This paper discusses the methodological relevance of agent-based modeling and simulation (ABMS) in economics and finance and illustrates its analytical value through a simple cryptocurrency market model implemented in NetLogo. The paper first reviews the core principles of ABMS, emphasizing heterogeneous agents, bounded rationality, local interaction, endogenous dynamics and macro-level emergence. It then summarizes the role of ABMS in economic analysis, macroeconomic forecasting, financial market dynamics, systemic risk assessment and cryptocurrency research. In the empirical part, a regime-dependent agent-based model with fundamentalists and non-fundamentalists is used to simulate Bitcoin market dynamics under alternative specifications. The model is driven by macro-fundamental, crypto-fundamental and sentiment signals and validated against the historical Bitcoin price path and stylized facts of returns. The results show that the stabilized noBTC specification achieves the best compromise between price-path accuracy and regime stability, while the reproduction of stylized facts remains only partial. In particular, the simulations capture the lack of return autocorrelation fairly well, but volatility clustering, excess kurtosis and skewness remain weaker than in the benchmark study and in the empirical Bitcoin series. The findings support the view that ABMS is a useful complement to standard analytical and econometric models, especially when interactions, nonlinearities and institutional details are central to the problem under study.

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