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Learning in agent based models

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  • Alan Kirman

    () (GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - CNRS - Centre National de la Recherche Scientifique - ECM - Ecole Centrale de Marseille)

Abstract

This paper examines the process by which agents learn to act in economic environments. Learning is particularly complicated in such situations since the environment is, at least in part, made up of other agents who are also learning. At best, one can hope to obtain analytical results for a rudimentary model. To make progress in understanding the dynamics of learning and coordination in general cases one can simulate agent based models to see whether the results obtained in skeletal models translate into the more general case. Using this approach can help us to understand which are the crucial assumptions in determining whether learning converges and, if so, to which sort of state. Three examples are presented, one in which agents learn to form trading relationships, one in which agents misspecify the model of their environment and a last one in which agents may learn to take actions which are systematically favourable, (or unfavourable) for them. In each case simulating models in which agents operate with simple rules in a complex environment, allows us to examine the role of the type of learning process used by the agents the extent to which they coordinate on a final outcome and the nature of that outcome.

Suggested Citation

  • Alan Kirman, 2010. "Learning in agent based models," Working Papers halshs-00545169, HAL.
  • Handle: RePEc:hal:wpaper:halshs-00545169 Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-00545169
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    References listed on IDEAS

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    Cited by:

    1. Marko Petrovic & Bulent Ozel & Andrea Teglio & Marco Raberto & Silvano Cincotti, 2017. "Eurace Open: An agent-based multi-country model," Working Papers 2017/09, Economics Department, Universitat Jaume I, Castellón (Spain).
    2. Antonelli, Cristiano, 2017. "From the Economics of Information to the Economics of Knowledge. Length: pages 39," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201714, University of Turin.
    3. Liu, Chunping & Minford, Patrick, 2014. "Comparing behavioural and rational expectations for the US post-war economy," Economic Modelling, Elsevier, vol. 43(C), pages 407-415.
    4. Salle, Isabelle & Yıldızoğlu, Murat & Sénégas, Marc-Alexandre, 2013. "Inflation targeting in a learning economy: An ABM perspective," Economic Modelling, Elsevier, vol. 34(C), pages 114-128.
    5. Antonelli, Cristiano, 2017. "From the Economics of Information to the Economics of Knowledge," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201706, University of Turin.
    6. Matteo G. Richiardi, 2015. "The future of agent-based modelling," LABORatorio R. Revelli Working Papers Series 141, LABORatorio R. Revelli, Centre for Employment Studies.
    7. Simone Landini & Mauro Gallegati & Joseph Stiglitz, 2015. "Economies with heterogeneous interacting learning agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(1), pages 91-118, April.
    8. Voronovitsky, Mark, 2015. "The Agent-Based Model of the Closed Market of the One Commodity with Finite Automata as Participants of the Market," MPRA Paper 70439, University Library of Munich, Germany.
    9. Matteo G. Richiardi, 2017. "The Future of Agent-Based Modeling," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 43(2), pages 271-287, March.
    10. Вороновицкий М.М., 2014. "Агент - Ориентированная Модель Замкнутого Однотоварного Рынка," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 50(2), pages 73-87, апрель.
    11. Вороновицкий М.М., 2015. "Агент-Ориентированная Модель Замкнутого Однотоварного Рынка При Рациональном Предпочтении Участников," Журнал Экономика и математические методы (ЭММ), Центральный Экономико-Математический Институт (ЦЭМИ), vol. 51(3), pages 64-80, июль.
    12. Barroso, Ricardo Vieira & Lima, Joaquim Ignacio Alves Vasconcellos & Lucchetti, Alexandre Henrique & Cajueiro, Daniel Oliveira, 2016. "Interbank network and regulation policies: an analysis through agent-based simulations with adaptive learning," MPRA Paper 73308, University Library of Munich, Germany.

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    Keywords

    Learning; agent based models; simulations; equilibria; asymmetric outcomes;

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