LINKING AGENT-BASED COMPUTATIONAL ECONOMICS TO

Year 2017, , 1 - 17, 01.01.2017

M. Oğuz Arslan

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

Keywords

Economic Methodology, Post Keynesian Economics, Agent-based Computational Economics

LINKING AGENT-BASED COMPUTATIONAL ECONOMICS TO POST KEYNESIAN ECONOMICS

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.

Keywords

Ekonomik Metodoloji, Post Keynesyen İktisat, Ajan Tabanlı Hesaplamalı İktisat

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.