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LINKING AGENT-BASED COMPUTATIONAL ECONOMICS TO

Year 2017, Volume: 13 Issue: 1, 1 - 17, 01.01.2017
https://doi.org/10.17130/ijmeb.20173126260

Abstract

Agent-based computational economics is relatively a new methodology in economics. It is defined as ‘the computational modeling of economic processes including whole economies as open-ended dynamic systems of interacting agents’. Contrary to fundamental assumptions of neoclassical and mainstream approaches, agent-based computational economics assumes that a agents are heterogeneous and bounded rational decision makers in an economy, b an economy is a non-linear, complex and adaptive system. Moreover, these assumptions seem to hold true for Post Keynesian economics to a large extent. Given that, this study attempts to bring out similarities and potential links between these two economic thoughts

References

  • Arthur, W. B. (2006). Out-of-equilibrium economics and agent-based modeling. In Tesfatsion L. & Judd K. L. (eds), Handbook of computational economics, 2: Agent-based computational economics (pp. 1551-1564). Amsterdam: North-Holland.
  • Bassi, F. & Lang, D. (2016). Investment hysteresis and potential output: A post-Keynesian– Kaleckian agent-based approach, Economic Modelling, 52, 35-49.
  • Blume, L. E. & Durlauf, S. N. (2006). The economy as an evolving complex system. III. current perspectives and future directions. New York: Oxford University Press.
  • Botte, F. (2014). Instability in a macroeconomic agent based model, filling the gap between micro and macro theories. Working Paper. www.boeckler.de/pdf/v_2014_10_30_botte. pdf
  • Bruun, C. (2007). Chapter XIV: Agent-based computational economics, In Rennard J. P. (ed.), Handbook of Research on Nature-Inspired Computing for Economics and Management. (pp. 183-197). Hershey: Idea Group Reference
  • Bruun, C. (2008). Rediscovering the economics of Keynes in an agent-based computational setting. Paper presented at Agent-Based Modeling in Economics and Finance, Trento, Italy.
  • Bruun, C. & Heyn-Johnsen, C. (2009). The paradox of monetary profits: An obstacle to understanding financial and economic crisis?. Economics: The Open-Access, Open- Assessment, E-Journal, Discussion Paper, 52.
  • Bruun, C. (2010). The economics of Keynes in an almost stock-flow consistent agent- based setting. In Velupillai V. & Zambelli, S. (eds.), Computable, constructive and behavioural economic dynamics: Essays in honour of Kumaraswamy, (pp. 442-461). London: Routledge.
  • Buchanan, M. (2008). This economy does not compute. The New York Times, October 1.
  • Canzian, G., Gaffeo, E. & Tamborini, R. (2009). Keynes in the computer laboratory. An agent- based model with MEC, MPC, LP. In Hernandez C., Posada M. & Lopez-Paradez A. (eds), Artificial Economics: The Generative Method in Economics, (pp. 15-28), Heidelberg: Springer.
  • Cogliano, J. F. & Jiang, X. (2016). Agent-based computational economics: simulation tools for heterodox research. In Lee F. S. & Cronin B. (eds), Handbook of Research Methods and Application in Heterodox Economics. Cheltenham: Edward Elgar.
  • David, N. (2013). Validating simulations. In Edmonds B. & Meyer R. (eds), Simulating social complexity: A handbook. (pp. 135-171). Heidelberg: Springer.
  • Epstein, J. M. (2006a). Generative Social Science. New Jersey: Princeton University Press.
  • Epstein, J. M. (2006b). Remarks on the foundations of agent-based generative social science. In Tesfatsion L. & Judd K. L. (eds). Handbook of computational economics, (pp. 1585- 1604). 2: Agent-Based Computational Economics. Amsterdam: North-Holland.
  • Farmer, J. D. & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460 (7256). 685-686.
  • Gaffard, J-L. & Napoletano, M. (2012). Improving the toolbox: New advances in agent-based and computational models. In Gaffard J-L. & Napoletano M. (eds). Agent-Based Models and Economic Policy, (pp. 7-13). Paris: OFCE. www.ofce.sciences-po.fr/pdf/revue/124/ revue-124.pdf.
  • Gilbert, N. & Troitzsch, K. G. (2004). Simulation for the social sciences. Second Ed., New York: Open University Press.
  • Gilbert, N. (2008). Agent-based models. Los Angeles: Sage Publications.
  • Goodwin, R. M. (1967). A growth cycle’. In Feinstein C. H. (ed.), (pp. 54-58). Socialism, capitalism and economic growth: Essays presented to maurice dobb, Cambridge: Cambridge University Press.
