Advertisement

Agent-Based Computational Economics and Industrial Organization Theory

  • Claudia NardoneEmail author
Chapter
First Online:
  • 233 Downloads
Part of the Computational Social Sciences book series (CSS)

Abstract

Agent-based computational economics (ACE) is “the computational study of economic processes modeled as dynamic systems of interacting agents.” This new perspective offered by agent-based approach makes it suitable for building models in industrial organization (IO), whose scope is the study of the strategic behavior of firms and their direct interactions. Better understanding of industries’ dynamics is useful in order to analyze firms’ contribution to economic welfare and improve government policy in relation to these industries.

Keywords

Agent-based computational economics Industrial organization theory Bounded rationality Complexity Strategic behavior of firms 
This is a preview of subscription content, log in to check access.

References

  1. Arthur, W. B. (2006). Out-of-equilibrium economics and agent-based modeling. In Handbook of computational economics. Elsevier, Amsterdam, North Holland (Vol. 2, pp. 1551–1564).Google Scholar
  2. Arthur, W. B., Holland, J. H., LeBaron, B., Palmer, R., &Taylor, P. (1996). Asset pricing under endogenous expectation in an artificial stock market. Santa Fe Institute, Working Paper No. 96-12-093.Google Scholar
  3. Arthur, W. B., Kollman, K., Miller, J., Page, S., Durlauf, S. N., & Lane, D. A. (1997). Computational political economy. In The economy as an evolving complex system II (Vol. 17, pp. 461–490).Google Scholar
  4. Barr, J., & Saraceno, F. (2005). Cournot competition, organization and learning. Journal of Economic Dynamics and Control, 29(1–2), 277–295.CrossRefGoogle Scholar
  5. Caiani, A., Godin, A., Caverzasi, E., Gallegati, M., Kinsella, S., & Stiglitz, J. E. (2016). Agent based-stock flow consistent macroeconomics: Towards a benchmark model. Journal of Economic Dynamics and Control, 69, 375–408.CrossRefGoogle Scholar
  6. Chang, M. H. (2011). Entry, exit, and the endogenous market structure in technologically turbulent industries. Eastern Economic Journal, 37(1), 51–84.CrossRefGoogle Scholar
  7. Chen, S. H. (2012). Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective. Journal of Economic Dynamics and Control, 36(1), 1–25.CrossRefGoogle Scholar
  8. Colander, D., Goldberg, M., Haas, A., Juselius, K., Kirman, A., Lux, T., & Sloth, B. (2009). The financial crisis and the systemic failure of the economics profession. Critical Review, 21(2–3), 249–267.CrossRefGoogle Scholar
  9. Cristelli, M., Pietronero, L., & Zaccaria, A. (2011). Critical overview of agent-based models for economics. arXiv preprint arXiv:1101.1847.Google Scholar
  10. Delli Gatti, D., Desiderio, S., Gaffeo, E., Cirillo, P., & Gallegati, M. (2011). Macroeconomics from the Bottom-up (Vol. 1). Springer Science & Business Media, Springer, Milano.Google Scholar
  11. Diao, J., Zhu, K., & Gao, Y. (2011). Agent-based simulation of durables dynamic pricing. Systems Engineering Procedia, 2, 205–212.CrossRefGoogle Scholar
  12. Dosi, G. (2012). Economic coordination and dynamics: Some elements of an alternative “evolutionary” paradigm (No. 2012/08). LEM Working Paper Series.Google Scholar
  13. Dosi, G., Fagiolo, G., & Roventini, A. (2010). Schumpeter meeting Keynes: A policy-friendly model of endogenous growth and business cycles. Journal of Economic Dynamics and Control, 34(9), 1748–1767.CrossRefGoogle Scholar
  14. Dosi, G., Napoletano, M., Roventini, A., & Treibich, T. (2017). Micro and macro policies in the Keynes+ Schumpeter evolutionary models. Journal of Evolutionary Economics, 27(1), 63–90.CrossRefGoogle Scholar
  15. Fagiolo, G., & Roventini, A. (2012). Macroeconomic policy in DSGE and agent-based models. Revue de l'OFCE, 124(5), 67–116.CrossRefGoogle Scholar
  16. Fagiolo, G., &Roventini, A. (2016). Macroeconomic policy in DSGE and agent-based models redux: New developments and challenges ahead. Available at SSRN 2763735.Google Scholar
  17. Farmer, J. D., & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460(7256), 685.CrossRefGoogle Scholar
  18. Goodfriend, M., & King, R. G. (1997). The new neoclassical synthesis and the role of monetary policy. NBER Macroeconomics Annual, 12, 231–283.CrossRefGoogle Scholar
  19. Guerci, E., Rastegar, M. A., & Cincotti, S. (2010). Agent-based modeling and simulation of competitive wholesale electricity markets. In Handbook of power systems II (pp. 241–286). Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
  20. Hämäläinen, R. P., Mäntysaari, J., Ruusunen, J., & Pineau, P. O. (2000). Cooperative consumers in a deregulated electricity market—Dynamic consumption strategies and price coordination. Energy, 25(9), 857–875.CrossRefGoogle Scholar
  21. Holland, J. H., & Miller, J. H. (1991). Artificial adaptive agents in economic theory. The American Economic Review, 81(2), 365–370.Google Scholar
