Real-Time Decentralized Information Processing and Returns to Scale

Timothy Van Zandt () and Roy Radner

No 1233, Discussion Papers from Northwestern University, Center for Mathematical Studies in Economics and Management Science

Abstract: We use a model of real-time decentralized information processing to understand how constraints on human information processing affect the returns to scale of organizations. We identify three informational (dis)economies of scale: diversification of heterogeneous risks (positive), sharing of information and of costs (positive), and crowding out of recent information due to information processing delay (negative). Because decision rules are endogenous, delay does not inexorably lead to decreasing returns to scale. However, returns are more likely to be decreasing when computation constraints, rather than sampling costs, limit the information upon which decisions are conditioned. The results illustrate how information processing constraints together with the requirement of informaitonal integration cause a breakdown of the replication arguments that have been used to establish nondecreasing technological returns to scale.

Keywords: returns to scale; real-time computation; decentralized information processing; organizations; bounded rationality (search for similar items in EconPapers)
JEL-codes: D23 D83 (search for similar items in EconPapers)
Date: 1998-11
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Citations: View citations in EconPapers (41)

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