  • Hasselman, K. (2010). Application of system dynamics to climate policy assessment. In Fitt A. D., Norbury J., Ockendon H. and Wilson E. (eds). (pp. 203-208). Progress in industrial mathematics at ECMI 2008. Heidelberg: Springer.
  • Janssen, M. (2012). Introduction to agent-based modeling. OpenABM – CoMSES Network. https://www.openabm.org/book/introduction-agent-based-modeling.
  • Lavoie, M. (2015). Post-Keynesian economics: New foundations. Cheltenham: Edward Elgar.
  • Lee, F. S. (2012). Heterodox economics and its critics. Review of Political Economy, 24 (2), 337-351.
  • Michell, J. (2014). A Steindlian account of the distribution of corporate profits and leverage: A stock-flow consistent macroeconomic model with agent-based microfoundations. The Post-Keynesian Economics Study Group. https://www.postkeynesian.net
  • Moore, B. J. (2006). Shaking the invisible hand: Complexity, endogenous money and exogenous interest rate. Hampshire: Palgrave Macmillan.
  • Moss, S. (2011). Agent based modeling and neoclassical economics: a critical perspective. In Meyers R. A. (ed.),(pp. 22-29). Complex systems in finance and econometrics, New York: Springer.
  • Radzicki, M. J. (2008). Institutional economics, post-Keynesian economics, and system dynamics: Three strands of a heterodox braid. In Harvey J. T. & Garnett R. F. (eds),(pp. 156-184). Future Directions for Heterodox Economics. Ann Arbor, MI: The University of Michigan Press.
  • Reid, S. G. (2014). Agent-based computational economic models. Turing finance. Retrieved: January 13. From http://www.turingfinance.com/agent-based-computational-economic- models/
  • Richiardi, M. G. (2012). Agent-based computational economics: a short introduction. The Knowledge Engineering Review, 27 (2),137-149.
  • Rosser, J. B. (2006). Complex dynamics and post Keynesian economics. In Setterfield M. (ed.). (pp. 74-98.). Complexity, endogenous money and macroeconomic theory, Cheltenham: Edward Elgar.
  • Rosser, J. B. (2009). Theoretical and policy issues in complex Post Keynesian ecological economics. In Holt P. F., Pressman S. & Spash C. L. (eds), (pp. 221-236.). Post Keynesian and ecological economics: Confronting environmental issues, Cheltenham: Edward Elgar.
  • Setterfield, M. & Budd, A. (2011). A Keynes-Kalecki model of cyclical growth with agent- based features”. In Arestis P. (ed.), (pp. 228-250). Microeconomics, macroeconomics and economic policy, Hampshire: Palgrave Macmillan.
  • The Economist (2010). Agents of change. Jul 22nd.
  • Tesfatsion, L. (2016). Agent-based computational economics: Growing economies from the bottom up. Iowa State University, Ames, Iowa. From http://www2.econ.iastate.edu/ tesfatsi/ace.htm.
  • Tesfatsion, L. (2006). Agent-based computational economics: A constructive approach to economic theory. In Tesfatsion L. and Judd K. L. (eds), (pp. 831-880). Handbook of computational economics, 2: Agent-based computational economics, Amsterdam: North-Holland.
  • Thornton, T. (2014). The coming complexity revolution? Heterodoxy in economics: from history to pluralism J. E. King Conference, April 15-16, 2014, Victoria University, Melbourne. https://www.vu.edu.au/sites/default/files/cses/pdfs/ thornton-paper.pdf.
  • Wikipedia(a). Complex adaptive system. From https://en.wikipedia.org/wiki/Complex_ adaptive_system.
  • Wikipedia(b). Complex dynamics. From https://en.wikipedia.org/wiki/Complex_ dynamics.
  • Wilensky, U. & Rand, W. (2015). An introduction to agent-based modeling: Modeling natural, social and engineered complex systems with Netlogo. Cambridge, MA: The MIT Press.
  • Wilensky, U. (2011). Netlogo simple economy model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. From http://ccl. northwestern.edu/netlogo/models/IABMTextbook/SimpleEconomy.
  • Wilensky, U. (2005). NetLogo wolf sheep predation (system dynamics) model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/ wolfsheeppredation(system dynamics).
  • Wilensky, U. (1997). Netlogo segregation model. Center for Connected Learning and Computer- Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/ netlogo/models/segregation
  • Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern. edu/netlogo/
  • https://www.postkeynesian.net/about/
  • http://www.complexityexplorer.org/explore/glossary.