  22. Kay, A. (2011). UK monetary policy change during the financial crisis: Paradigms, spillovers, and goal co-ordination. Journal of Public Policy, 31(2), 143–161.CrossRefGoogle Scholar
  23. Keynes, J. (1956). M. 1936. The general theory of employment, interest and money, pp. 154–6.Google Scholar
  24. Kirman, A. (2010). The economic crisis is a crisis for economic theory. CESifo Economic Studies, 56(4), 498–535.CrossRefGoogle Scholar
  25. Kirman, A. (2016). Ants and nonoptimal self-organization: Lessons for macroeconomics. Macroeconomic Dynamics, 20(2), 601–621.CrossRefGoogle Scholar
  26. Kok K, Derzsi Z, Gordijn J, Hommelberg M, Warmer C, Kamphuis R, Akkermans H (2008) Agent-based electricity balancing with distributed energy resources, a multiperspective case study. In: HICSS ’08: Proceedings of the 41st Annual Hawaii International Conference on System Sciences, IEEE Computer Society, Washington, DC, USA.Google Scholar
  27. Krugman, P. (2009). How did economists get it so wrong? New York Times, 2(9), 2009.Google Scholar
  28. LeBaron, B. (2006). Agent-based computational finance. In Handbook of computational economics. Elsevier, Amsterdam, North Holland (Vol. 2, pp. 1187–1233).Google Scholar
  29. Müller, M., Sensfuß, F., & Wietschel, M. (2007). Simulation of current pricing-tendencies in the German electricity market for private consumption. Energy Policy, 35(8), 4283–4294.CrossRefGoogle Scholar
  30. Newman, M. E., Strogatz, S. H., & Watts, D. J. (2001). Random graphs with arbitrary degree distributions and their applications. Physical Review E, 64(2), 026118.CrossRefGoogle Scholar
  31. Rand, W., & Rust, R. T. (2011). Agent-based modeling in marketing: Guidelines for rigor. International Journal of Research in Marketing, 28(3), 181–193.CrossRefGoogle Scholar
  32. Rixen, M., & Weigand, J. (2014). Agent-based simulation of policy induced diffusion of smart meters. Technological Forecasting and Social Change, 85, 153–167.CrossRefGoogle Scholar
  33. Romer, P. (2016). The trouble with macroeconomics. The American Economist, 20, 1–20.Google Scholar
  34. Roop, J. M., & Fathelrahman, E. (2003). Modeling electricity contract choice: An agent-based approach. In Summer study for energy efficiency in industry. New York. http://gridwise.pnl.gov/docs/pnnl38282.pdf, downloaded, 29, 2005.
  35. Rumley, S., Kaegi, E., Rudnick, H., & Germond, A. (2008). Multi-agent approach to electrical distribution networks control. In Computersoftware and applications, COMPSAC ‘08. 32ndAnnual IEEE international, 28 2008-aug. 1 2008 (pp. 575–580).Google Scholar
  36. Sanchez-Cartas, J. M. (2018). Agent-based models and industrial organization theory. A price-competition algorithm for agent-based models based on Game Theory. Complex Adaptive Systems Modeling, 6(1), 2.CrossRefGoogle Scholar
  37. Schelling, T. C. (1978). Micromotives and macrobehavior. W. W. Norton & Company, New York.Google Scholar
  38. Shubik, M. (1975). The uses and methods of gaming (pp. 49–116). New York: Elsevier.Google Scholar
  39. Simon, H. A. (1987). Behavioral economics. In J. Eatwell, M. Milgate, & P. Newman (Eds.), The New Palgrave (pp. 221–225). London: Macmillan.Google Scholar
  40. Stiglitz, J. E. (2011). Rethinking macroeconomics: What failed, and how to repair it. Journal of the European Economic Association, 9(4), 591–645.CrossRefGoogle Scholar
  41. Stiglitz, J. E. (2015). Reconstructing macroeconomic theory to manage economic policy. In E. Laurent & J. Le Cacheux (Eds.), Fruitful economics: Papers in honor of and by Jean-Paul Fitoussi (pp. 20–49). Basingstoke: Palgrave Macmillan.Google Scholar
  42. Tesfatsion, L. (2003). Agent-based computational economics: Modeling economies as complex adaptive systems. Information Sciences, 149(4), 262–268.CrossRefGoogle Scholar
  43. Tesfatsion, L. (2006). Agent-based computational economics: A constructive approach to economic theory. In Handbook of computational economics. Elsevier, Amsterdam, North Holland (Vol. 2, pp. 831–880).Google Scholar
  44. van Leeuwen, E., & Lijesen, M. (2016). Agents playing Hotelling’s game: An agent-based approach to a game theoretic model. The Annals of Regional Science, 57(2–3), 393–411.CrossRefGoogle Scholar
  45. Von Hayek, F. A. (1937). Economics and knowledge. Economica, 4(13), 33–54.CrossRefGoogle Scholar
  46. Yu, J., Zhou, J. Z., Yang, J., Wu, W., Fu, B., & Liao, R. T., (2004). Agent-based retail electricity market: Modeling and analysis. Proceedings of the third international conference on machine learning and cybernetics, Shanghai.Google Scholar
  47. Zhang, T., & Brorsen, B. W. (2011). Oligopoly firms with quantity-price strategic decisions. Journal of Economic Interaction and Coordination, 6(2), 157.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.CEIS – Centre for Economic and International Studies, Faculty of Economics – University of Rome “Tor Vergata”RomeItaly

Personalised recommendations

Cite chapter

Buy options