Close
2025-26
Economics + computer science + cognitive neuroscience
Research question
Artificial intelligence can have a big impact on the functioning of financial markets, e.g., through machine learning and algorithmic trading. This impact may be positive, enhancing efficiency and liquidity, or negative, offering more opportunities for manipulation and collusion, and amplifying volatility and instability. Our research question is: how does the interaction between humans and machines affect the process of trading and market making, and what are the consequences for the efficiency and stability of financial markets? To answer this question, we focus on
human behavior and how it adapts to the interaction with algorithms in financial markets.
Project’s aims and objectives
The impact of artificial intelligence and machine learning on financial markets is an emerging area of research. While economists, computer scientists, and cognitive neuroscientists have begun to explore related questions, much of this work remains confined within individual disciplines and interdisciplinary work is lacking. For instance, some studies use simulation techniques to understand whether Qlearning (a type of reinforcement learning consisting in learning the best action based on the rewards received from actions previously taken) converges to a competitive financial market but made no attempt to understand the human–machine interactions. At the same time, Q-learning’s neural and computational underpinnings have been heavily investigated in neuroscience but with limited applications to large social interactions, such as financial markets.
This interdisciplinary project aims to analyze the human–algorithm interaction in financial markets by integrating tools and approaches from economics, computer science, and cognitive neuroscience. We want to investigate the phenomenon both theoretically and with experiments, with a first design of a virtual financial market with humans and machines. We are interested in understanding how machines learn from data which are the result of past trade by humans and how humans adapt and trade against machines.
Ultimately, our goal is to uncover the mechanisms driving human–machine interactions in financial contexts and to generate foundational insights that can inform future research, regulation, and the design of more robust market systems.
Principal Investigator
Professor Antonio Guarino
Professor of Economics, Department of Economics, Social & Historical Sciences
Non-Social Science Co-Investigator
Professor Mirco Musolesi
Professor of Computer Science, Department of Computer Science, Engineering Sciences
Second Co-Investigator
Professor Benedetto De Martino
Professor of Cognitive Neuroscience, Institute of Cognitive Neuroscience, Brain Sciences
Early Career Researchers
Hee Yoon, Lecturer in Economics and Finance Economics, School of Management, Engineering Sciences
Huilei Kang, PhD Student, Economics, Social & Historical Sciences
Pranav Sankhe, PhD Student, Cognitive, Neuroscience, Brain Sciences
Olivia Macmillan Scott, PhD Student Computer Science, Engineering Sciences
Elizaveta Tennant, PhD Student, Computer Science, Engineering Sciences
Xiaotong Wu, PhD Student Economics, School of Management, Engineering Sciences