J. Lussange, Belyanin Alexei, S. Bourgeois-Gironde, B. Gutkin
Learning and cognition in financial markets: A paradigm shift for agent-based models. Intelligent Systems Conference, IntelliSys 2020. London; United Kingdom, 3-4.09.2020 // Advances in Intelligent Systems and Computing (AISC) book series. 2021. Vol. 1252 : Intelligent Systems and Applications. P. 241-255. DOI 10.1007/978-3-030-55190-2_19.
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DOI 10.1007/978-3-030-55190-2_19

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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