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Potential, Erfolge und Herausforderungen der Agenten-basierten Modellierung in den Wirtschaftswissenschaften

  • Herbert DawidEmail author
Aufsätze
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Zusammenfassung

Dieser Artikel diskutiert die Anwendung von Agenten-basierten Modellen in der volkswirtschaftlichen Forschung. Es wird eine kurze Einführung in die ökonomische Analyse mittels Agenten-basierter Modelle gegeben und die Entwicklung der entsprechenden Forschung in den letzten Jahren skizziert. Schließlich werden die wichtigsten Vorzüge des Ansatzes und auch die zentralen Herausforderungen diskutiert.

Schlüsselwörter

Agenten-basierte Modellierung Heterogenität Beschränkte Rationalität Empirische Valdidierung Politikanalyse 

Potential, achievements and challenges of agent-based modeling in economics

Abstract

This paper discusses the application of agent-based models in Economics. A short introduction to economic analysis by means of agent-based models is given and the development of research in this area during the last years is sketched. Furthermore, the main advantages and challenges of this approach are discussed.

Keywords

Agent-based modeling Heterogeneity Bounded rationality Empirical validation Policy analysis 

Notes

Danksagung

Der Autor ist dankbar für hilfreiche Kommentare von Philipp Harting und Michael Neugart.

