Sciences Po Spire - A Computational Theory of the Firm

Type:		Article

Title:		A Computational Theory of the Firm

Author(s):		Barr, Jason (Author) Saraceno, Francesco - OFCE (Author)

In:		Journal of Economic Behavior and Organization

Date issued:		2002-11

Volume:		49

Issue:		3

Pages:		345-361 p.

Notes:		doi:10.1016/S0167-2681(02)00002-1

ISSN:		01672681

Keywords:		Firm learning, Information Processing, Neural Networks

JEL:		C63, D21, D83, L20

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.

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