Abstract
This paper presents a conceptually simple approach on adjusting the time step size in a stochastic discrete time agent-based simulation and demonstrates how this could be done in practical implementation. The choice of time step size in such a system is often based on the nature of the phenomenon to be modelled and the tolerated simulation time. A finer time scale may be desired upon the introduction of new events which could possibly change the system state in smaller time intervals. Our approach divides each original time step into any integral number of equally spaced sub-steps based on simple assumptions, and thus allows a simulation system to incorporate such events and produce results with finer time scale. Regarding the tradeoff between finer scale and higher use of resource, our approach also highlights the implementation techniques that increase the resource usage and simulation time only marginally. We analyze the results of this refinement on a stochastic simulation model for epidemic spread and compare the results with the original system without
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