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