Skip to main content


  • Murat Yıldızoğlu (a1), Marc-Alexandre Sénégas (a1), Isabelle Salle (a1) and Martin Zumpe (a1)

This article questions the rather pessimistic conclusions of Allen and Carroll [Macroeconomic Dynamics 5 (2001), 255–271] about the ability of consumers to learn the optimal buffer-stock-based consumption rule. To this end, we develop an agent-based model in which alternative learning schemes can be compared in terms of the consumption behavior that they yield. We show that neither purely adaptive learning nor social learning based on imitation can ensure satisfactory consumption behavior. In contrast, if the agents can form adaptive expectations, based on an evolving individual mental model, their behavior becomes much more interesting in terms of its regularity and its ability to improve performance (which is a clear manifestation of learning). Our results indicate that assumptions on bounded rationality and on adaptive expectations are perfectly compatible with sound and realistic economic behavior, which, in some cases, can even converge to the optimal solution. This framework may therefore be used to develop macroeconomic models with adaptive dynamics.

Corresponding author
Address correspondence to: Murat Yıldızoğlu, GREThA (UMR CNRS 5113), Bordeaux University, Avenue Léon Duguit, F-33608 PESSAC Cedex, France; e-mail:
Hide All
Allen, Todd W. and Carroll, Christopher D. (2001) Individual learning about consumption. Macroeconomic Dynamics 5 (2), 255271.
Arifovic, J. (1994) Genetic algorithm learning and the cobweb model. Journal of Economic Dynamics and Control 18, 328.
Butler, N.A. (2001) Optimal and orthogonal latin hypercube designs for computer experiments. Biometrika 88 (3), 847857.
Carroll, Christopher D. (1992) The buffer-stock theory of saving: Some macroeconomic evidence. Brookings Papers on Economic Activity (2), 61156.
Carroll, Christopher D. (1997) Buffer stock saving and the life cycle/permanent income hypothesis. Quarterly Journal of Economics 112 (1), 156.
Carroll, Christopher D. (2001) A theory of the consumption function, with and without liquidity constraints. Journal of Economic Perspectives 15 (3), 2346.
Carroll, Christopher D. (2004) Theoretical Foundations of Buffer Stock Saving. Working paper 10867 NBER.
Cioppa, T.M. (2002) Efficient Nearly Orthogonal And Space-Filling Experimental Designs For High-Dimensional Complex Models. Ph.D. dissertation, in philosophy in operations research, Naval Postgraduate School.
Cioppa, Thomas M. and Lucas, Thomas W. (2007) Efficient nearly orthogonal and space-filling latin hypercubes. Technometrics 49 (1), 4555.
Deaton, Angus (1991) Saving and liquidity constraints. Econometrica 59 (5), 12211248.
Deaton, Angus (1992) Understanding Consumption. New York: Oxford University Press.
Evans, G.W. and Honkapohja, S. (2001) Learning and Expectations in Macroeconomics. Princeton, NJ: Princeton University Press.
Fang, K.T., Lin, D.K.J., Winker, P., and Zhang, Y. (2000) Uniform design: Theory and application. Technometrics 42 (3), 237248.
Goupy, J. and Creighton, L. (2007) Introduction to Design of Experiments with JMP Examples, 3rd ed.Cary, NC: SAS Institute.
Happe, K. (2005) Agent-Based Modelling and Sensitivity Analysis by Experimental Design and Metamodelling: An Application to Modelling Regional Structural Change. Paper prepared for the XIth International Congress of the European Association of Agricultural Economists.
Holland, John H. (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge, MA: MIT Press.
Holland, J. and Miller, J. H. (1991) Artificial adaptive agents in economic theory. American Economic Review Papers and Procedings 81 (2), 363370.
Holland, J.H., Holyoak, K.J., Nisbett, R.E., and Thagard, P.R. (1989) Induction. Processes of Inference, Learning and Discoverey. Cambridge, MA: MIT Press.
Howitt, Peter and Özak, Ömer (2009) Adaptive Consumption Behavior. Working paper 15427, NBER.
Kleijnen, Jack P.C., Sanchez, Susan M., Lucas, Thomas W., and Cioppa, Thomas M. (2005) A user's guide to the brave new world of designing simulation experiments. INFORMS Journal on Computing 17 (3), 263289.
Lettau, Martin and Harald, Uhlig (1999) Rules of thumb versus dynamic programming. American Economic Review 89 (1), 148174.
Masters, Timothy (1993) Practical Neural Network Recipes in C++. New York: Academic Press.
Oeffner, M. (2008) Agent-Based Keynesian Macroeconomics—An Evolutionary Model Embedded in an Agent-Based Computer Simulation. Doctoral dissertation, Bayerische Julius—Maximilians Universitat.
R Development Core Team (2003) R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Available at
Salmon, Mark (1995) Bounded rationality and learning: Procedural learning. In Kirman, Alan and Salmon, Mark (eds.), Learning and Rationality in Economics, pp. 236275. Oxford, UK: Blackwell.
Sanchez, S.M. (2005) Nolhdesigns Spreadsheet. Available at
Simon, Herbert A. (1976) From substantial to procedural rationality. In Latsis, S.J. (ed.), Method and Appraisal in Economics, pp. 129148. Cambridge, UK: Cambridge University Press.
Sutton, Richard S. and Barto, Andrew G. (1998) Reinforcement Learning: an Introduction. Cambridge, MA: MIT Press.
Vallée, Thomas and Yıldızoğlu, Murat (2009) Convergence in the finite cournot oligopoly with social and individual learning. Journal of Economic Behaviour and Organization (72), 670690.
Vriend, Nicolaas (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), 119.
Wickham, Hardley (2009) ggplot2 Elegant Graphics for Data Analysis. Dordrecht: Springer.
Wilson, Stewart W. (1995) Classifier fitness based on accuracy. Evolutionary Computation 3 (2), 149175.
Ye, K.Q. (1998) Orthogonal column latin hypercubes and their application in computer experiments. Journal of the American Statistical Association 93 (444), 14301439.
Yildizoglu, Murat (2001) Connecting adaptive behaviour and expectations in models of innovation: The potential role of artificial neural networks. European Journal of Economics and Social Systems 15 (3), 203220.
Yildizoglu, Murat (2002) Competing R&D strategies in an evolutionary industry model. Computational Economics 19 (1), 5265.
Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Macroeconomic Dynamics
  • ISSN: 1365-1005
  • EISSN: 1469-8056
  • URL: /core/journals/macroeconomic-dynamics
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Full text views

Total number of HTML views: 0
Total number of PDF views: 26 *
Loading metrics...

Abstract views

Total abstract views: 270 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 19th June 2018. This data will be updated every 24 hours.