SOCIAL LEARNING ABOUT CONSUMPTION

This paper applies a social learning model to the optimal consumption rule of Allen & Carroll (2001), and delivers convincing convergence dynamics towards the optimal rule. These findings constitute a significant improvement regarding previous results in the literature, both in terms of speed of convergence and parsimony of the learning model. The learning model exhibits several appealing features: it is frugal, easy to apply to a range of learning objectives, requires few procedures and little information. Particular care is given to behavioural interpretation of the modelling 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 modelling social learning behaviour. Our results suggest a method for modelling bounded rationality, which could be tested most interestingly within the framework of a wide range of economic models with adaptive dynamics.

[1]  P. Collard,et al.  Modeling Luxury Consumption: An Inter-Income Classes Study of Demand Dynamics and Social Behaviors , 2013 .

[2]  A. Rubinstein Modeling Bounded Rationality , 1998 .

[3]  Larry J. Eshelman,et al.  Foundations of Genetic Algorithms-2 , 1993 .

[4]  J. Hutchinson,et al.  Simple heuristics and rules of thumb: Where psychologists and behavioural biologists might meet , 2005, Behavioural Processes.

[5]  H. Simon Method and Appraisal in Economics: From substantive to procedural rationality , 1976 .

[6]  D. Fudenberg,et al.  The Theory of Learning in Games , 1998 .

[7]  Nathan Palmer Learning to Consume : Individual versus Social Learning , 2022 .

[8]  D.E. Goldberg,et al.  Classifier Systems and Genetic Algorithms , 1989, Artif. Intell..

[9]  HERBERT A. SIMON,et al.  The Architecture of Complexity , 1991 .

[10]  Francisco Herrera,et al.  Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis , 1998, Artificial Intelligence Review.

[11]  Christopher D. Carroll,et al.  INDIVIDUAL LEARNING ABOUT CONSUMPTION , 2001 .

[12]  Dominique Torre,et al.  Residents' Influence on the Adoption of Environmental Norms in Tourism , 2013 .

[13]  Raphaël Chiappini,et al.  Persistence vs. mobility in industrial and technological specialisations: evidence from 11 Euro area countries , 2014 .

[14]  C. Antonelli,et al.  Knowledge Cumulability and Complementarity in the Knowledge Generation Function , 2013 .

[15]  Thomas Lux,et al.  Genetic learning as an explanation of stylized facts of foreign exchange markets , 2005 .

[16]  Jasmina Arifovic,et al.  Genetic algorithms and inflationary economies , 1995 .

[17]  C. Carroll,et al.  Buffer-Stock Saving and the Life Cycle/Permanent Income Hypothesis , 1996 .

[18]  Jerry Suls,et al.  Handbook of social comparison : theory and research , 2000 .

[19]  Murat Yildizoglu,et al.  Connecting Adaptive Behaviour and Expectations in Models of Innovation: The Potential Role of Artificial Neural Networks , 2001 .

[20]  Murat Yıldızoğlu,et al.  LEARNING THE OPTIMAL BUFFER-STOCK CONSUMPTION RULE OF CARROLL , 2012, Macroeconomic Dynamics.

[21]  A. Janetos Strategies of female mate choice: A theoretical analysis , 1980, Behavioral Ecology and Sociobiology.

[22]  J. Binswanger,et al.  Dynamic decision making with feasibility goals: A procedural-rationality approach , 2011 .

[23]  Maëlle Della Peruta,et al.  Le rôle des connaissances architecturales dans l’élaboration de la plateforme technologique d’un écosystème en émergence: le cas des plateformes NFC , 2013 .

[24]  Mark Salmon,et al.  Bounded Rationality and Learning: Procedural Learning , 1994 .

[25]  H. Simon Rational Decision Making in Business Organizations , 1978 .

[26]  L. Booker Foundations of genetic algorithms. 2: L. Darrell Whitley (Ed.), Morgan Kaufmann, San Mateo, CA, 1993, ISBN 1-55860-263-1, 322 pp., US$45.95 , 1994 .

[27]  Nicolaas J. Vriend,et al.  An Illustration of the Essential Difference between Individual and Social Learning, and its Consequences for Computational Analyses , 1998 .

[28]  C. Carroll A Theory of the Consumption Function, with and Without Liquidity Constraints (Expanded Version) , 2001 .

[29]  Alexander Rosenberg,et al.  Method and Appraisal in Economics. , 1976 .

[30]  A. Tversky,et al.  Choice under Conflict: The Dynamics of Deferred Decision , 1992 .

