  With the rapid development of AI-based decision aids, different forms of AI
assistance have been increasingly integrated into the human decision making
processes. To best support humans in decision making, it is essential to
quantitatively understand how diverse forms of AI assistance influence humans'
decision making behavior. To this end, much of the current research focuses on
the end-to-end prediction of human behavior using ``black-box'' models, often
lacking interpretations of the nuanced ways in which AI assistance impacts the
human decision making process. Meanwhile, methods that prioritize the
interpretability of human behavior predictions are often tailored for one
specific form of AI assistance, making adaptations to other forms of assistance
difficult. In this paper, we propose a computational framework that can provide
an interpretable characterization of the influence of different forms of AI
assistance on decision makers in AI-assisted decision making. By
conceptualizing AI assistance as the ``{\em nudge}'' in human decision making
processes, our approach centers around modelling how different forms of AI
assistance modify humans' strategy in weighing different information in making
their decisions. Evaluations on behavior data collected from real human
decision makers show that the proposed framework outperforms various baselines
in accurately predicting human behavior in AI-assisted decision making. Based
on the proposed framework, we further provide insights into how individuals
with different cognitive styles are nudged by AI assistance differently.
