Modeling the Firm as an Artificial Neural Network
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.
|Date of creation:||Oct 2005|
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- 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
- Jason Barr & Francesco Saraceno, 2009.
"Organization, learning and cooperation,"
Sciences Po publications
info:hdl:2441/9832, Sciences Po.
- Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
- 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.
- 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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