concepts:calibration_demystified
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concepts:calibration_demystified [2009/11/24 01:46] marcus.williams |
concepts:calibration_demystified [2010/06/10 19:44] (current) deryn.crockett |
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| Calibration is the process of finding simulator input values for a historical time period which reproduce the observed simulator outputs over that period. To accomplish this a distinct calibrator framework is created alongside the simulator framework, within the same model family (see [[concepts:How it's Organized]]). There are two main motivations for calibration. The first is to look back at the history of a system through the same conceptual framework used to explore the future of that system - a prerequisite for trend analysis. The second is to get starting values - or initial conditions - of model stocks. | Calibration is the process of finding simulator input values for a historical time period which reproduce the observed simulator outputs over that period. To accomplish this a distinct calibrator framework is created alongside the simulator framework, within the same model family (see [[concepts:How it's Organized]]). There are two main motivations for calibration. The first is to look back at the history of a system through the same conceptual framework used to explore the future of that system - a prerequisite for trend analysis. The second is to get starting values - or initial conditions - of model stocks. | ||
| - | {{:concepts:calibration.png|}} | + | {{:concepts:calibration2.png|}} |
| The diagram above shows the relationship between a simulator framework and its corresponding calibrator framework - with respect to inputs, outputs and the time periods for which they operate. Key points to note: | The diagram above shows the relationship between a simulator framework and its corresponding calibrator framework - with respect to inputs, outputs and the time periods for which they operate. Key points to note: | ||
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| As an example, take a simple population model in which | As an example, take a simple population model in which | ||
| - | population<sub>t</sub> = population<sub>t-1</sub> + net immigration<sub>t-1</sub> + births<sub>t-1</sub> - deaths<sub>t-1</sub> | + | >>population<sub>t</sub> = population<sub>t-1</sub> + net immigration<sub>t-1</sub> + births<sub>t-1</sub> - deaths<sub>t-1</sub> |
| If historical data for population, births and deaths are available then calibration involves determining the historical net immigration levels. For real-world models this is a non-trivial problem and requires a separate calibrator framework because: | If historical data for population, births and deaths are available then calibration involves determining the historical net immigration levels. For real-world models this is a non-trivial problem and requires a separate calibrator framework because: | ||
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| * Data quality issues are common, and often manifest themselves through inconsistent or infeasible calibration results. For example, suppose that net immigration in the above example were broken into its component parts - immigration and emigration. If estimates for historical immigration were available and used in the calibration to estimate emigration levels, it is possible that a naive calibration procedure would produce some negative values for historical emigration. Of course, this is infeasible and so the calibration framework must consider these issues to produce an internally consistent and feasible calibration solution. | * Data quality issues are common, and often manifest themselves through inconsistent or infeasible calibration results. For example, suppose that net immigration in the above example were broken into its component parts - immigration and emigration. If estimates for historical immigration were available and used in the calibration to estimate emigration levels, it is possible that a naive calibration procedure would produce some negative values for historical emigration. Of course, this is infeasible and so the calibration framework must consider these issues to produce an internally consistent and feasible calibration solution. | ||
| - | Note that the term calibration used in the whatIf? Platform (Design Approach) context differs from specification and estimation of a statistical model. In particular, the use of data is different as in statistical modelling the data are used to estimate the parameters in the model using an appropriate statistical technique, while in the design approach, data which have not been measured, such as lifetimes of capital stocks, are estimated and then the data set is used to calibrate the model. The calibration is successful if the model reproduces the data, given a set of historical input variables, and this is considered a necessary but not sufficient condition for a valid model. [1] | + | Note that the term calibration used in the whatIf? Platform (Design Approach) context differs from specification and estimation of a statistical model. In particular, the use of data is different as in statistical modelling the data are used to estimate the parameters in the model using an appropriate statistical technique, while in the design approach, data which have not been measured, such as lifetimes of capital stocks, are estimated and then the data set is used to calibrate the model. The calibration is successful if the model reproduces the data, given a set of historical input variables, and this is considered a necessary but not sufficient condition for a valid model((Gault, F. D., K. E. Hamilton, R. B. Hoffman, and B. C. McInnis. The Design Approach to Socio-Economic Modelling. Futures, February, 1987. http://www.whatiftechnologies.com/publication/THE_DESIGN_APPROACH_TO_SOCIO_ECONOMIC_MODELLING.pdf)). |
concepts/calibration_demystified.1259027204.txt.gz ยท Last modified: 2009/11/24 01:46 by marcus.williams
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