Scenario Description: Difference between revisions

From Pardee Wiki
Jump to navigation Jump to search
No edit summary
No edit summary
(3 intermediate revisions by the same user not shown)
Line 1: Line 1:
= 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.
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.


Line 6: Line 5:
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 and on that has 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 is to a degree "tuned" to reproduce the behavior of respected forecasters.
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 and on that has 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 is to a degree "tuned" to reproduce the behavior of respected forecasters.


Change initial conditions and parameters using the  [[Quick Scenario Analysis with Tree|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 in International Futures (IFs)|Guide to Scenario Analysis]].
The [[Quick_Scenario_Analysis_with_Tree|Quick Scenario Tree]] allows users to create and save two different kinds of files: Scenario-Load-Files (.sce) and Run-Result-Files (.run). The Scenario-Load-Files files represent changes that were made to the scenario tree but that were not yet entirely run through IFs software. The Run-Result-Files represent files that were originally changes to the scenario tree that were eventually entirely run through IFs software. The running a Scenario-Load-Files file will make those changes permanent and therefore a Run-Result-File.


Use the  [[Quick Scenario Analysis with Tree|Quick Scenario Tree]]  to create and save two different kinds of files: Scenario-Load-Files (.sce) and Run-Result-Files (.run). The Scenario-Load-Files files represent changes that were made to the scenario tree but that were not yet entirely run through IFs software. The Run-Result-Files represent files that were originally changes to the scenario tree that were eventually entirely run through IFs software. The running of a Scenario-Load-Files file will make those changes permanent and therefore produce a Run-Result-File.
In addition to the base case, most versions of IFs will include a number of other previously-run scenarios (see [[How_Do_I...?_Lessons_0-4#Lesson_0:_IFs_Vocabulary|Lesson 0]] for additional important terminology), perhaps the set of scenarios for the National Intelligence Council’s (NIC) 2020 Project or those for the Global Environmental Outlook (GEO) of the United Nations Environmental Program. If you look, for instance, at the Packaged Display form, you will see a list of previously-run scenarios in the box at the bottom left of the screen. Because those have already been run, based on a set of interventions constituting their foundations, the user can immediately display their results.
 
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.
 
=<span style="font-size:xx-large;">Parameter Types</span>=
Parameters are numbers that determine relationships among variables in the equations of IFs. You often set parameters to a single value across time and they therefore do not always "vary" as do "real" variables. Many parameters are "policy handles." More generally, parameters can actually be thought of as a special type of variable, the value of which you set in order to determine the behavior of the model.
 
'''Multipliers.'''&nbsp;They have a normal value of 1, and to increase whatever they multiply (say agricultural yield) by 50 percent you increase the parameter to 1.5. To decrease it by 25 percent you would decrease the multiplier parameter to 0.75. You will almost always spread such changes out over time, keeping the multiplier's value at 1 in the base year and gradually increasing or decreasing it over a period of years. You should almost never change a multiplier in the initial year because the model is set up to provide accurate results for that year and will compensate for and thereby offset your change. For instance, if you set a multiplier on food production equal to 1.5 for the first year and all years thereafter, you might find that the results were no different than in the base case. You must instead gradually introduce your change, preserving the multiplier value of "1" in the initial year. Examples of multipliers include: AGDEMM, ENPM, FREEDOMM, MORTM, PROTECM, QEM, RDM, RESORM, TFRM, and YLM. Note that multipliers typically end with the letter "M".
 
'''Additive Factors.'''&nbsp;Most have a normal value of 0, thereby leaving that to which you add them (it could be exports) unchanged. How much you would add to achieve a 50 percent increase might depend on the amount to which you added it. Most additive parameters are, however, applied multiplicatively to the quantity they modify (that is, 1 plus the parameter is multiplied times the quantity), thereby scaling the parameter. In that case, the base or normal value of the parameter will be zero, but one can achieve a 50 percent increase in the quantity modified with a value of 0.5 and a 50 percent decrease with -0.5. You will very seldom want to change the base year value of additive parameters because it will either incorrectly change model results in the base year or, more likely, will result in model compensation to protect initial model results. An example of an additive parameter is: XSHIFT. Although earlier versions of IFs used additive factors and multipliers with comparable frequency, most additive factors have been replaced by multipliers to standardize most parameter change.
 
