Integrating human cognitive traits in synthetic intelligent agents to understand market dynamics – CogFinAIgent

Understanding how individuals interact in large-scale social and economic structures, e.g. financial markets, is a key challenge for artificial intelligence systems. On the one hand, we need to grasp how markets shape decision making and on the other, it is crucial to grasp how human cognitive traits impact and structure market dynamics. We aim to characterize these feedbacks using intelligent agent-based models (ABM-AIs) whose properties link individual and macro levels. We will combine ABM-AIs with a computational neuroscience approach to endow agents with realistic learning and key human cognitive traits (impulsivity, confirmation bias, imitation) to understand how they impact market dynamics. We will then turn the tables and use the developed ABM-AI market model platform to structure human experiments, to study how the macro-level impacts individual cognition and patterns of decision making in the financial market. Finally we will then use our ABM-AI platform to analyze a large-scale trader-resolved database to identify the propensity of biases in populations of professional traders. The project aims to identify the impact of interplay of human and artificial cognition on macro-market dynamics.