Barr and Saraceno (JEDC, forthcoming) model the firm as a type of artificial neural network (ANN) which plays a repeated Cournot game. Each period, the network/firm must estimate the relationship between environmental conditions and optimal output. Among other results, the paper develops the notion of a Network Size Equilibrium (NSE): which is an optimal network size for each of the players. The concept of NSE allows us to map environmental complexity to a type of industrial structure, i.e., the average network size in equilibrium. This paper builds on the previous work by exploring the dynamic adjustment process of networks. That is to say, we explore how the network (firm) evolves over time in reaction to the environmental complexity and the behavior of its rival. We model how firms endogenously "grow" over time in the adjustment process toward a network size equilibrium by exploring different adjustment algorithms, which may involve different costs. Further we explore the stability and the types of equilibria that can emerge, given different environmental scenarios.
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