LINKING AGENT-BASED COMPUTATIONAL ECONOMICS TO POST KEYNESIAN ECONOMICS

Year 2017, Volume: 13 Issue: 1, 1 - 17, 01.01.2017
https://doi.org/10.17130/ijmeb.20173126260

Abstract

Agent-based computational economics is relatively a new methodology in economics. It is defined as ‘the computational modeling of economic processes including whole economies as open-ended dynamic systems of interacting agents’. Contrary to fundamental assumptions of neoclassical and mainstream approaches, agent-based computational economics assumes that a agents are heterogeneous and bounded rational decision makers in an economy, b an economy is a non-linear, complex and adaptive system. Moreover, these assumptions seem to hold true for Post Keynesian economics to a large extent. Given that, this study attempts to bring out similarities and potential links between these two economic thoughts.

References

  • Arthur, W. B. (2006). Out-of-equilibrium economics and agent-based modeling. In Tesfatsion L. & Judd K. L. (eds), Handbook of computational economics, 2: Agent-based computational economics (pp. 1551-1564). Amsterdam: North-Holland.
  • Bassi, F. & Lang, D. (2016). Investment hysteresis and potential output: A post-Keynesian– Kaleckian agent-based approach, Economic Modelling, 52, 35-49.
  • Blume, L. E. & Durlauf, S. N. (2006). The economy as an evolving complex system. III. current perspectives and future directions. New York: Oxford University Press.
  • Botte, F. (2014). Instability in a macroeconomic agent based model, filling the gap between micro and macro theories. Working Paper. www.boeckler.de/pdf/v_2014_10_30_botte. pdf
  • Bruun, C. (2007). Chapter XIV: Agent-based computational economics, In Rennard J. P. (ed.), Handbook of Research on Nature-Inspired Computing for Economics and Management. (pp. 183-197). Hershey: Idea Group Reference
  • Bruun, C. (2008). Rediscovering the economics of Keynes in an agent-based computational setting. Paper presented at Agent-Based Modeling in Economics and Finance, Trento, Italy.
  • Bruun, C. & Heyn-Johnsen, C. (2009). The paradox of monetary profits: An obstacle to understanding financial and economic crisis?. Economics: The Open-Access, Open- Assessment, E-Journal, Discussion Paper, 52.
  • Bruun, C. (2010). The economics of Keynes in an almost stock-flow consistent agent- based setting. In Velupillai V. & Zambelli, S. (eds.), Computable, constructive and behavioural economic dynamics: Essays in honour of Kumaraswamy, (pp. 442-461). London: Routledge.
  • Buchanan, M. (2008). This economy does not compute. The New York Times, October 1.
  • Canzian, G., Gaffeo, E. & Tamborini, R. (2009). Keynes in the computer laboratory. An agent- based model with MEC, MPC, LP. In Hernandez C., Posada M. & Lopez-Paradez A. (eds), Artificial Economics: The Generative Method in Economics, (pp. 15-28), Heidelberg: Springer.
  • Cogliano, J. F. & Jiang, X. (2016). Agent-based computational economics: simulation tools for heterodox research. In Lee F. S. & Cronin B. (eds), Handbook of Research Methods and Application in Heterodox Economics. Cheltenham: Edward Elgar.
  • David, N. (2013). Validating simulations. In Edmonds B. & Meyer R. (eds), Simulating social complexity: A handbook. (pp. 135-171). Heidelberg: Springer.
  • Epstein, J. M. (2006a). Generative Social Science. New Jersey: Princeton University Press.
  • Epstein, J. M. (2006b). Remarks on the foundations of agent-based generative social science. In Tesfatsion L. & Judd K. L. (eds). Handbook of computational economics, (pp. 1585- 1604). 2: Agent-Based Computational Economics. Amsterdam: North-Holland.
  • Farmer, J. D. & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460 (7256). 685-686.
  • Gaffard, J-L. & Napoletano, M. (2012). Improving the toolbox: New advances in agent-based and computational models. In Gaffard J-L. & Napoletano M. (eds). Agent-Based Models and Economic Policy, (pp. 7-13). Paris: OFCE. www.ofce.sciences-po.fr/pdf/revue/124/ revue-124.pdf.
  • Gilbert, N. & Troitzsch, K. G. (2004). Simulation for the social sciences. Second Ed., New York: Open University Press.
  • Gilbert, N. (2008). Agent-based models. Los Angeles: Sage Publications.
  • Goodwin, R. M. (1967). A growth cycle’. In Feinstein C. H. (ed.), (pp. 54-58). Socialism, capitalism and economic growth: Essays presented to maurice dobb, Cambridge: Cambridge University Press.
  • Hasselman, K. (2010). Application of system dynamics to climate policy assessment. In Fitt A. D., Norbury J., Ockendon H. and Wilson E. (eds). (pp. 203-208). Progress in industrial mathematics at ECMI 2008. Heidelberg: Springer.
