Agent-based modeling is a scientific research method based on computer models that simulate the behavior and interactions of autonomous agents in a complex system [16]. Agent-based models represent the system as a collection of individual agents, each with its own unique characteristics, rules, and behaviors. These agents interact with one another and with their environment, often resulting in emergent patterns and behaviors that can be difficult to predict using traditional mathematical models. Agent-based models can test and compare multiple variables in simulated financial markets, thereby more accurately predicting and evaluating changes and risks in financial markets. However, agent-based modeling may also have some limitations such as data requirements and validation challenges, as accurate agent-based modeling often demands detailed data on individual agents and their interactions, which may not always be readily available or feasible to collect, and researchers should ensure that the model’s behavior aligns with real-world observations and is a faithful representation of the system under study, which may be difficult.

The agent-based modeling method has been widely used to evaluate the effectiveness of investment portfolios, predict market volatility, and other aspects. Yang et al. (2022) [17] discussed the use of the method in studying market microstructure, financial crises, and market regulation. Dehkordi et al. (2023) [18] provided a comprehensive overview of the use of agent-based modeling in finance, including its strengths and weaknesses. They argued that the method can provide valuable insights into market behavior, but that care must be taken in constructing the models to ensure they accurately reflect the real-world dynamics of financial markets. Axtell and Farmer (2020) [19] reviewed the use of the method in studying market stability, asset pricing, and other aspects of finance. They argued that agent-based modeling has the potential to transform our understanding of financial markets and improve our ability to predict and prevent financial crises.

In empirical study, the influence of financial literacy on stock market participation and financial behavior is well established. However, in this work, the agent-based modeling method has unique advantages. First, it can consider investors with different levels of financial literacy in the experimental environment, avoiding interference factors that are difficult to control in the actual market. However, in empirical research, there is a possibility of omitted variables or a lack of certain unobservable changes in investor sentiment or psychological aspects [20]. Second, the agent-based modeling method can quickly generate a large amount of data, thereby improving the accuracy and credibility of the research [21]. Finally, through the analysis of the experimental results, we can determine what will happen if we implement certain measures to enhance the financial literacy of all investors or a particular group of investors, such as strengthening information disclosure or providing more investor education. These measures require significant costs to implement. In particular, investor education is a long-term and resource-intensive undertaking, and its effectiveness is uncertain. Empirical research can only analyze outcomes that have already occurred, while agent-based models have a forward-looking nature, which can reduce the trial-and-error costs of policies.