While Herbert Simon espoused development of general models of behavior, he also strongly advo-cated that these models be based on realistic assumptions about humans and therefore reflect the complexity of human cognition and social systems (Simon 1997). Hence, the model of bounded rationality is typically lauded as a realistic model of behavior, but its application to studies of policy processes and policy analysis has been somewhat limited because the more complex models of de-cision processes are not well suited for precise predictions about the outcomes of such processes. One promising approach to BR- based policy studies would be to couple research on bounded ra-tionality with agent-based modeling. Agent-based models (ABMs) are computational models for simulating the behavior and interactions of any number of decision makers in a dynamic system. Agent-based models are better suited than are general equilibrium models for capturing behavior patterns of complex systems. ABMs may have the potential to represent complex systems without oversimplifying them. At the same time, research in bounded rationality and behavioral economics has already yielded many insights that could inform the modeling with regards to behavioral rules. Bounded rationality research therefore could ensure realistic modeling while the ABM approach enhances the predictive value of behavioral modeling. This paper thus discusses the promise and limitations of using agent-based models to advance the use of the bounded rationality framework in the use of policy studies. We discuss the potential for modeling of policy processes, but primarily for policy analysis.