[31]  Murat Yıldızoğlu,et al.  Modelling Social Learning in an Agent-Based New Keynesian Macroeconomic Model , 2012 .

[32]  P. Howitt,et al.  Adaptive Consumption Behavior , 2009 .

[33]  H. Simon The Sciences of the Artificial, (Third edition) , 1997 .

[34]  C. Charlier,et al.  Distortion Effects of Export Quota Policy: An Analysis of the China-Raw Materials Dispute , 2014 .

[35]  M. Friedman,et al.  Essays in Positive Economics , 1954 .

[36]  S. Bikhchandani,et al.  Learning from the behavior of others : conformity, fads, and informational cascades , 1998 .

[37]  Shengxiang Yang,et al.  Genetic Algorithms with Memory- and Elitism-Based Immigrants in Dynamic Environments , 2008, Evolutionary Computation.

[38]  Marco Grazzi,et al.  Dynamics of investment and firm performance: comparative evidence from manufacturing industries , 2013 .

[39]  A. Deaton Saving and Liquidity Constraints , 1989 .

[40]  Ulrich Hoffrage,et al.  Models of bounded rationality : The approach of fast and frugal heuristics , 2004 .

[41]  Mark Salmon,et al.  Learning and Rationality in Economics , 1995 .

[42]  Uzay Kaymak,et al.  Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies , 2009 .

[43]  R. Selten,et al.  Bounded rationality: The adaptive toolbox , 2000 .

[44]  Amel Attour Adoption et modèles de diffusion régionale de l’innovation dans les gouvernements locaux: le cas du développement de l’e-Gouvernement en Lorraine , 2012 .

[45]  Murat Yildizoglu Connecting adaptive behaviour and expectations in models of innovation: The Potential Role of Artificial Neural Networks , 2001 .

[46]  Colin Camerer,et al.  Learning and Visceral Temptation in Dynamic Savings Experiments , 2008 .

[47]  D Kahneman,et al.  On the reality of cognitive illusions. , 1996, Psychological review.

[48]  Herbert Dawid,et al.  Learning of equilibria by a population with minimal information , 1997 .

[49]  R. Arena Sraffa's and Wittgenstein's Crossed Influences: Forms of Life and Snapshots , 2013 .

[50]  John Duffy,et al.  A model of learning and emulation with artificial adaptive agents , 1998 .

[51]  Eric von Hippel,et al.  “Pyramiding: Efficient search for rare subjects” , 2009 .

[52]  Liquidity in European Equity ETFs: What Really Matters? , 2013 .

[53]  Jürgen Branke,et al.  Memory enhanced evolutionary algorithms for changing optimization problems , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[54]  H. Simon,et al.  From substantive to procedural rationality , 1976 .

[55]  Latsis,et al.  Method and Appraisal in Economics: Contents , 1976 .

[56]  Thomas Lux,et al.  Genetic learning as an explanation of stylized facts of foreign exchange markets , 2003, 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings..

[57]  Glenn Ellison,et al.  Word-of-Mouth Communication and Social Learning , 1995 .

[58]  Neil B. Niman,et al.  Natural images in economic thought: The role of biological analogies in the theory of the firm , 1994 .

[59]  Pascal Huguet,et al.  Social comparison choices in the classroom: further evidence for students' upward comparison tendency and its beneficial impact on performance , 2001 .

[60]  Herbert A. Simon,et al.  The Sciences of the Artificial , 1970 .

[61]  T. Sargent Bounded rationality in macroeconomics , 1993 .

[62]  T. Vallée,et al.  Convergence in the Finite Cournot Oligopoly with Social and Individual Learning , 2009 .

[63]  Jasmina Arifovic EVOLUTIONARY ALGORITHMS IN MACROECONOMIC MODELS , 2000, Macroeconomic Dynamics.

[64]  W. Arthur Designing Economic Agents that Act Like Human Agents: A Behavioral Approach to Bounded Rationality , 1991 .

[65]  Jasmina Arifovic,et al.  Social Learning and Monetary Policy Rules , 2007 .

[66]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[67]  J. Holland,et al.  Artificial Adaptive Agents in Economic Theory , 1991 .

[68]  Glenn Ellison,et al.  Rules of Thumb for Social Learning , 1993, Journal of Political Economy.

[69]  Kurt Hornik,et al.  The dynamics of genetic algorithms in interactive environments , 1996 .

[70]  H. Uhlig,et al.  Rules of Thumb versus Dynamic Programming , 1999 .

[71]  H. Simon,et al.  A Behavioral Model of Rational Choice , 1955 .

[72]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[73]  K. Judd Computationally Intensive Analyses in Economics , 2006 .