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Households Debt Behavior and Financial Instability: Towards an Agent-Based Model with Experimentally Estimated Behavioral Rules

  • Conference paper
Distributed Computing and Artificial Intelligence, 11th International Conference

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 290))

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Abstract

The present paper suggests the development of an experimentally microfounded Agent-based model in order to cope with the complexity and instability of the macroeconomic environment. The focus of the paper is on the microspecification of the ABM. For the micro level, I suggest to design an experiment in order to gain insights into households’ behaviors. For the macro level, I plan to build an ABM where agents are estimated, rather than calibrated, by using data collected in the experimental laboratory.

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References

  1. Velupillai, K.V.: Towards an algorithmic revolution in economic theory. Journal of Economic Surveys 25(3), 401–430 (2011)

    Article  Google Scholar 

  2. Kirman, A.P.: Whom or What Does The Representative Individual Represent. Journal of Economic Perspective 6, 117–136 (1992)

    Article  Google Scholar 

  3. Akerlof, G.A.: Behavioral macroeconomics and macroeconomic behavior. American Economic Review 92(3), 411–433 (2002)

    Article  Google Scholar 

  4. Kahneman, D.: Maps of bounded rationality: Psychology for behavioral economics. American Economic Review 93(5), 1449–1475 (2003)

    Article  Google Scholar 

  5. Kao, S.Y.F., Velupillai, K.V.: Behavioural economics: Classical and modern. ASSRU Discussion Papers 1126, ASSRU - Algorithmic Social Science Research Unit (2011)

    Google Scholar 

  6. Tesfatsion, L.: Agent-based computational economics: Modeling economies as complex adaptive systems. Information Sciences 149, 263–269 (2003)

    Article  Google Scholar 

  7. Epstein, J.M.: Agent-based computational models and generative social science. In: Generative Social Science Studies in Agent-Based Computational Modeling. Introductory Chapters. Princeton University Press (2007)

    Google Scholar 

  8. Delli Gatti, D., Gaffeo, E., Gallegati, M., Giulioni, G., Palestrini, A.: Emergent Macroeconomics. Springer, Berlin (2008)

    Google Scholar 

  9. LeBaron, B., Tesfatsion, L.: Modeling macroeconomies as open-ended dynamic systems of interacting agents. American Economic Review 98(2), 246–250 (2008)

    Article  Google Scholar 

  10. Fagiolo, G., Birchenhall, C., Windrum, P.: Empirical Validation in Agent-based Models: Introduction to the Special Issue. Computational Economics 30(3), 189–194 (2007)

    Article  Google Scholar 

  11. Fagiolo, G., Moneta, A., Windrum, P.: A critical guide to empirical validation of agent-based models in economics: Methodologies, procedures, and open problems. Computational Economics 30(3), 195–226 (2007)

    Article  Google Scholar 

  12. Kinsella, S., Greiff, M., Nell, E.J.: Income distribution in a stock-flow consistent model with education and technological change. Eastern Economic Journal 37(1), 134–149 (2011)

    Article  Google Scholar 

  13. Seppecher, P.: Flexibility of wages and macroeconomic instability in an agent-based computational model with endogenous money. Macroeconomic Dynamics 16(S2), 284–297 (2012)

    Article  Google Scholar 

  14. Raberto, M., Teglio, A., Cincotti, S.: Debt, deleveraging and business cycles: An agent-based perspective. Economics - The Open-Access, Open-Assessment E-Journal 6(27), 1–49 (2012)

    Google Scholar 

  15. Arthur, W.B.: On designing economic agents that behave like human agents. Journal of Ecolutionary Economics 3, 1–22 (1993)

    Google Scholar 

  16. Arthur, W.B.: Designing Economic Agents that Act like Human Agents: A Behavioral Approach to Bounded Rationality. American Economic Review Papers and Proceedings 81(2), 353–359 (1991)

    Google Scholar 

  17. Duffy, J.: Agent-based models and human subject experiments. In: Tesfatsion, L., Judd, K.L. (eds.) Handbook of Computational Economics. Handbook of Computational Economics, vol. 2, pp. 949–1011. Elsevier (2006)

    Google Scholar 

  18. Gode, D., Sunder, S.: Allocative efficiency of markets with zero intelligence (z1) traders: Market as a partial substitute for individual rationality. Gsia Working Papers, Carnegie Mellon University, Tepper School of Business (1991)

    Google Scholar 

  19. Arifovic, J.: The behavior of the exchange rate in the genetic algorithm and experimental economies. Journal of Political Economy 104(3), 510–541 (1996)

    Article  Google Scholar 

  20. Arifovic, J.: Evolutionary algorithms in macroeconomic models. Macroeconomic Dynamics 4, 373–414 (2000)

    Article  MATH  Google Scholar 

  21. Dawid, H.: Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models. Springer Verlag New York, Inc. (1996)

    Google Scholar 

  22. Chen, S.H., Yu, T.: Agents learned, but do we? knowledge discovery using the agent-based double auction markets. Frontiers of Electrical and Electronic Engineering in China, 159–170 (2011)

