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Proceedings of SAI Intelligent Systems Conference

IntelliSys 2020: Intelligent Systems and Applications pp 241–255Cite as

Learning and Cognition in Financial Markets: A Paradigm Shift for Agent-Based Models

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1252)

Abstract

The history of research in finance and economics has been widely impacted by the field of Agent-based Computational Economics (ACE). While at the same time being popular among natural science researchers for its proximity to the successful methods of physics and chemistry for example, the field of ACE has also received critics by a part of the social science community for its lack of empiricism. Yet recent trends have shifted the weights of these general arguments and potentially given ACE a whole new range of realism. At the base of these trends are found two present-day major scientific breakthroughs: the steady shift of psychology towards a hard science due to the advances of neuropsychology, and the progress of reinforcement learning due to increasing computational power and big data. We outline here the main lines of a computational research study where each agent would trade by reinforcement learning.

Keywords

  • Financial markets
  • Agent-based models
  • Multi-agent systems
  • Reinforcement learning

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Acknowledgment

This work was supported by the RFFI grant nr. 16-51-150007 and CNRS PRC nr. 151199, and received support from FrontCog ANR-17-EURE-0017. A preliminary preprint of this work was uploaded to arXiv  [150].

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  1. Laboratoire des Neurosciences Cognitives, INSERM U960, Département des Études Cognitives, École Normale Supérieure PSL University, 29 rue d’Ulm, 75005, Paris, France

    Johann Lussange & Boris Gutkin

  2. ICEF, National Research University Higher School of Economics and Primakov Institute for World Economy and International Relations, Russian Academy of Sciences, 8 Myasnitskaya st., 101000, Moscow, Russia

    Alexis Belianin

  3. Institut Jean-Nicod, UMR 8129, Département des Études Cognitives, École Normale Supérieure PSL University, 29 rue d’Ulm, 75005, Paris, France

    Sacha Bourgeois-Gironde

  4. Laboratoire d’Économie Mathématique et de Microéconomie Appliquée, EA 4442, Université Paris II Panthéon-Assas, 4 rue Blaise Desgoffe, 75006, Paris, France

    Sacha Bourgeois-Gironde

  5. Center for Cognition and Decision Making, Department of Psychology, NU University Higher School of Economics, 8 Myasnitskaya st., 101000, Moscow, Russia

    Boris Gutkin

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  1. Johann Lussange
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  4. Boris Gutkin
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Correspondence to Johann Lussange .

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  1. Saga University, Saga, Japan

    Prof. Dr. Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Prof. Rahul Bhatia

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Lussange, J., Belianin, A., Bourgeois-Gironde, S., Gutkin, B. (2021). Learning and Cognition in Financial Markets: A Paradigm Shift for Agent-Based Models. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1252. Springer, Cham. https://doi.org/10.1007/978-3-030-55190-2_19

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