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INDIVIDUAL EXPECTATIONS AND AGGREGATE BEHAVIOR IN LEARNING-TO-FORECAST EXPERIMENTS

Published online by Cambridge University Press:  09 December 2011

Cars Hommes  and
Thomas Lux
Cars Hommes*
Affiliation:
University of Amsterdam
Thomas Lux
Affiliation:
University of Kiel, Kiel Institute for the World Economy and Bank of Spain Chair in Computational Economics, University of Castellón
*
Address correspondence to: Cars Hommes CeNDEF, School of Economics, University of Amsterdam, Valckenierstraat 65-67, 1018 XE Amsterdam, the Netherlands; e-mail: C.H.Hommes@uva.nl.
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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.

Keywords

LearningHeterogeneous ExpectationsGenetic AlgorithmsExperimental Economics
Type
Articles
Information
Macroeconomic Dynamics , Volume 17 , Issue 2 , March 2013 , pp. 373 - 401
Copyright
Copyright © Cambridge University Press 2011

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References

REFERENCES

Adam, K. (2007) Experimental evidence on the persistence of output and inflation. Economic Journal 117, 603–636.CrossRefGoogle Scholar
Anufriev, M. and Hommes, C.H. (2009) Evolutionary Selection of Individual Expectations and Aggregate Outcomes. Cendef working paper 09-09, University of Amsterdam.Google Scholar
Anufriev, M. and Hommes, C.H. (in press) Evolution of market heuristics. Knowledge Engineering Review.Google Scholar
Arifovic, J. (1994) Genetic algorithm learning and the cobweb model. Journal of Economic Dynamics and Control 18, 3–28.CrossRefGoogle Scholar
Arifovic, J. (1996) The behavior of the exchange rate in the genetic algorithm and experimental economies. Journal of Political Economy 104, 510–541.CrossRefGoogle Scholar
Arifovic, J. and Gencay, R. (2000) Statistical properties of genetic learning in a model of exchange rate. Journal of Economic Dynamics and Control 24, 981–1005.CrossRefGoogle Scholar
Arthur, W.B., Holland, J.H., LeBaron, B., Palmer, R., and Tayler, P. (1997) Asset pricing under endogenous expectations in an artificial stock market. In Arthur, W., Lane, D., and Durlauf, S. (eds.), The Economy as an Evolving Complex System II, pp. 15–44. Reading, MA: Addison–Wesley.Google Scholar
Branch, W.A. (2004) The theory of rationally heterogeneous expectations: Evidence from survey data on inflation expectations. Economic Journal 114, 592–621.CrossRefGoogle Scholar
Brock, W.A. and Hommes, C.H. (1997) A rational route to randomness. Econometrica 65, 1059–1095.CrossRefGoogle Scholar
Casari, M. (2004) Can genetic algorithms explain experimental anomalies? Computational Economics 24, 257–275.CrossRefGoogle Scholar
Chavas, J.-P. (2000) On information and market dynamics: The case of the U.S. beef market. Journal of Economic Dynamics and Control 24, 833–853.CrossRefGoogle Scholar
Chen, S.-H. and Wang, P. (2002) Computational Intelligence in Economics and Finance. Berlin: Springer-Verlag.Google Scholar
Dawid, H. (1999) Adaptive Learning by Genetic Algorithms: Analytical Results and Applications to Economic Models, 2nd ed.Berlin: Springer-Verlag.CrossRefGoogle Scholar
Duffy, J. (2006) Agent-based models and human-subject experiments. In Tesfatsion, Leigh and Judd, Kenneth L. (eds.), Handbook of Computational Economics, 1st ed., vol. 2, chap. 19, pp. 949–1011. Amsterdam: Elsevier.Google Scholar
Duffy, J. (2008) Experimental macroeconomics. In Durlauf, S. and Blume, L. (eds.), The New Palgrave Dictionary of Economics, 2nd Ed.New York: Palgrave Macmillan.Google Scholar
Erev, I. and Roth, A.E. (1999) Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria. American Economic Review 88, 848–881.Google Scholar
Evans, G.W. and Honkapohja, S. (2001) Learning and Expectations in Macroeconomics. Princeton, NJ: Princeton University Press.CrossRefGoogle Scholar
Evans, G.W. and Ramey, G. (1992) Expectation, calculation and macroeconomics dynamics. American Economic Review 82, 207–224.Google Scholar
Ezekiel, M. (1938) The cobweb theorem. Quarterly Journal of Economics 52, 255–280.CrossRefGoogle Scholar
Freeman, R.B. (1975) Legal “cobwebs:” A recursive model of the market for new lawyers. Review of Economics and Statistics 57, 171–179.CrossRefGoogle Scholar
Freeman, R.B. (1976) A cobweb model of the supply and starting salary of new engineers. Industrial and Labor Relations Review 29, 236–248.CrossRefGoogle Scholar
Heemeijer, P., Hommes, C.H., Sonnemans, J., and Tuinstra, J. (2009) Price stability and volatility in markets with positive and negative expectations feedback: An experimental investigation. Journal of Economic Dynamics and Control 33, 1052–1072.CrossRefGoogle Scholar
Herrera, F., Lozano, M., and Verdegay, J. (1998) Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis. Artificial Intelligence Review 12, 265–319.CrossRefGoogle Scholar
Holland, J. (1975) Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.Google Scholar
