Modeling the Firm as an Artificial Neural Network

Modeling the Firm as an Artificial Neural Network

J. Barr (Observatoire Français des Conjonctures Économiques, France) and F. Saraceno (Rutgers University, USA)
DOI: 10.4018/978-1-59140-984-7.ch019
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The purpose of this chapter is to make the case that first a standard artificial neural network can be used as a general model of the information processing activities of the firm; second, to present a synthesis of Barr and Saraceno (2002, 2004, 2005), who offer various models of the firm as an artificial neural network. An important motivation of this work is the desire to bridge the gap between economists, who are mainly interested in market outcomes, and management scholars, who focus on firm organization. The first model has the firm in a price-taking situation. We show that increasing environmental complexity is associated with larger firm size and lower profits. In the second and third models, neural networks compete in a Cournot game. We demonstrate that they can learn to converge to the Cournot-Nash equilibrium and that optimal network sizes increase with complexity. In addition, we investigate the conditions that are necessary for two networks to learn to collude over time.

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