The paper examines the application of the concept of economic efficiency toudorganizational issues of collective information processing in decision making. Informationudprocessing is modeled in the framework of the dynamic parallel-processing model ofudassociative computation with an endogenous set-up cost of the processors. The model isudextended to include the specific features of collective information processing in the team ofuddecision makers which could cause an error in data analysis. In such a model, the conditionsudfor efficient organization of information processing are defined and the architecture of theudefficient structures is considered. We show that specific features of collective decisionudmaking procedures require a broader framework for judging organizational efficiencyudthan has traditionally been adopted. In particular, and contrary to the results presented inudeconomic literature, we show that in human data processing (unlike in computer systems),udthere is no unique architecture for efficient information processing structures, but a number ofudvarious efficient forms can be observed. The results indicate that technological progressudresulting in faster data processing (ceteris paribus) will lead to more regular informationudprocessing structures. However, if the relative cost of the delay in data analysis increasesudsignificantly, less regular structures could be efficient. (authors' abstract)
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