Random Initialization Methods in Simulation

Abstract

In a steady-state simulation, the initial conditions often bias the estimators, sometimes severely. A common method for ameliorating this is to delete an initial portion of the run, perhaps entailing substantial data loss. We investigate feasible methods for initializing such simulations that lead to lower estimator bias or, alternatively, less requisite deletion. Deterministic and stochastic initialization rules are compared, with appropriately chosen stochastic rules' being preferable in terms of several measures of quality of point estimators and confidence intervals. Forms for the initial distribution are suggested by the maximum entropy principle, the parameters of which may be specified from short pilot runs. These initialization rules are tested on a range of models with analytical tractability varying From complete to none.

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