The computational solution of large scale linear programming problems con- tains various di±culties. One of the most di±cult things is to ensure numerical stability. We have another di±culty of di®erent nature, namely the original data we are using also contains errors. In this paper we shall show that the e®ect of the random errors in the original data has a diminishing tendency for the optimal value as the number of constraints and the number of variables increase. We obtain laws of large numbers under random linear programs. We extend Prekopa\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s results (1972) to the case of asso- ciated random variables.

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