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Improving Confidence in Simulations through the Application of Multiple Updating Strategies

Today Cellular Automata (CA) and Agent Based Model (ABM) based simulations are used to make predictions and discover properties of real world scenarios, e.g. predicting the spread of influenza contagions. In these situations it is important that we have maximum confidence in the simulations we employ. One factor that is known to affect the outcomes of a simulation is the choice of updating strategy used. Specific strategies can cause artifacts – i.e. outcomes of a simulation not due to any intrinsic properties of the model itself but caused directly by the updating strategy. To guard against artifacts multiple strategies should be used on a model and the outcomes compared. Currently, while both Synchronous and asynchronous updating are used in CA based simulations, AU is used almost exclusively in ABM. This is due to: (i) until recently, the lack of any well-known SU algorithm that can handle complex ABM interactions and (ii) belief that AU is a better model of how the real world works.