Learning and cognition in financial markets: A paradigm shift for agent-based models

Johann Lussange, Alexis Belianin, Sacha Bourgeois-Gironde, Boris Gutkin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications - Proceedings of the 2020 Intelligent Systems Conference IntelliSys Volume 3
EditorsKohei Arai, Supriya Kapoor, Rahul Bhatia
Number of pages15
ISBN (Print)9783030551896
StatePublished - 2021
Externally publishedYes
EventIntelligent Systems Conference, IntelliSys 2020 - London, United Kingdom
Duration: 3 Sep 20204 Sep 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1252 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceIntelligent Systems Conference, IntelliSys 2020
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2021.


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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science


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