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Modeling the Firm as an Artificial Neural Network

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Author Info

  • Jason Barr

    ()

  • Francesco Saraceno

    ()

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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File URL: http://www.rutgers-newark.rutgers.edu/econnwk/workingpapers/2005-011.pdf
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Bibliographic Info

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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Related research

Keywords: neural networks; information processing; firm learning; agent-based;

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References

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  1. Jason Barr & Francesco Saraceno, 2004. "Organization, Learning and Cooperation," Computational Economics 0402001, EconWPA.
  2. 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.
  3. 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.
  4. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
  5. 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.
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Cited by:
  1. Guido Fioretti, 2002. "Recognizing Investment Opportunities at the Onset of Recoveries," Macroeconomics 0207008, EconWPA.

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