'''Exponents.'''&nbsp;For instance, many "elasticities" raise something to a power. For these parameters the "normal value" will vary greatly, but they will most often fall between -2 and 2, with many clustering around 0. In most cases it will make sense to change these parameters for all years including the first - generally the model will not use them in the first year and they will affect results only in subsequent years. Elasticities in IFs include: ELASAC, ELASS, ENGEL, and PRODME.
 
'''Reactivities.'''&nbsp;These are factors that relate growth in one process to growth in another. Although many will range between -2 and 2 (with 0 eliminating linkage of the processes), some have very large values. They are very much like elasticities, but the formulations that use them do not have exponential form. Reactivities include: CDMF, CPOWDF, CWARF, NWARF, and REAC.
 
'''Growth Rates.'''&nbsp;It is possible to force some processes to grow at specified rates. More commonly, the specified rates serve as targets and the dynamics of the model often shift actual growth rates somewhat, necessitating experimentation with targets to achieve a desired growth. Examples include: EPRODR and TGRLD.
 
'''Allocating coefficients.'''&nbsp;Coefficients are often used in multiplicative relationships with other variables, but many such coefficients are not what were earlier called multipliers (with a base value of 1). Instead they can serve an allocating role. For instance, eyou can use parameters to allocate governmental spending to health, education, and the military. Allocating coefficients frequently have values between 0 and 1. Again, you should generally not change these parameters in the initial year because the model will often compensate for changed values in the first year. Instead, change them by series over time. Allocating coefficients in IFs include: AIDLP, AIDV, CARABR, DRCPOW, DRNPOW, GK, LAPOPR, NMILF, REPAYR, and RFSSH.
 
'''Transforming coefficients.'''&nbsp;Some coefficients transform units of variables or link variables in other ways. Examples in IFs are: CARFUEL1, CARFUEL2, CARFUEL3, and FRQK.
 
'''Variables.'''&nbsp;This category should technically not be called parameters at all. They could and would be computed endogenously, if the model included the appropriate theoretical structure. They generally do not determine the interaction of other variables. Such variables include: AQUACUL and OFSCTH.
 
'''Initial conditions.'''&nbsp;Again, these are not strictly parameters, but rather first-year values for variables subsequently computed by the model. Although many initial conditions, like the population (POP) of the U.S., are sufficiently well-known that they should not be changed by model users, others, like the ultimate availability of oil and gas resources are only reasonable guesses. Thus users should feel free to change some initial conditions based on new data or even simply to test the implications. This category includes a great many variables, such as: LD and RESOR.
 
'''Switches.'''&nbsp;These parameters turn something on or off. They generally take on values of 1 (on) or 0 (off), but can have additional settings. For instance, some switches not only turn on some process, but set a key value within it (like the level of energy exports). 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. Switches allow you to fundamentally alter the structure of a model. Switches include: ACTREAON, AGON, ALLY, ENON, ENTL, ENPRIX, and SQUEEZ.
 
The focus here is on exogenous parameters only - on those elements of the model that you can change. 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. You can display those, but unlike true parameters, you cannot change them.
=<span style="font-size:xx-large;">Customization of Parameters</span>=
Access to time-variant parameter specification (the Change Values form) can be from either guided scenario analysis or self-managed scenario analysis.
 
Although most modeling discussions portray parameters as if they should be fixed over time, that is a very limiting conceptualization of them. In fact, it is normally better to specify parameters so that a particular phenomenon (e.g., a change in values concerning fertility, a policy-influenced movement towards higher savings rates, or a development of renewable energy technologies) phases in over time.
 
