Abstract
This paper discusses the need for, the mechanics of, and some potential application of agent-based modeling and computational analysis in industrial organization.
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Notes
See Doraszelski and Pakes [2007] for a comprehensive survey of this literature.
One may question whether real entrepreneurs in a turbulent market environment would be capable, even intuitively, of solving the type of complex optimization problems involved in this approach.
It should be noted that substantial efforts have been made in this literature to alleviate the computational burden while remaining within the MPE framework. See, for instance, Pakes and McGuire [2001], Doraszelski and Judd [2004], and Weintraub et al. [2008].
There are many ways to introduce agent heterogeneity, but the most obvious would be to assume heterogeneous levels of production efficiency that can be modified over time as firms engage in search for more efficient production methods for the sake of survival. [This is the approach taken in Chang [2009a, 2009b].]
See Chang [2009b] for this line of inquiry using an agent-based computational model.
See Axtell [1999] for an agent-based model that endogenously generates this type of distribution for firm sizes.
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I thank my colleagues, Jon Harford and Matthew Henry, for their insightful comments on an earlier version of this paper.
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Chang, MH. Agent-based Modeling and Computational Experiments in Industrial Organization: Growing Firms and Industries in silico. Eastern Econ J 37, 28–34 (2011). https://doi.org/10.1057/eej.2010.30
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DOI: https://doi.org/10.1057/eej.2010.30