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The presence of noise and the availability of a limited number of samples prevent the transition probabilities of a gene regulatory network from being accurately estimated. Thus, it is important to study the effect of modeling errors on the final outcome of an intervention strategy and to design robust intervention strategies. Two major approaches applied to the design of robust policies in general are the Mini-Max (worst case) approach and the Bayesian approach. In this paper we will compare the Minimax, Bayesian and Global robustness approach with respect to intervention in genetic regulatory networks.