Modeling the Firm as an Artificial Neural Network

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Modeling the Firm as an Artificial Neural Network

Author Info

Jason Barr ()
Francesco Saraceno ()

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Abstract

The purpose of this chapter is two-fold: (1) to make the case that a standard backward propagation artificial neural network (ANN) can be used as a general model of the information processing activities of the firm, and (2) to present a synthesis of Barr and Saraceno (BS) (2002, 2004, 2005), who offer various models of the firm as an artificial neural network.

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Paper provided by Department of Economics, Rutgers University, Newark in its series Working Papers Rutgers University, Newark with number 2005-011.

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Length: 30 pages
Date of creation: Oct 2005
Date of revision:
Handle: RePEc:run:wpaper:2005-011

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Find related papers by JEL classification:
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques
D21 - Microeconomics - - Production and Organizations - - - Firm Behavior
D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets

This paper has been announced in the following NEP Reports:

NEP-ALL-2005-11-12 (All new papers)
NEP-CBE-2005-11-12 (Cognitive & Behavioural Economics)
NEP-CMP-2005-11-12 (Computational Economics)
NEP-ICT-2005-11-12 (Information & Communication Technologies)
NEP-MIC-2005-11-12 (Microeconomics)

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

Sgroi, D., 2003. "Using Neural Networks to Model Bounded Rationality in Interactive Decision-Making," Cambridge Working Papers in Economics 0339, Faculty of Economics, University of Cambridge. [Downloadable!]
Jason Barr & Francesco Saraceno, 2004. "Organization, Learning and Cooperation," Computational Economics 0402001, EconWPA. [Downloadable!]
Barr, Jason & Saraceno, Francesco, 2005. "Cournot competition, organization and learning," Journal of Economic Dynamics and Control, Elsevier, vol. 29(1-2), pages 277-295, January. [Downloadable!] (restricted)
Myong-Hun Chang & Joseph E Harrington Jr, 2004. "Agent-Based Models of Organizations," Economics Working Paper Archive 515, The Johns Hopkins University,Department of Economics. [Downloadable!]
Other versions:
- Chang, Myong-Hun & Harrington, Joseph Jr., 2006. "Agent-Based Models of Organizations," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 26, pages 1273-1337 Elsevier. [Downloadable!] (restricted)
Chung-Ming Kuan & Halbert White, 1992. "Artificial Neural Networks: An Econometric Perspective," University of California at San Diego, Economics Working Paper Series 92-11, Department of Economics, UC San Diego.
Other versions:
- Chung-Ming Kuan & Halbert White, 1994. "Artificial neural networks: an econometric perspective," Econometric Reviews, Taylor and Francis Journals, vol. 13(1), pages 1-91. [Downloadable!] (restricted)

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