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The model for the biologically inspired agent-based computation systems EMAS and iEMAS conformed to BDI standard is presented. System dynamics was modeled as the stationary Markov chain. The space of states and transition functions were identified. The probability transition of the whole system is composed of the conditional transitions caused by the particular actions. Such a model allows for better understanding the behavior of the proposed complex systems as well as their limitations. Because no constraint for the total number of agents was introduced, the model express the behavior of maximum configuration of the systems. Therefore it plays the similar role to the SGA infinite population model introduced by Vose. The sample application of iEMAS to the difficult global optimization problem (optimization of the artificial neural network architecture) showing its efficiency was also attached.