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Functionality concerns ways in which modeling can be used to support broader policy objectives ; the reason ( s ) why construction of a model is the most appropriate methodology to investigate the policy issue . While this is of most importance to the policy developer , the modeler must understand these objectives in order to deliver a useful model .
Much of the functionality arises through the modeling process and is relatively independent of the modeling technique selected . However , broad groups of modeling techniques do have different strengths . Mathematical or computer models are required for forecasting , but this power comes at the cost of some loss in accessibility and hence potential for communication . Regardless of the technique chosen , models can help to synthesize knowledge and manage unknowns , and thereby encourage a broad understanding of the policy issue and options .
In contrast , accuracy substantially depends on which modeling technique is selected . Each technique has assumptions that can match or conflict with important characteristics of the system being modeled , such as whether heterogeneity is important and the extent to which the behaviors of entities within the system are independent . Abstraction must retain the relationships that are important in generating the system behavior and represent those in the model if the model behavior is to provide guidance about how the system would behave under different conditions . Policy developers rely on the modeler ’ s skill to develop a sufficiently accurate model , and are unlikely to understand the implications of different techniques . The framework identifies the key issues to allow the policy developer to be involved in the decisions that affect the accuracy of the model .
Feasibility concerns the resource requirements for the policy modeling project . Like functionality , these are largely independent of the modeling technique . However , quantitative methods such as mathematical or computer simulations require extensive data if the model is to be used for forecasting or for detailed comparisons of options . Skills , knowledge , time , and other resource needs depend on the issue and the intended depth of the model more than the modeling technique .
The framework is intended as a discussion guide , not a set of rules about what is appropriate for particular policy analyses . In practice , it is often necessary to compromise on some aspects to meet other , more important , objectives . Such compromises are best decided between the policy analyst and the modeler , with both understanding the positive and negative consequences of the decisions . The framework and the details about each dimension provide a starting point for these decisions .
VI - Acknowledgements
This paper is based on work I performed at the National Centre for Epidemiology and Population Health at The Australian National University , as a Research Fellow funded by the Australian Research Council Centre of Excellence in Policing and Security . It has benefited enormously from discussion and collaboration with Professor Gabriele Bammer .
I am also grateful to Corinna Elsenbroich and other colleagues at the University of Surrey who helped me to refine my ideas through discussion and comments on various drafts , and to the referees who provided helpful suggestions for improvement .
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