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Individual Expectations And Aggregate Behavior In Learning-To-Forecast Experiments

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  • Hommes, Cars
  • Lux, Thomas

Abstract

Models with heterogeneous interacting agents explain macro phenomena through interactions at the micro level. We propose genetic algorithms as a model for individual expectations to explain aggregate market phenomena. The model explains all stylized facts observed in aggregate price fluctuations and individual forecasting behaviour in recent learning-to-forecast laboratory experiments with human subjects (Hommes et al. 2007), simultaneously and across different treatments.

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  • Hommes, Cars & Lux, Thomas, 2013. "Individual Expectations And Aggregate Behavior In Learning-To-Forecast Experiments," Macroeconomic Dynamics, Cambridge University Press, vol. 17(2), pages 373-401, March.
    • Handle: RePEc:cup:macdyn:v:17:y:2013:i:02:p:373-401_00
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    Cited by:

    1. Selten, Reinhard & Neugebauer, Tibor, 2019. "Experimental stock market dynamics: Excess bids, directional learning, and adaptive style-investing in a call-auction with multiple multi-period lived assets," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 209-224.
    2. Bao, Te & Hommes, Cars & Pei, Jiaoying, 2021. "Expectation formation in finance and macroeconomics: A review of new experimental evidence," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    3. Iori, G. & Porter, J., 2012. "Agent-Based Modelling for Financial Markets," Working Papers 12/08, Department of Economics, City University London.
    4. Makarewicz, Tomasz, 2021. "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," Journal of Economic Behavior & Organization, Elsevier, vol. 190(C), pages 626-673.
    5. Arifovic, Jasmina & Karaivanov, Alexander, 2010. "Learning by doing vs. learning from others in a principal-agent model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(10), pages 1967-1992, October.
    6. Mitja Steinbacher & Matthias Raddant & Fariba Karimi & Eva Camacho Cuena & Simone Alfarano & Giulia Iori & Thomas Lux, 2021. "Advances in the agent-based modeling of economic and social behavior," SN Business & Economics, Springer, vol. 1(7), pages 1-24, July.
    7. Annarita Colasante & Simone Alfarano & Eva Camacho-Cuena, 2020. "Heuristic Switching Model and Exploration-Exploitation Algorithm to Describe Long-Run Expectations in LtFEs: a Comparison," Computational Economics, Springer;Society for Computational Economics, vol. 56(3), pages 623-658, October.
    8. Troy Tassier, 2013. "Handbook of Research on Complexity, by J. Barkley Rosser, Jr. and Edward Elgar," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 39(1), pages 132-133.
    9. Biondo, Alessio Emanuele, 2017. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics Discussion Papers 2017-104, Kiel Institute for the World Economy (IfW Kiel).
    10. Biondo, Alessio Emanuele, 2018. "Learning to forecast, risk aversion, and microstructural aspects of financial stability," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 12, pages 1-21.
    11. Cars Hommes & Tomasz Makarewicz & Domenico Massaro & Tom Smits, 2017. "Genetic algorithm learning in a New Keynesian macroeconomic setup," Journal of Evolutionary Economics, Springer, vol. 27(5), pages 1133-1155, November.
    12. Tae-Seok Jang & Stephen Sacht, 2016. "Animal Spirits and the Business Cycle: Empirical Evidence from Moment Matching," Metroeconomica, Wiley Blackwell, vol. 67(1), pages 76-113, February.
    13. Hommes, Cars, 2011. "The heterogeneous expectations hypothesis: Some evidence from the lab," Journal of Economic Dynamics and Control, Elsevier, vol. 35(1), pages 1-24, January.
    14. Annarita Colasante & Simone Alfarano & Eva Camacho-Cuena & Mauro Gallegati, 2020. "Long-run expectations in a learning-to-forecast experiment: a simulation approach," Journal of Evolutionary Economics, Springer, vol. 30(1), pages 75-116, January.
    15. Makarewicz, Tomasz, 2019. "Traders, forecasters and financial instability: A model of individual learning of anchor-and-adjustment heuristics," BERG Working Paper Series 141, Bamberg University, Bamberg Economic Research Group.
    16. Cars Hommes, 2010. "The heterogeneous expectations hypothesis: some evidence from the lab," Post-Print hal-00753041, HAL.
    17. Mikhail Anufriev & Cars Hommes & Tomasz Makarewicz, 2019. "Simple Forecasting Heuristics that Make us Smart: Evidence from Different Market Experiments," Journal of the European Economic Association, European Economic Association, vol. 17(5), pages 1538-1584.
    18. Leonidas Sandoval Junior & Italo De Paula Franca, 2011. "Shocks in financial markets, price expectation, and damped harmonic oscillators," Papers 1103.1992, arXiv.org, revised Sep 2011.
    19. Tai, Chung-Ching & Chen, Shu-Heng & Yang, Lee-Xieng, 2018. "Cognitive ability and earnings performance: Evidence from double auction market experiments," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 409-440.
    20. Mikhail Anufriev & Cars Hommes & Raoul Philipse, 2013. "Evolutionary selection of expectations in positive and negative feedback markets," Journal of Evolutionary Economics, Springer, vol. 23(3), pages 663-688, July.
    21. Antonio Doria, Francisco, 2011. "J.B. Rosser Jr. , Handbook of Research on Complexity, Edward Elgar, Cheltenham, UK--Northampton, MA, USA (2009) 436 + viii pp., index, ISBN 978 1 84542 089 5 (cased)," Journal of Economic Behavior & Organization, Elsevier, vol. 78(1-2), pages 196-204, April.
    22. Mario Gutiérrez-Roig & Carlota Segura & Jordi Duch & Josep Perelló, 2016. "Market Imitation and Win-Stay Lose-Shift Strategies Emerge as Unintended Patterns in Market Direction Guesses," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-19, August.
    23. Guerci, E. & Kirman, A. & Moulet, S., 2014. "Learning to bid in sequential Dutch auctions," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 374-393.
    24. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046.
    25. Hommes, Cars, 2018. "Carl’s nonlinear cobweb," Journal of Economic Dynamics and Control, Elsevier, vol. 91(C), pages 7-20.

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    More about this item

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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