Over the past decades, we see a trend that social scientists adopt the experiment approach to study our social and political world. Particularly, agent-based modelling (ABM) is employed as a tool for "thought experiment" because theorists usually (1) fall short of empirical data to contrast with experiment results and (2) are more interested in solving theoretical puzzles than empirical puzzles. Consequently, current application of ABM in social sciences (except the field of business

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... has not reached the stage of sound validation and verification (V&V). Researchers are usually not sure now stable their model will perform. To take a further step out of this situation, we suggest that researchers focus on internal validation and conduct sensitivity tests. We argue that this step at least ensures that simulation process and results pass such tests are more trustworthy than those that fail the tests. Moreover, we demonstrate the utility of using grid computing for sensitivity tests. We show how we identify a model's problem by analysing results of 8,470 runs of simulation derived from grid computing.