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Many games can be developed quickly , even where stakeholders have limited formal education or resources and no experience with modeling . For example , a board game was developed with villagers in N ’ Gnith in Northern Senegal to represent key issues in land use competition ( D ’ Aquino et al ., 2003 ; Lynam et al ., 2003 ). Playing the game allowed herders and farmers to understand the perspective of the other group about access to water and facilitated consensus about policy options to be investigated . To investigate the likely outcome of the options , the game rules and player behavior were converted by the researchers to an agent-based model ( a form of computer simulation ), which the villagers were able to understand because of their experience with the board game .
This use of a computer simulation in conjunction with the land use game highlights the strength of these ( and mathematical ) models . They are used extensively for forecasting and options comparison because they are able to represent relationships by computer code or equations and apply that knowledge to hypothetical situations to estimate costs , benefits , and other consequences of policy decisions even in the presence of unknowns . The System Dynamics model used to plan water management for a section of the Rio Grande described above ( Tidwell et al ., 2004 ) is typical of the way in which computer simulations are used to compare policy options . Model users could create complicated combinations of initiatives and estimate their joint effects . For example , individual initiatives include low water flow appliances in new and existing buildings , reducing agricultural land , removal of certain plants from the river bank , additional reservoir storage , desalination , and many other measures . For this project , the model allowed a compromise package to be developed by demonstrating that packages that were designed by those with specific interests ( such as environmental protection ) were unable to meet legal obligations about water quantity .
The Design and Build phases of computer simulation and mathematical models provide additional functionality . In particular , the rigorous process to define the relationships formally elicits and organizes knowledge and highlights unknowns , as the model cannot be run without all connections in place . Furthermore , the Test phase compares model outputs to whatever data is available , which provides a check for the credibility of believed relationships . From these examples , it is clear that there is no simple way to map modeling techniques and functionality . Much of the functionality is delivered through an effective modeling process ( summarized in Figure 2 ) rather than the particular technique used . This is particularly true of tasks such as eliciting and organizing knowledge , which can be delivered with any model . In addition , in any policy modeling project , the same model delivers several different functions .
However , there are some functionality differences between broad groups of modeling techniques . Computer and mathematical models allow forecasting and options comparison but the represented knowledge is generally less accessible than for diagrams and games . Computer models have an interface with which users can try out different decisions to provide experience based understanding of the system . Mathematical models generally rely on the modeler running scenarios and providing an analysis . Users have only limited direct access to the model rules , and the language of equations or computer code is usually difficult to understand .
These differences make some methods more or less suitable for certain policy objectives . Kelly ( Letcher ) et al . ( 2013 ) eval-
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