Literatur

  1. Anufriev, M., Assenza, T., Hommes, C., & Massaro, D. (2013). Interest rules and macroeconomic stability under hetergenous expectations. Macroeconomic Dynamics, 17, 1574–1604.Google Scholar
  2. Arifovic, J., & Ledyard, J. (2010). A behavioral model for mechanism design: individual evolutionary learning. Journal of Economic Behavior and Organization, 78, 374–395.Google Scholar
  3. Arifovic, J., Bullard, J., & Kostyshyna, O. (2013). Social learning and monetary policy rules. Economic Journal, 123, 38–76.Google Scholar
  4. Arifovic, J., Dawid, H., Deissenberg, C., & Kostyshyna, O. (2010). Learning benevolent leadership in a heterogenous agents economy. Journal of Economic Dynamics and Control, 34, 1768–1790.Google Scholar
  5. Arthur, W., Holland, J., LeBaron, B., Palmer, R., & Taylor, P. (1997). Asset pricing and endogenous expectations in an artificial stock market. In W. Arthur, S. Durlauf & D. Lane (Hrsg.), The economy as an evolving complex system II. Reading, MA: Addison-Wesley.Google Scholar
  6. Artinger, F., & Gigerenzer, G. (2016). Heuristic pricing in an uncertain market: ecological and constructivist rationality. SSRN: Working Paper.Google Scholar
  7. Aymanns, C., Farmer, J. D., Kleinnijenhuis, A. M., & Wetzer, T. (2018). Models of financial stability and their application in stress tests. In C. Hommes & B. LeBaron (Hrsg.), The handbook of computational economics (Bd. 4, S. 329–391). Amsterdam: North-Holland.Google Scholar
  8. Balint, T., Lamperti, F., Mandel, A., Napoletano, M., Roventini, A., & Sapio, A. (2017). Complexity and the economics of climate change: a survey and a look forward. Ecological Economics, 158, 252–265.Google Scholar
  9. Barde, S., & van der Hoog, S. (2017). An empirical validation protocol for large-scale agent-based models. Bielefeld: Universität Bielefeld. Working Papers in Economics and Management No. 04-2017Google Scholar
  10. Battiston, S., Delli Gatti, D., Gallegati, M., Greenwald, B., & Stiglitz, J. (2007). Credit chains and bankruptcy propagation in production networks. Journal of Economic Dynamics and Control, 31, 2061–2084.Google Scholar
  11. Branch, W., & McGough, B. (2018). Heterogeneous expectations and micro-foundations in macroeconomics. In C. Hommes & B. LeBaron (Hrsg.), The handbook of computational economics (Bd. 4, S. 3–62). Amsterdam: North-Holland.Google Scholar
  12. Carroll, C. (2003). Macroeconomic expectations of households and professional forecasters. Quarterly Journal of Economics, 118, 269–298.Google Scholar
  13. Chang, M.-H. (2015). A computational model of industry dynamics. London.: Routledge.Google Scholar
  14. Dawid, H. (2015). Modeling the economy as a complex system. In B. A. Furtado, P. Sakowski & M. Tovolli (Hrsg.), Modeling complex systems for public policies (S. 191–216). Brasilia: IPEA.Google Scholar
  15. Dawid, H., & Delli Gatti, D. (2018). Agent-based macroeconomics. In C. Hommes & B. LeBaron (Hrsg.), The handbook of computational economics (Bd. 4, S. 63–156). Amsterdam: North-Holland.Google Scholar
  16. Dawid, H., & Gemkow, S. (2014). How do social networks contribute to wage inequality? Insights from an agent-based analysis. Industrial and Corporate Change, 23, 1171–1200.Google Scholar
  17. Dawid, H., & Harting, P. (2012). Capturing firm behavior in agent-based models of industry evolution and macroeconomic dynamics. In G. Bünsdorf (Hrsg.), Evolution, organization and economic behavior Bd. 6. Cheltenham: Edward Elgar.Google Scholar
  18. Dawid, H., & Reimann, M. (2004). Evaluating market attractiveness: individual incentives vs. industry profitability. Computational Economics, 24, 321–355.Google Scholar
  19. Dawid, H., Harting, P., & Neugart, M. (2014). Economic convergence: policy implications from a heterogeneous agent model. Journal of Economic Dynamics and Control, 44, 54–80.Google Scholar
  20. Dawid, H., Harting, P., & Neugart, M. (2018). Cohesion policy and inequality dynamics: insights from a heterogeneous agents macroeconomic model. Journal of Economic Behavior and Organization, 150, 220–255.Google Scholar
  21. De Grauwe, P., & Macchiarelli, C. (2015). Animal spirits and credit cycles. Journal of Economics Dynamics and Control, 58, 95–117.Google Scholar
  22. Dieci, R., & He, X.-Z. (2018). Heterogeneous agent models in finance. In C. Hommes & B. LeBaron (Hrsg.), The handbook of computational economics (Bd. 4, S. 257–328). Amsterdam: North-Holland.Google Scholar
  23. Dosi, G., Fagiolo, G., Napoletano, M., & Roventini, A. (2013). Income distribution, credit and fiscal policies in an agent-based Keynesian model. Journal of Economic Dynamics and Control, 37, 1598–1625.Google Scholar
  24. Dosi, G., Marengo, L., Bassanini, A., & Valente, M. (1999). Norms as emergent properties of adaptive learning: The case of economic routines. Journal of Evolutionary Economics, 9, 5–26.Google Scholar
  25. Dosi, G., Pereira, M., Roventini, A., & Virgillito, M. (2017). When more flexibility yields more fragility: the microfoundations of Keynesian aggregate unemployment. Journal of Economic Dynamics and Control, 81, 162–186.Google Scholar
  26. Duffy, J. (2012). Macroeconomics: a survey of laboratory research. In J. H. Kagel & A. E. Roth (Hrsg.), The handbook of experimental economics (Bd. 2, S. 1–90). Amsterdam: Elsevier.Google Scholar
  27. Fagiolo, G., & Roventini, A. (2017). Macroeconomic policy in DSGE and agent-based models redux: new developments and challenges ahead. Journal of Artificial Societies and Social Simulation, 20, 1.Google Scholar
  28. Farmer, D. J., & Foley, D. (2009). The economy needs agent-based modelling. Nature, 460, 685–686.Google Scholar
