Real-Time Decentralized Information Processing and Returns to Scale

Abstract

We study the properties of real-time decentralized information processing, as a model of human information processing in organizations, and use the model to understand how constraints on human information processing affect the returns to scale of firms. With real-time processing, decentralization does not unambiguously reduce delay, because processing a subordinate's report precludes processing current data. Because decision rules are endogenous, delay does not inexorably lead to eventually decreasing returns to scale; however, returns are more likely to be decreasing when computation constraints, rat her than sampling costs, limit the information upon which decisions are conditioned. The results illustrate that the requirement of informational integration causes a breakdown of the replication arguments that are often used to establish non-decreasing returns.