Information Processing in Firms and Returns to Scale

What are the returns to scale in decision-making, when information processing is costly? In a parallel-processing model of a firm, we characterize efficient networks for associative operations, where efficiency is measured in terms of the number of individual processors (of given capacity) and the information-processing delay. We then embed this structure in a model of decision-making under uncertainty, and find that returns to scale can vary from increasing to sharply decreasing, depending on the intercorrelation of the data and on the loss function for incorrect decisions.