A Computational Theory of the Firm

Jason Barr and Francesco Saraceno ()
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Francesco Saraceno: OFCE - Observatoire français des conjonctures économiques (Sciences Po) - Sciences Po - Sciences Po

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Abstract: This paper proposes using computational learning theory (CLT) as a framework for analyzing the information processing behavior of firms; we argue that firms can be viewed as learning algorithms. The costs and benefits of processing information are linked to the structure of the firm and its relationship with the environment. We model the firm as a type of artificial neural network (ANN). By a simulation experiment, we show which types of networks maximize the net return to computation given different environments.

Keywords: Firm learning; Information Processing; Neural Networks (search for similar items in EconPapers)
Date: 2002-11
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Citations: View citations in EconPapers (19)

Published in Journal of Economic Behavior and Organization, 2002, 49 (3), pp.345 - 361. ⟨10.1016/S0167-2681(02)00002-1⟩

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Related works:
Journal Article: A computational theory of the firm (2002)
Working Paper: A Computational Theory of the Firm (2002)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03597701

DOI: 10.1016/S0167-2681(02)00002-1

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