Hommes, C.H. (2002) Modeling the stylized facts in finance through simple nonlinear adaptive systems. Proceedings of the National Academy of Sciences 99, 7221–7228.CrossRefGoogle ScholarPubMed
Hommes, C.H. (2006) Heterogeneous agent models in economics and finance. In Tesfatsion, L. and Judd, K.J. (eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics, pp. 1109–1186. Amsterdam: Elsevier.CrossRefGoogle Scholar
Hommes, C. (2009) Bounded rationality and learning in complex markets. In Rosser, J. Barkley Jr., (eds.), Handbook of Research on Complexity, pp. 87–123. Cheltenham, UK: Edward Elgar.Google Scholar
Hommes, C. (2011) The heterogeneous expectations hypothesis: Some evidence from the lab. Journal of Economic Dynamics and Control 35, 1–24.CrossRefGoogle Scholar
Hommes, C.H., Sonnemans, J., Tuinstra, J., and van de Velden, H. (2005) Coordination of expectations in asset pricing experiments. Review of Financial Studies 18, 955–980.CrossRefGoogle Scholar
Hommes, C., Sonnemans, J., Tuinstra, J., and van de Velden, H. (2007) Learning in cobweb experiments. Macroeconomic Dynamics 11 (S1), 8–33.CrossRefGoogle Scholar
Kahneman, D. (2003) Maps of bounded rationality: Psychology for behavioral economics. American Economic Review 93, 1449–1475.CrossRefGoogle Scholar
Kirman, A. (1993) Ants, rationality and recruitment. Quarterly Journal of Economics 108, 137–156.CrossRefGoogle Scholar
Kirman, A. (2006) Heterogeneity in economics. Journal of Economic Interaction and Coordination 1, 89–117.CrossRefGoogle Scholar
Krugman, P.R. (2001) The oil-hog cycle. The New York Times (Nov. 18), p. 14.Google Scholar
LeBaron, B., Arthur, B., and Palmer, R. (1999) The time series properties of an artifical stock market. Journal of Economic Dynamics and Control 23, 1487–1516.CrossRefGoogle Scholar
Lux, T. (1995) Herd behavior, bubbles and crashes. Economic Journal 105, 881–896.CrossRefGoogle Scholar
Lux, T. and Marchesi, M. (1999) Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397 (Feb.), 498–500.CrossRefGoogle Scholar
Lux, T. and Marchesi, M. (2000) Volatility clustering in financial markets: A micro-simulation of interacting agents. International Journal of Theoretical and Applied Finance 3, 675–702.CrossRefGoogle Scholar
Lux, T. and Schornstein, S. (2005) Genetic learning as an explanation of stylized facts of foreign exchange markets. Journal of Mathematical Economics 41, 169–196.CrossRefGoogle Scholar
Mankiw, N.G., Reis, R.A.M.R., and Wolfers, J. (2003) Disagreement about Inflation Expectations. NBER working paper W9796.CrossRefGoogle Scholar
Marimon, R. and Sunder, S. (1994) Expectations and learning under alternative monetary regimes: An experimental approach. Economic Theory 4, 131–162.CrossRefGoogle Scholar
Muth, J.F. (1961) Rational expectations and the theory of price movements. Econometrica 29, 315–335.CrossRefGoogle Scholar
Nerlove, M. (1958) Adaptive expectations and cobweb phenomena. Quarterly Journal of Economics 72, 227–240.CrossRefGoogle Scholar
Reis, R. (2006) Inattentive producers. Review of Economic Studies 73, 793–821.CrossRefGoogle Scholar
Rosen, S., Murphy, K., and Scheinkman, J. (1994) Cattle cycles. Journal of Political Economy 102, 468–492.CrossRefGoogle Scholar
Sargent, T.J. (1993) Bounded Rationality in Macroeconomics. Oxford: Clarendon Press.CrossRefGoogle Scholar
Shiller, R.J. (2000) Measuring bubble expectations and investor confidence. Journal of Psychology and Financial Markets 1, 49–60.CrossRefGoogle Scholar
Sutan, A. and Willinger, M. (2005) Why Do We Guess Better in Negative Feedback Situations? An Experiment of Beauty Contest Games with Negative Feedback and Interior Equilibria. Working paper, University of Montpellier.CrossRefGoogle Scholar
Tversky, A. and Kahneman, D. (1974) Judgment under uncertainty: Heuristics and biases. Science 185, 1124–1131.CrossRefGoogle ScholarPubMed
van de Velden, H. (2001) An Experimental Approach to Expectation Formation in Dynamic Economic Systems. Ph.D. dissertation, Tinbergen Institute Research Series 268.Google Scholar
Vissing-Jorgensen, A. (2003) Perspective on behavioral finance: Does “irrationality” disappear with wealth? Evidence from expectations and actions. In Gertler, M. and Rogoff, K. (eds.), NBER Macroeconomics Annual. Cambridge, MA: MIT Press.Google Scholar
Vriend, N. (2000) An illustration of the essential difference between individual and social learning and its consequences for computational analyses. Journal of Economic Dynamics and Control 24, 1–14.CrossRefGoogle Scholar
Wellford, C.P. (1989) A Laboratory Analysis of Price Dynamics and Expectations in the Cobweb model. Discussion paper 89-15, Department of Economics, University of Arizona.Google Scholar
Zarkin, G.A. (1985) Occupational choice: An application to the market for public school teachers. Quarterly Journal of Economics 100, 409–446.CrossRefGoogle Scholar