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food availability will decrease . Once this occurs , the life paths of rabbits are affected by the other rabbits , with death rates depending on rabbit density . This constrained population growth Verhulst ( 1838 ) is an example of indirect interaction mediated through the environment . If wolves migrate to the grassland , they will be directly interacting with the rabbits by eating them . The populations of rabbits and wolves become completely interdependent Lotka ( 1956 ). In these examples , the appropriate interaction to be included in a model of the rabbit population depends not only on characteristics of the rabbits , but also their environment .
The characteristics of interaction and heterogeneity may be considered together when selecting a modeling technique ( Kelly ( Letcher ) et al ., 2013 ). This is because cohort models are able to incorporate indirect interaction and some types of direct interaction without difficulty , where that interaction is related to the size of a group . However , individual oriented models are more flexible in the ways in which an entity recognizes the behavior of other entities , and thus able to deal with a broader range of interactions .
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Desired functionality has only limited impact on the specific modeling technique , operating at broad type of technique such as diagrams , games , mathematical model , or computer simulation . In contrast , specific methods make different assumptions about the characteristics of the system being modeled . A mismatch between these assumptions and the actual target system characteristics limits the potential accuracy of the model , as the technique may not be able to represent important relationships .
Classifying techniques by their assumptions is complicated by at least two difficulties . First , there is no list of techniques and many techniques are known by several names . Second , while the assumptions have been presented with distinct categories , real systems and techniques do not necessarily separate neatly and techniques are constantly being modified to extend the problems for which they are suitable .
One approach is to compile and describe several methods , using examples and typical types of problems to provide guidance on appropriate techniques ( Complex Systems Modelling Group , 2010 ; Gilbert & Troitzsch , 2005 ), perhaps supplemented with some questions to help select a method Kelly ( Letcher ) et al . ( 2013 ). Alternatively , methods can be grouped for convenience of discussion but without any specific structure ( Brailsford et al ., 2009 ). Formal taxonomies of commonly used methods in health technology assessment ( Barton et al ., 2004 ; Brennan et al ., 2006 ; Stahl , 2008 ) have particularly focused on issues of aggregation and interaction , but also considered time , space , and other characteristics .
See the appendix for a non-comprehensive list of several well-known and less familiar methods with brief descriptions . It identifies their assumptions , but does not classify by them . The methods are all potentially useful for analyzing complex policy issues , selected for their diversity .
V - Conclusion
This paper presents a policy modeling
framework with three themes : functionality , accuracy , and feasibility . The primary purpose of the framework is to identify key issues involved in selecting a modeling technique . However , it can also be used to structure more general policy modeling issues and support effective collaboration .
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