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A polynomial-time algorithm for the identification of interaction and memory structures in discrete valued, nondeterministic, cyclic social behavior data is developed. The output of the probabilistic search algorithm is the strategy update function for each individual automaton agent in given population. For our modeling purpose, we used automata networks model and added “block-extended memory” property to its original definition. The approach can also be considered as a limit cycle construction technique for discrete

IDENTIFICATION OF STRUCTURE IN NONDETERMINISTIC CYCLIC SOCIAL CONVENTIONS