  • Janssen, M. (2012). Introduction to agent-based modeling. OpenABM – CoMSES Network. https://www.openabm.org/book/introduction-agent-based-modeling.
  • Lavoie, M. (2015). Post-Keynesian economics: New foundations. Cheltenham: Edward Elgar.
  • Lee, F. S. (2012). Heterodox economics and its critics. Review of Political Economy, 24 (2), 337-351.
  • Michell, J. (2014). A Steindlian account of the distribution of corporate profits and leverage: A stock-flow consistent macroeconomic model with agent-based microfoundations. The Post-Keynesian Economics Study Group. https://www.postkeynesian.net
  • Moore, B. J. (2006). Shaking the invisible hand: Complexity, endogenous money and exogenous interest rate. Hampshire: Palgrave Macmillan.
  • Moss, S. (2011). Agent based modeling and neoclassical economics: a critical perspective. In Meyers R. A. (ed.),(pp. 22-29). Complex systems in finance and econometrics, New York: Springer.
  • Radzicki, M. J. (2008). Institutional economics, post-Keynesian economics, and system dynamics: Three strands of a heterodox braid. In Harvey J. T. & Garnett R. F. (eds),(pp. 156-184). Future Directions for Heterodox Economics. Ann Arbor, MI: The University of Michigan Press.
  • Reid, S. G. (2014). Agent-based computational economic models. Turing finance. Retrieved: January 13. From http://www.turingfinance.com/agent-based-computational-economic- models/
  • Richiardi, M. G. (2012). Agent-based computational economics: a short introduction. The Knowledge Engineering Review, 27 (2),137-149.
  • Rosser, J. B. (2006). Complex dynamics and post Keynesian economics. In Setterfield M. (ed.). (pp. 74-98.). Complexity, endogenous money and macroeconomic theory, Cheltenham: Edward Elgar.
  • Rosser, J. B. (2009). Theoretical and policy issues in complex Post Keynesian ecological economics. In Holt P. F., Pressman S. & Spash C. L. (eds), (pp. 221-236.). Post Keynesian and ecological economics: Confronting environmental issues, Cheltenham: Edward Elgar.
  • Setterfield, M. & Budd, A. (2011). A Keynes-Kalecki model of cyclical growth with agent- based features”. In Arestis P. (ed.), (pp. 228-250). Microeconomics, macroeconomics and economic policy, Hampshire: Palgrave Macmillan.
  • The Economist (2010). Agents of change. Jul 22nd.
  • Tesfatsion, L. (2016). Agent-based computational economics: Growing economies from the bottom up. Iowa State University, Ames, Iowa. From http://www2.econ.iastate.edu/ tesfatsi/ace.htm.
  • Tesfatsion, L. (2006). Agent-based computational economics: A constructive approach to economic theory. In Tesfatsion L. and Judd K. L. (eds), (pp. 831-880). Handbook of computational economics, 2: Agent-based computational economics, Amsterdam: North-Holland.
  • Thornton, T. (2014). The coming complexity revolution? Heterodoxy in economics: from history to pluralism J. E. King Conference, April 15-16, 2014, Victoria University, Melbourne. https://www.vu.edu.au/sites/default/files/cses/pdfs/ thornton-paper.pdf.
  • Wikipedia(a). Complex adaptive system. From https://en.wikipedia.org/wiki/Complex_ adaptive_system.
  • Wikipedia(b). Complex dynamics. From https://en.wikipedia.org/wiki/Complex_ dynamics.
  • Wilensky, U. & Rand, W. (2015). An introduction to agent-based modeling: Modeling natural, social and engineered complex systems with Netlogo. Cambridge, MA: The MIT Press.
  • Wilensky, U. (2011). Netlogo simple economy model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. From http://ccl. northwestern.edu/netlogo/models/IABMTextbook/SimpleEconomy.
  • Wilensky, U. (2005). NetLogo wolf sheep predation (system dynamics) model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/netlogo/models/ wolfsheeppredation(system dynamics).
  • Wilensky, U. (1997). Netlogo segregation model. Center for Connected Learning and Computer- Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern.edu/ netlogo/models/segregation
  • Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. http://ccl.northwestern. edu/netlogo/
  • https://www.postkeynesian.net/about/
  • http://www.complexityexplorer.org/explore/glossary.
There are 45 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

M. Oğuz Arslan This is me

Publication Date January 1, 2017
Published in Issue Year 2017 Volume: 13 Issue: 1

Cite

APA Arslan, M. O. (2017). LINKING AGENT-BASED COMPUTATIONAL ECONOMICS TO POST KEYNESIAN ECONOMICS. Uluslararası Yönetim İktisat Ve İşletme Dergisi, 13(1), 1-17. https://doi.org/10.17130/ijmeb.20173126260