productivity growth rate), or migrateinadd (migration rate inward additive factor). Additive Factors typically end with “add”.

Switch: Turn off or on model elements, therefore they alter the structure of the model. They generally take on values of 1 (on) or 0 (off). Switches are most often on or off for the entire run, but it sometimes makes sense to "throw a switch" in the middle of a run. Switch examples include: agon (agriculture economy linkage) and squeez (economic impact of energy shortage). Switch parameters may end in "sw" but have no specific naming structure.

Initial Condition: These are not strictly parameters, but rather first-year values for variables that are subsequently computed by the model, or values for rates of change. These cannot be changed in years beyond the base. These include parameters like: carinit (carbon dioxide in atmosphere in base year, initial condition) or igdpr (initial gdp growth rate). There is no specific naming structure to these parameters.

Limit: These parameters set a limit for a variable such as the maximum or minimum. Some examples include: watwastetreatcostupper (wastewater treatment unit maximum cost, limit) or ylmax (maximum possible agricultural yield, limit). There is no specific naming structure to these parameters but many end in “max” “min” "upper" and "lower".

Rate: Change rates directly or to alter rates of growth or decline. Some examples of rate parameters are: femshrgr (annual growth of female share of the labor force, rate) or ginidomr (domestic gini index growth rate). There is no specific naming structure to these parameters.

Relationships: Set or alter the relationship between two variables, by setting or altering the response level of one variable based on changes in another. Examples include: fpricr1 (food prices response to stock level, relationship) or elasde (energy demand to change in price, relationship)

Target: These are parameters that set a target value and the number of years to achieve certain targets or convergence between countries or variables. Target parameters typically come in pairs with the first parameter setting the target for a variable and the second setting the number of years to achieve that target. For example: sanithhbasictrgtval (percent of people with at least basic sanitation service, Target) and sanithhbasictrgtyr (percent of people with at least basic sanitation service, year to achieve, target). Many times, these parameters end in “val” and “yr” corresponding with setting the target value and year respectively. Another common ending for target parameters is “conv” for conversion targets.

Level: Parameters that override the value of a variable, like: enprix (energy price, level). There is no specific naming structure to these parameters.

Function Coefficient: Alter terms in internal calculations. These parameters will change the underlying structure of equations in the model and as a result will alter the values of variables. Examples of function coefficients include: labinformcoeffintercept (Intercept in the Calculation of Informal Labor Share, Function Coefficient) or govriskweight (Weights in the Calculation of the Government Risk Index, Function Coefficient).

The focus here is on exogenous parameters only - on those elements of the model that can be manually changed. Many computed variables are used in the computation of other variables in the same way that parameters are, as multipliers, additive factors, coefficients, and so on. These can be displayed too, but unlike true parameters, they cannot be changed.