Multi Agent Market

Multi-agent market models simulate economic systems by representing individual agents (e.g., traders, households, firms) interacting within a market environment. Current research focuses on improving model realism and predictive power using reinforcement learning, deep learning, and Bayesian optimization to calibrate agent behaviors and market dynamics, often incorporating elements of behavioral finance to capture human decision-making limitations. These models are valuable for analyzing market microstructure, predicting the impact of policy changes (e.g., tax credits), and evaluating the performance of trading strategies, offering insights into market stability and efficiency. The resulting advancements contribute to a deeper understanding of complex economic systems and inform the design of more robust and efficient financial markets.