  29. Geisdendorf, S. (2018). Evolutionary climate-change modeling - A multi-agent climate-economic model. Computational Economics, 52, 921–951.Google Scholar
  30. Gemkow, S., & Neugart, M. (2011). Referral hiring, endogenous social networks, and inequality: an agent-based analysis. Journal of Evolutionary Economics, 21, 703–719.Google Scholar
  31. Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual Review of Psychology, 62, 451–482.Google Scholar
  32. Gilbert, N., Pyka, A., & Ahrweiler, P. (2001). Innovation networks-a simulation approach. Journal of Artificial Societies and Social Simulation, 4, 3.Google Scholar
  33. Goudet, O., Kant, J.-D., & Ballot, G. (2015). Forbidding fixed duration contracts: unfolding the opposing effects with a multiagent model of the French labour market. In F. Amblard, F. Miguel, A. Blanchet & B. Gaudou (Hrsg.), Advances in artificial economics, lecture notes in economics and mathematical systems (Bd. 676, S. 151–167). Berlin: Springer.Google Scholar
  34. Haldane, A. (2016). „The dappled world“, GLS Shackle Biennial Memorial Lecture. https://www.bis.org/review/r161115a.pdf Google Scholar
  35. Haldane, A. G., & Turrell, A. E. (2019). Drawing on different disciplines: macroeconomic agent-based models. Journal of Evolutionary Economics.  https://doi.org/10.1007/s00191-018-0557-5 Google Scholar
  36. Haruvy, E., Lahav, Y., & Noussair, C. (2007). Traders’ expectations in asset markets: experimental evidence. American Economic Review, 97, 1901–1920.Google Scholar
  37. Hommes, C., Sonnemans, J., Tuinstra, J., & van de Velden, H. (2005). Coordination of expectations in asset pricing experiments. Review of Financial Studies, 18, 955–980.Google Scholar
  38. Howitt, P. (2012). What have central bankers learned from modern macroeconomic theory? Journal of Macroeconomics, 34, 11–22.Google Scholar
  39. Kirman, A. (1992). Whom or what does the representative individual represent? Journal of Economic Perspectives, 6, 117–136.Google Scholar
  40. Kirman, A. (2016). Ants and Nonoptimal self-organization: lessons for macroeconomics. Macroeconomic Dynamics, 20, 601–621.Google Scholar
  41. Kirman, A., & Vriend, N. (2001). Evolving market: an ACE model of price dispersion and loyalty. Journal of Economics Dynamics and Control, 25, 495–502.Google Scholar
  42. Lamperti, F., Roventini, A., & Sani, A. (2018). Agent-based model calibration using machine learning surrogates. Journal of Economic Dynamics and Control, 90, 366–389.Google Scholar
  43. LeBaron, B. (2006). Agent-based computational finance. In L. Tesfatsion & K. Judd (Hrsg.), Handbook of computational economics (Bd. II, S. 1187–1233). Amsterdam: North-Holland.Google Scholar
  44. LeBaron, B., & Tesfatsion, L. (2008). Modeling macroeconomies as open-ended systems of interacting agents. American Economic Review: Papers and Proceedings, 98, 246–250.Google Scholar
  45. Lux, T., & Marchesi, M. (1999). Scaling and criticality in a stochastic multi-agent model of a financial market. Nature, 397, 498–500.Google Scholar
  46. Lux, T., & Zwinkels, C. (2018). Empirical validation of agent-based models. In C. Hommes & B. LeBaron (Hrsg.), The handbook of computational economics (Bd. 4, S. 437–488). Amsterdam: North-Holland.Google Scholar
  47. Malerba, F., Nelson, R., Orsenigo, L., & Winter, S. (1999). History-friendly models of industry evolution: the computer industry. Industrial and Corporate Change, 1, 3–41.Google Scholar
  48. Malerba, F., Nelson, R., Orsenigo, L., & Winter, S. (2016). Innovation and the evolution of industries: history-friendly models. Cambridge: Cambridge University Press.Google Scholar
  49. Nelson, R., & Winter, S. (1982). An evolutionary theory of economic change. Cambridge, MA.: Belknap Press.Google Scholar
  50. Neugart, M., & Richiardi, M. (2018). Agent-based models of the labor market. In S.-H. Chen, M. Kaboudan & Y.-R. Du (Hrsg.), The Oxford handbook of computational economics and finance (S. 667–687). Oxford: Oxford University Press.Google Scholar
  51. Ragot, X. (2018). Heterogeneous agents in the macroeconomy: reduced-heterogeneity representations in DSGE models. In C. Hommes & B. LeBaron (Hrsg.), The handbook of computational economics (Bd. 4, S. 215–253). Amsterdam: North-Holland.Google Scholar
  52. Russo, A., Riccetti, L., & Gallegati (2016). Increasing inequality, consumer credit and financial fragility in an agent based macroeconomic model. Journal of Evolutionary Economics, 26, 25–47.Google Scholar
  53. Sinitskaya, E., & Tesfatsion, L. (2015). Macroeconomies as constructively rational games. Journal of Economic Dynamics and Control, 61, 152–182.Google Scholar
  54. Tesfatsion, L. (2006). Agent-based computational economics: a constructive approach to economic theory. In L. Tesfatsion & K. Judd (Hrsg.), Handbook of computational economics (Bd. II, S. 831–880). Amsterdam: North-Holland.Google Scholar
  55. Trichet, J. (2010). Reflections on the nature of monetary policy non-standard measures and finance theory, Opening address at the ECB Central Banking Conference 2010. http://www.ecb.europa.eu/press/key/date/2010/html/sp101118.en.html Google Scholar
  56. Vallee, T., & Yildizoglu, M. (2006). Social and technological efficiency of patent systems. Journal of Evolutionary Economics, 16, 189–206.Google Scholar

Copyright information

© List-Gesellschaft e.V. 2019

Authors and Affiliations

  1. 1.Fakultät für Wirtschaftswissenschaften und Institut für Mathematische WirtschaftsforschungUniversität BielefeldBielefeldDeutschland

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