<![CDATA[The dynamics of financial markets are shaped by complex interactions among heterogeneous agents, often deviating from the assumptions of classical economic theory. This study explores the use of agent-based models (ABMs) as a computational approach to capture the emergent behaviors and nonlinearities inherent in financial systems. By simulating markets with agents possessing bounded rationality, adaptive expectations, and diverse trading strategies, ABMs offer insights into phenomena such as market bubbles, crashes, and volatility clustering. This paper presents a comprehensive framework for modeling financial markets using ABMs, incorporating key elements such as market microstructure, information diffusion, and behavioral rules. Through a series of simulation experiments, we demonstrate how varying agent behaviors influence price dynamics and systemic risk. The findings highlight the capacity of ABMs to replicate empirical stylized facts observed in real-world markets and to serve as a valuable tool for stress-testing regulatory policies. This research contributes to the growing body of literature advocating for computational economics as a complementary lens to understand the evolving landscape of global financial systems.]]>
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