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This paper studies the properties of real-time decentralized information processing as a model of human information processing in organizations. Real-time decentralized processing—which models the computation of decision rules in a temporal decision problem by members of an organization—captures both the cost of computation in terms of the members' time and the constraints imposed by computational delay on the use of recent information. Unlike a batch processing model, it has no single measure of delay because decisions are computed from data of heterogeneous lags. Furthermore, decentralization does not unambiguously reduce delay, because processing a message precludes processing current data.
Timothy Van Zandt (Thu,) studied this question.