Alternative Ways to Use the Change Values Form:
 
'''Use the Slider Bar to Change a Parameter for All Years.'''&nbsp;You move the slider to the left or right to change the parameter value and then touch the Register Change button to actually change the parameter.
 
'''Specify a Desired Value for One or More Years.'''&nbsp;You specify a desired numerical value, indicate the number of years you wish to repeat that value (one or more) and then touch the Change/Repeat button.
 
'''Interpolate to a Desired Value over Several Years.'''&nbsp;You specify a desired value, indicate the number of years over which you wish to interpolate to that value, and then touch the Interpolate button.
 
'''Move Forward or Backward Across Years.'''&nbsp;You touch the Previous Year or the Next Year buttons to move across time without changing parameter values.
 
'''Cancel all Changes.'''&nbsp;You touch the Cancel all Changes button to revert to the parameter values before you began making changes.
 
'''Example.'''&nbsp;Try increasing the value of agricultural yields (YL) in Mexico by raising the value of a parameter called "ylm" from 1.0 in the initial year to 1.3 in 2020. That would build in an assumption of a 30% increase in the productivity of African agriculture, relative to the base case. To do this, select Scenario Analysis from the Main Menu and the Self-Managed Scenario Analysis sub-option. On the Self-Managed Scenario Analysis form select the Change option and Full Set sub-option. Specify ylm and choose Mexico as the country/region. Success in doing that will take you automatically to the Change Values (time-variant parameter specification) form. Designate 1.3 as the desired value to which you will interpolate (that is, move gradually over time) and indicate the number of years for the interpolation (say 20). Select the interpolate action option to carry it out. Then identify 1.3 as the desired value you wish to repeat (that should already be done for you), 100 or some other large number as the years to repeat, and select the change/repeat button. Exit and select the Display option. Select ylm for display, and look at it in a table or graph to make sure you have changed this parameter as you desired. It is often a good idea to check the success of a parameter change before running the model.
=<span style="font-size:xx-large;">Understanding Model Computations</span>=
It is critical that there be as much transparency as possible with respect to computations that underlie the variables chosen for display. In a large, integrated model, achieving such transparency is not simple. You are invited to look at the very extensive Help section called&nbsp;[[Understand IFs|"Understanding the Model: ‘Opening the Black Box’"]]&nbsp;for extensive documentation via flow charts, equations, and computer code.
 
While working with display of variables, however, there are several ways in which to drill down for explanations of what lies behind their computations. After you have added variable or parameter changes to the Quick Scenario with Tree you can learn more about how a parameter or variable is generated by clicking on it and exploring the options.

Revision as of 15:07, 25 July 2017

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 computer 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 and on that has 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 is to a degree "tuned" to reproduce the behavior of respected forecasters.

The Quick Scenario Tree allows users to create and save two different kinds of files: Scenario-Load-Files (.sce) and Run-Result-Files (.run). The Scenario-Load-Files files represent changes that were made to the scenario tree but that were not yet entirely run through IFs software. The Run-Result-Files represent files that were originally changes to the scenario tree that were eventually entirely run through IFs software. The running a Scenario-Load-Files file will make those changes permanent and therefore a Run-Result-File.

In addition to the base case, most versions of IFs will include a number of other previously-run scenarios (see Lesson 0 for additional important terminology), perhaps the set of scenarios for the National Intelligence Council’s (NIC) 2020 Project or those for the Global Environmental Outlook (GEO) of the United Nations Environmental Program. If you look, for instance, at the Packaged Display form, you will see a list of previously-run scenarios in the box at the bottom left of the screen. Because those have already been run, based on a set of interventions constituting their foundations, the user can immediately display their results.

Retrieved from "https://pardeewiki.du.edu//index.php?title=Scenario_Description&oldid=7511"