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AI-assisted decision-making systems hold immense potential to enhance human judgment, but their effectiveness is often hindered by a lack of understanding of the diverse ways in which humans take AI recommendations. Current research frequently relies on simplified, ``one-size-fits-all'' models to characterize an average human decision-maker, thus failing to capture the heterogeneity of people's decision-making behavior when incorporating AI assistance. To address this, we propose Mix and Match (M&M), a novel computational framework that explicitly models the diversity of human decision-makers and their unique patterns of relying on AI assistance. M&M represents the population of decision-makers as a mixture of distinct decision-making processes, with each process corresponding to a specific type of decision-maker. This approach enables us to infer latent behavioral patterns from limited data of human decisions under AI assistance, offering valuable insights into the cognitive processes underlying human-AI collaboration. Using real-world behavioral data, our empirical evaluation demonstrates that M&M consistently outperforms baseline methods in predicting human decision behavior. Furthermore, through a detailed analysis of the decision-maker types identified in our framework, we provide quantitative insights into nuanced patterns of how different individuals adopt AI recommendations. These findings offer implications for designing personalized and effective AI systems based on the diverse landscape of human behavior patterns in AI-assisted decision-making across various domains.