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SOCIAL LEARNING ABOUT CONSUMPTION

Published online by Cambridge University Press:  28 September 2015

Isabelle Salle  and
Pascal Seppecher
Isabelle Salle*
Affiliation:
University of Amsterdam and Tinbergen Institute
Pascal Seppecher
Affiliation:
University of Paris 13 and University of Nice Sophia Antipolis
*
Address correspondence to: Isabelle Salle, CeNDEF, Amsterdam School of Economics, University of Amsterdam, Roetersstraat 11, NL-1018 WB, Amsterdam, the Netherlands; e-mail: I.L.Salle@uva.nl.
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Abstract

This paper applies a social learning model to the optimal consumption rule of Allen and Carroll [Macroeconomic Dynamics 5(2001), 255–271] and delivers convincing convergence dynamics toward the optimal rule. These findings constitute a significant improvement over previous results in the literature, in terms of both speed of convergence and parsimony of the learning model. The learning model exhibits several appealing features: it is frugal, is easy to apply to a various range of learning objectives, and requires few procedures and little information. Particular care is given to behavioral interpretation of the modeling assumptions in light of evidence from the fields of psychology and social science. Our results highlight the need to depart from the genetic metaphor, and account for intentional decision-making, based on agents' relative performances. By contrast, we show that convergence is strongly hindered by exact imitation processes, or random exploration mechanisms, which are usually assumed when modeling social learning behavior. Our results suggest a method for modeling bounded rationality, which could be interestingly tested in a wide range of economic models with adaptive dynamics.

Keywords

LearningBounded RationalityEvolutionary AlgorithmsConsumption Rule
Type
Articles
Information
Macroeconomic Dynamics , Volume 20 , Issue 7 , October 2016 , pp. 1795 - 1825
Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

