Efficient Organization of Collective Data-Processing

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