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Cournot Competition and Endogenous Firm Size

  • Francesco Saraceno
  • Jason Barr

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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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2004 with number 129.

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Date of creation: 11 Aug 2004
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Handle: RePEc:sce:scecf4:129
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  1. Martin Currie & Stan Metcalfe, 2001. "Firm routines, customer switching and market selection under duopoly," Journal of Evolutionary Economics, Springer, vol. 11(4), pages 433-456.
  2. Alfred D. Chandler, 1969. "Strategy and Structure: Chapters in the History of the American Industrial Enterprise," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262530090, June.
  3. Nickell, Stephen J, 1996. "Competition and Corporate Performance," Journal of Political Economy, University of Chicago Press, vol. 104(4), pages 724-46, August.
  4. Jan W. Rivkin & Nicolaj Siggelkow, 2003. "Balancing Search and Stability: Interdependencies Among Elements of Organizational Design," Management Science, INFORMS, vol. 49(3), pages 290-311, March.
  5. 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 Elsevier.
  6. Barr, Jason & Saraceno, Francesco, 2009. "Organization, learning and cooperation," Journal of Economic Behavior & Organization, Elsevier, vol. 70(1-2), pages 39-53, May.
  7. Evans, David S, 1987. "Tests of Alternative Theories of Firm Growth," Journal of Political Economy, University of Chicago Press, vol. 95(4), pages 657-74, August.
  8. 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.
  9. DeCanio, Stephen J. & Watkins, William E., 1998. "Information processing and organizational structure," Journal of Economic Behavior & Organization, Elsevier, vol. 36(3), pages 275-294, August.
  10. Barr, Jason & Saraceno, Francesco, 2002. "A computational theory of the firm," Journal of Economic Behavior & Organization, Elsevier, vol. 49(3), pages 345-361, November.
  11. Radner, Roy, 1993. "The Organization of Decentralized Information Processing," Econometrica, Econometric Society, vol. 61(5), pages 1109-46, September.
  12. Jovanovic, Boyan, 1982. "Selection and the Evolution of Industry," Econometrica, Econometric Society, vol. 50(3), pages 649-70, May.
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