Allen, T.W. and Carroll, C. (2001) Individual learning about consumption. Macroeconomic Dynamics 5, 255–271.Google Scholar
Arifovic, J. (2000) Evolutionary algorithms in macroeconomic models. Macroeconomic Dynamics 4, 373–414.Google Scholar
Arifovic, Y. (1995) Genetic algorithms and inflationary economies. Journal of Monetary Economics 36 (1), 219–243.Google Scholar
Arifovic, Y., Bullard, J., and Kostyshyna, O. (2013) Social learning and monetary policy rules. Economic Journal 123 (567), 38–76.Google Scholar
Arthur, W.B. (1991) Designing economic agents that act like human agents: a behavioral approach to bounded rationality. American Economic Review 81 (2), 353–359.Google Scholar
Bikhchandani, S., Hirshleifer, D., and Welch, I. (1998) Learning from the behavior of others: Conformity, fads, and informational cascades. Journal of Economic Perspectives 12 (3), 151–170.Google Scholar
Binswanger, J. (2011) Dynamic decision making with feasibility goals: A procedural-rationality approach. Journal of Economic Behavior and Organization 78 (3), 219–228.Google Scholar
Brown, A.L., Chua, Z.E., and Camerer, C.F. (2009) Learning and visceral temptation in dynamic saving experiments. Quarterly Journal of Economics 124 (1), 197–223.Google Scholar
Bullard, J. and Duffy, J. (1998) A model of learning and emulation with artificial adaptive agents. Journal of Economic Dynamics and Control 22 (2), 179–207.Google Scholar
Carroll, C. (1997) Buffer stock saving and the life cycle permanent income hypothesis. Quarterly Journal of Economics 112 (1), 1–56.Google Scholar
Carroll, C. (2001) A theory of the consumption function, with and without liquidity constraints. Journal of Economic Perspectives 15 (3), 23–46.CrossRefGoogle Scholar
Dawid, H. (1997) Learning of equilibria by a population with minimal information. Journal of Economic Behavior and Organization 32, 1–18.Google Scholar
Dawid, H. and Hornik, K. (1996) The dynamics of genetic algorithms in interactive environments. Journal of Network and Computer Applications 19, 5–19.Google Scholar
Deaton, A. (1991) Saving and liquidity constraints. Econometrica 59 (5), 1221–1248.Google Scholar
Ellison, G. and Fudenberg, D. (1993) Rules of thumb for social learning. Journal of Political Economy 101 (4), 612–643.Google Scholar
Ellison, G. and Fudenberg, D. (1995) Word-of-mouth communication and social learning. Quarterly Journal of Economics 110 (1), 93–125.Google Scholar
Eshelman, L. and Schaffer, J. (1993) Real-coded genetic algorithms and interval-schemata. In Foundations of Genetic Algorithms 2. San Mateo, CA: Morgan Kaufmann.Google Scholar
Franke, J. (1999) Memory enhanced evolutionary algorithms for changing optimization problems. In Proceedings of the 1999 Congress on Evolutionary Computation, vol. 3.Google Scholar
Friedman, M. (1953) Essays in Positive Economics. Chicago: University of Chicago Press.Google Scholar
Fudenberg, D. and Levine, D. (1998) Theory of Learning in Games. Cambridge, MA: MIT Press.Google Scholar
Gigerenzer, G. and Selten, R. (2001) Bounded Rationality: The Adaptive Toolbox. Cambridge, MA: MIT Press.Google Scholar
Goldberg, D. E. (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, MA: Addison–Wesley.Google 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.Google Scholar
Hoffrage, U. and Reimer, T. (2004) Models of bounded rationality: The approach of fast and frugal heuristics. International Review of Management Studies 15 (4), 437–459.Google Scholar
Holland, J. (1975) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Oxford, UK: University of Michigan Press.Google Scholar
Holland, J., Goldberg, D., and Booker, L. (1989) Classifier systems and genetic algorithms. Artificial Intelligence 40, 235–289.Google Scholar
Holland, J. and Miller, J. (1991) Artificial adaptive agents in economic theory. AER Papers and Proceedings 91 (2), 365–370.Google Scholar
Howitt, P. and Özak, O. (2013) Adaptive consumption behavior. Journal of Economic Dynamics and Control 39 (C), 37–61.Google Scholar
Huguet, P., Dumas, F., Monteil, J.M., and Genestoux, N. (2001) Social comparison choices in the classroom: Further evidence for students' upward comparison tendency and its beneficial impact on performance. European Journal of Social Psychology 31, 557–578.Google Scholar
Hutchinson, J.M. and Gigerenzer, G. (2005) Simple heuristics and rules of thumb: Where psychologists and behavioural biologists might meet. Behavioural Processes 69, 97–124.Google Scholar
Janetos, A.C. (1980) Strategies of female mate choice: A theoretical analysis. Behavioral Ecological Sociobiology 7, 107–112.Google Scholar
Judd, K. (2006) Computationally intensive analyses in economics. In Tesfatsion, L. and Judd, K. (eds.), Handbook of Computational Economics, vol. 2, Chap. 17, pp. 881–884. Amsterdam: North-Holland.Google Scholar
Kahneman, D. and Tversky, A. (1996) On the reality of cognitive illusions. Psychological Review 103, 582–591.Google Scholar
Lettau, M. and Uhlig, H. (1999) Rules of thumb versus dynamic programming. American Economic Review 89 (1), 148–174.Google Scholar
Lux, T. and Schornstein, S. (2005) Genetic learning as an explanation of stylized facts of foreign exchange markets. Journal of Mathematical Economics 41 (1–2), 169–196.Google Scholar
Palmer, N. (2012) Learning to Consume: Individual versus Social Learning. Mimeo, George Mason University.Google Scholar
Penrose, E.T. (1952) Biological analogies in the theory of the firm. American Economic Review 42 (5), 804–809.Google Scholar
Rubinstein, A. (1998) Modeling Bounded Rationality. Cambridge, MA: MIT Press.Google Scholar
Salle, I., Zumpe, M., Yıldızoğlu, M., and Sénégas, M.-A. (2012) Modelling Social Learning in an Agent-Based New Keynesian Macroeconomic Model. Technical report 2012-20, Cahiers du GREThA, University of Bordeaux.Google Scholar
Salmon, M. (1995) Bounded rationality and learning: Procedural learning. In Kirman, A.P. and Salmon, M. (eds.), Learning and Rationality in Economics, Chap. 8, pp. 236–275. Oxford, UK: Basil Blackwell.Google Scholar
Sargent, T. (1993) Bounded Rationality in Macroeconomics. Oxford, UK: Oxford University Press.Google Scholar
Simon, H. (1955) A behavioural model of rational choice. Quarterly Journal of Economics 69, 99–118.Google Scholar
Simon, H. (1962) The architecture of complexity. Proceedings of the American Philosophical Society 106, 467–481.Google Scholar
Simon, H. (1976) From substantial to procedural rationality. In Latsis, S.J. (ed.), Method and Appraisal in Economics, pp. 129–148. Cambridge, UK: Cambridge University Press.Google Scholar
Simon, H. (1978) Rational Decision-Making in Business Organizations. Nobel Memorial Lecture.Google Scholar
Simon, H.A. (1996) The Sciences of the Artificial, 3rd ed. Cambridge, MA: MIT Press.Google Scholar
Suls, J. and Wheeler, L., eds (2000) Handbook of Social Comparison: Theory and Research. Dordrecht, the Netherlands: Kluwer Academic Publishers.Google Scholar
Tversky, A. and Shaar, E. (1982) Choice under conflict: The dynamics of the deferred decision. Psychological Science 3, 358–361.Google Scholar
Vallée, T. and Yıldızoğlu, M. (2009) Convergence in the finite Cournot oligopoly with social and individual learning. Journal of Economic Behavior and Organization 72 (2), 670–690.Google Scholar
Van den Berg, J. (1955) The Phenomenological Approach to Psychiatry: An Introduction to Recent Phenomenological Psychopathology. Springfield, IL: Thomas.Google Scholar
Von Hippel, E., Franke, N., and Prügl, R. W. (2009) Pyramiding: Efficient search for rare subjects. Research Policy 38 (9), 1397–1406.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, 1–19.Google Scholar
Waltman, L., van Eck, N. J., Dekker, R., and Kaymak, U. (2011) Economic modeling using evolutionary algorithms: The effect of a binary encoding of strategies. Journal of Evolutionary Economics 21, 737–756.Google Scholar
Yang, S. (2008) Genetic algorithms with memory- and elitism-based immigrants in dynamic environments. Evolutionary Computation 16 (3), 385–416.Google Scholar
Yıldızoğlu, M. (2001) Connecting adaptive behaviour and expectations in models of innovation: The potential role of artificial neural networks. European Journal of Economic and Social Systems 15 (3), 51–65.Google Scholar
Yıldızoğlu, M., Sénégas, M.-A., Salle, I., and Zumpe, M. (2014) Learning the optimal buffer-stock consumption rule of Carroll. Macroeconomic Dynamics 18 (4), 727–752.Google Scholar