    Google Scholar 

  23. Dosi, G., Fagiolo, G., Roventini, A.: Schumpeter meeting keynes: A policy-friendly model of endogenous growth and business cycles. Journal of Economic Dynamics and Control 34(9), 1748–1767 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  24. Chen, S.H.: Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective. Journal of Economic Dynamics and Control 36(1), 1–25 (2012)

    Article  MathSciNet  Google Scholar 

  25. Hansen, L.P., Heckman, J.J.: The empirical foundations of calibration. Journal of Economic Perspectives 10(1), 87–104 (1996)

    Article  Google Scholar 

  26. Booker, L.B., Goldberg, D.E., Holland, J.H.: Classifier systems and genetic algorithms. Artificial Intelligence 40(1-3), 235–282 (1989)

    Article  Google Scholar 

  27. Birchenhall, C.R.: Genetic algorithms, classifier systems and genetic programming and their use in the models of adaptive behaviour and learning. The Economic Journal 105(430), 788–795 (1995)

    Article  Google Scholar 

  28. Waltman, L., Eck, N., Dekker, R., Kaymak, U.: Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies. Journal of Evolutionary Economics 21(5), 737–756 (2011)

    Article  Google Scholar 

  29. Dawid, H., Dermietzel, J.: How robust is the equal split norm? responsive strategies, selection mechanisms and the need for economic interpretation of simulation parameters. Computational Economics 28(4) (2006)

    Google Scholar 

  30. Giulioni, G., Bucciarelli, E., Silvestri, M., D’Orazio, P.: Agent-based computational economics and experimental economics: a bridge to progress macroeconomics (2013) (manuscript submitted)

    Google Scholar 

  31. Smith, V.L.: Microeconomic Systems as an Experimental Science. American Economic Review 72(5), 923–955 (1982)

    Google Scholar 

  32. Smith, V.L.: Method in Experiment: Rhetoric and Reality. Experimental Economics 5, 91–110 (2002)

    Article  MATH  Google Scholar 

  33. Carroll, C.D.: Representing consumption and saving without a representative consumer. In: Measuring Economic Sustainability and Progress. NBER Chapters, National Bureau of Economic Research, Inc. (July 2012)

    Google Scholar 

  34. Hey, J.D., Dardanoni, V.: Optimal consumption under uncertainty: An experimental investigation. Economic Journal 98(390), 105–116 (1987)

    Article  Google Scholar 

  35. Carroll, C.: Lecture notes on solution methods for microeconomicdynamic stochastic optimization problems. Lecture Notes - John Hopkins University (2012)

    Google Scholar 

  36. Stachurski, J.: Economic Dynamics. Theory and Computation. MIT Press (2009)

    Google Scholar 

  37. Cubitt, R., Read, D.: Can intertemporal choice experiments elicit time preferences for consumption? Experimental Economics 10, 369–389 (2007)

    Article  MATH  Google Scholar 

  38. Coller, M., Williams, M.: Eliciting individual discount rates. Experimental Economics 2(2), 107–127 (1999)

    MATH  Google Scholar 

  39. Harrison, G.W., Harstad, R.M., Rutstrm, E.E.: Experimental methods and elicitation of values. Discussion paper serie b. University of Bonn, Germany (1995)

    Google Scholar 

  40. Greiner, B.: An online recruitment system for economic experiments. Forschung und Wissenschaftliches Rechnen 2003. GWDG Bericht (2004)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Philosophical, Pedagogical and Economic-Quantitative Sciences, “G. D’Annunzio” University, Viale Pindaro 42, 65127, Pescara, Italy

    Paola D’Orazio

  2. Research Group for Experimental Microfoundations of Macroeconomics (GEMM), Pescara, Italy

    Paola D’Orazio

Authors
  1. Paola D’Orazio
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Corresponding author

Correspondence to Paola D’Orazio .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, Osaka Institute of Technology, Osaka, Osaka, Japan

    Sigeru Omatu

  2. Université Libre de Bruxelles, Bruxelles, Belgium

    Hugues Bersini

  3. Dept. of Computing Science and Control Faculty of Science, University of Salamanca, Salamanca, Spain

    Juan M. Corchado

  4. Department of Computing Science and Control, Faculty of Science, University of Salamanca, Salamanca, Spain

    Sara Rodríguez

  5. Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland

    Paweł Pawlewski

  6. Dep. PPEQS, Section of Economics and Quantitative Methods, University of Chieti-Pescara, Pescara, Italy

    Edgardo Bucciarelli

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D’Orazio, P. (2014). Households Debt Behavior and Financial Instability: Towards an Agent-Based Model with Experimentally Estimated Behavioral Rules. In: Omatu, S., Bersini, H., Corchado, J., Rodríguez, S., Pawlewski, P., Bucciarelli, E. (eds) Distributed Computing and Artificial Intelligence, 11th International Conference. Advances in Intelligent Systems and Computing, vol 290. Springer, Cham. https://doi.org/10.1007/978-3-319-07593-8_12

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  • DOI: https://doi.org/10.1007/978-3-319-07593-8_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07592-1

  • Online ISBN: 978-3-319-07593-8

  • eBook Packages: EngineeringEngineering (R0)

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