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This thesis aims to provide a foundation for risk-aware decision making. Decision making under uncertainty is a core capability of an autonomous agent. A cornerstone for with long-term autonomy and safety is risk-aware decision making. A risk-aware model fully accounts for a known set of risks in the environment, with respect to the problem under consideration, and the process of decision making using such a model is risk-aware decision making. Formulating risk-aware models is critical for robust reasoning under uncertainty, since the impact of using less accurate models may be catastrophic in extreme cases due to overly optimistic view of problems. I propose adaptive modeling, a framework that helps balance the trade-off between model simplicity and risk awareness, for different notions of risks, while remaining computationally tractable.