# How Many Times Should a Stochastic Model Be Run? An Approach Based on Confidence Intervals

- Michael Byrne,
*Rice University*

## Abstract

A persistent problem in computational cognitive modeling is that
many models are stochastic. If a model is stochastic, what is the prediction made
by the model? In general, this problem is solved via Monte Carlo simulation. This
raises the question of how many runs of the model are adequate to produce a
meaningful prediction, a question that has received surprisingly little attention
from the community. This paper proposes a systematic approach to the selection of
the number of model runs based on confidence intervals and provides tables and
computational examples.

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