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Norms as emergent properties of adaptive learning: The case of economic routines

Author

Listed:
  • Marco Valente

    (Aalborg University, Aalborg, Denmark)

  • Andrea Bassanini

    (Faculty of Statistics, University "La Sapienza", Rome, Italy, and OECD, Paris, France)

  • Luigi Marengo

    (Department of Economics, University of Trento, Via Inama 1, I-38100 Trento, Italy)

  • Giovanni Dosi

    (Scvola Superiore S. Anna, Pisa, Italy)

Abstract

Interaction among autonomous decision-makers is usually modelled in economics in game-theoretic terms or within the framework of General Equilibrium. Game-theoretic and General Equilibrium models deal almost exclusively with the existence of equilibria and do not analyse the processes which might lead to them. Even when existence proofs can be given, two questions are still open. The first concerns the possibility of multiple equilibria, which game theory has shown to be the case even in very simple models and which makes the outcome of interaction unpredictable. The second relates to the computability and complexity of the decision procedures which agents should adopt and questions the possibility of reaching an equilibrium by means of an algorithmically implementable strategy. Some theorems have recently proved that in many economically relevant problems equilibria are not computable. A different approach to the problem of strategic interaction is a "constructivist" one. Such a perspective, instead of being based upon an axiomatic view of human behaviour grounded on the principle of optimisation, focuses on algorithmically implementable "satisfycing" decision procedures. Once the axiomatic approach has been abandoned, decision procedures cannot be deduced from rationality assumptions, but must be the evolving outcome of a process of learning and adaptation to the particular environment in which the decision must be made. This paper considers one of the most recently proposed adaptive learning models: Genetic Programming and applies it to one the mostly studied and still controversial economic interaction environment, that of oligopolistic markets. Genetic Programming evolves decision procedures, represented by elements in the space of functions, balancing the exploitation of knowledge previously obtained with the search of more productive procedures. The results obtained are consistent with the evidence from the observation of the behaviour of real economic agents.

Suggested Citation

  • Marco Valente & Andrea Bassanini & Luigi Marengo & Giovanni Dosi, 1999. "Norms as emergent properties of adaptive learning: The case of economic routines," Journal of Evolutionary Economics, Springer, vol. 9(1), pages 5-26.
    • Handle: RePEc:spr:joevec:v:9:y:1999:i:1:p:5-26
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    Cited by:

    1. Ballot, Gerard & Taymaz, Erol, 2001. "Training policies and economic growth in an evolutionary world," Structural Change and Economic Dynamics, Elsevier, vol. 12(3), pages 311-329, September.
    2. Giovanni Dosi & Luigi Marengo & Evita Paraskevopoulou & Marco Valente, 2017. "A model of cognitive and operational memory of organizations in changing worlds," Cambridge Journal of Economics, Oxford University Press, vol. 41(3), pages 775-806.
    3. Teppo Felin & Nicolai Foss, 2006. "Individuals and Organizations Thoughts on a Micro-Foundations Project for Strategic Management and Organizational Analysis," DRUID Working Papers 06-01, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.
    4. Jonard, N. & Yfldizoglu, M., 1998. "Technological diversity in an evolutionary industry model with localized learning and network externalities," Structural Change and Economic Dynamics, Elsevier, vol. 9(1), pages 35-53, March.
    5. Tanya Araújo & Miguel St. Aubyn, 2008. "Education, Neighborhood Effects And Growth: An Agent-Based Model Approach," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 11(01), pages 99-117.
    6. Peter Abell & Teppo Felin & Nicolai Foss, 2008. "Building micro-foundations for the routines, capabilities, and performance links," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 29(6), pages 489-502.
    7. Edmund Chattoe-Brown, 1998. "Just How (Un)realistic Are Evolutionary Algorithms As Representations of Social Processes?," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 1(3), pages 1-2.
    8. Elizabeth Webster, 2004. "Firms' decisions to innovate and innovation routines," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 13(8), pages 733-745.
    9. Karolina Safarzyńska & Jeroen Bergh, 2010. "Evolutionary models in economics: a survey of methods and building blocks," Journal of Evolutionary Economics, Springer, vol. 20(3), pages 329-373, June.
    10. Murat YILDIZOGLU, 2009. "Evolutionary approaches of economic dynamics (In French)," Cahiers du GREThA (2007-2019) 2009-16, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).
    11. Smith, Peter, 2004. "Reworking the Standard Model of Competitive Markets: The Role of Fuzzy Logic and Genetic Algorithms in Modelling Complex Non-Linear Economic System," General Discussion Papers 30569, University of Manchester, Institute for Development Policy and Management (IDPM).
    12. Herbert Dawid & Philipp Harting, 2012. "Capturing Firm Behavior in Agent-based Models of Industry Evolution and Macroeconomic Dynamics," Chapters, in: Guido Buenstorf (ed.), Evolution, Organization and Economic Behavior, chapter 6, Edward Elgar Publishing.
    13. Patalano, Roberta, 2007. "Resistance to change. Exploring the convergence of institutions, organizations and the mind toward a common phenomenon," MPRA Paper 3342, University Library of Munich, Germany.
    14. Jukka Kaisla, 2003. "Choice Behaviour: Looking for Remedy to Some Central Logical Problems in Rational Action," Kyklos, Wiley Blackwell, vol. 56(2), pages 245-262, May.
    15. Giovanni Dosi & Marco Faillo & Luigi Marengo, 2018. "Beyond "Bounded Rationality": Behaviours and Learning in Complex Evolving Worlds," LEM Papers Series 2018/26, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    16. Windrum, Paul, 1999. "Simulation models of technological innovation: A Review," Research Memorandum 005, Maastricht University, Maastricht Economic Research Institute on Innovation and Technology (MERIT).
    17. Peter Revay & Claudio Cioffi-Revilla, 2018. "Survey of evolutionary computation methods in social agent-based modeling studies," Journal of Computational Social Science, Springer, vol. 1(1), pages 115-146, January.
    18. Johannes Glückler, 2005. "Making Embeddedness Work: Social Practice Institutions in Foreign Consulting Markets," Environment and Planning A, , vol. 37(10), pages 1727-1750, October.

    More about this item

    Keywords

    Computability ; Genetic Programming ; Oligopoly;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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