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)