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Utilities of Simulations
Constructing computer simulations requires that researchers make the interactions and assumptions that are implicit in mental models explicit (Epstein 2008). Primary processes to be included (e.g., primary production) must be distinguished from processes judged appropriate to ignore (perhaps groundwater contributions to primary production), rules must be defined, and parameter values that help describe how elements interact are identified. In a collaborative effort team members of different disciplines come together to share ideas and data. Each team member uses implicit mental models to understand how the system functions, but many people have never made those models explicit. Making processes, rules, and parameters explicit can be an illuminating, rewarding, and challenging exercise. For example, for a group to work well together requires a understanding of required terms and some baseline desire and ability to communicate to scholars in other disciplines. Reaching common understanding on what the most salient components of a system may be and how those will be represented in a simulation promotes team building across disciplines (Axelrod 2006).
We agree with Epstein (2008) that the assumption many make of models is that their goal is to make predictions. Predictions can be made but often the assumptions of such models are so simplifying as to have little purchase in the real world. Myriad interactions and unforeseen changes make detailed predictions about future system states all but impossible in all but trivial circumstances. Prediction is rarely the goal of our work. Instead, we often seek to identify the magnitude and direction of change that may be expected in a system, for example, given the changes a particular policy or land management decision may make on the environment and for human wellbeing. Other work by ourselves and others uses hypothetical landscapes, and tests theory without being encumbered by specific circumstances (Griffin 2006).
More generally, simulation can explain relationships, which is distinct from prediction (Epstein 2008). Alternative core dynamics may be incorporated in simulations, and those dynamics treated as hypotheses to be tested in experiments (Peck 2004; Grimm and Railback 2006). For example, the influence of topography on animal behavior may be quantified by using the observed topography in simulations, then substituting a flat landscape. Simulation can guide data collection, with sensitivity analyses (i.e., varying a parameter across its reasonable range of values and exploring changes in output) identifying new questions and uncertainties and allowing data collection efforts to be prioritized. Gaps in understanding can be suggested if an application that incorporates current theory is unable to generate the expected responses. Complex patterns can be shown to have simple underpinnings (e.g., the classic graphic of the Mandlebrot set used to demonstrate the nature of fractals) and simple patterns may be shown to be produced by relationships more complex than assumed (Epstein 2008). Simulation is helpful where analytical, differential equation-based approaches may become mathematically complex and intractable. Lastly, simulation is helpful when manipulations to real systems would be too costly, disruptive, or unethical (Peck 2004).
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