  Those designing autonomous systems that interact with humans will invariably
face questions about how humans think and make decisions. Fortunately,
computational cognitive science offers insight into human decision-making using
tools that will be familiar to those with backgrounds in optimization and
control (e.g., probability theory, statistical machine learning, and
reinforcement learning). Here, we review some of this work, focusing on how
cognitive science can provide forward models of human decision-making and
inverse models of how humans think about others' decision-making. We highlight
relevant recent developments, including approaches that synthesize blackbox and
theory-driven modeling, accounts that recast heuristics and biases as forms of
bounded optimality, and models that characterize human theory of mind and
communication in decision-theoretic terms. In doing so, we aim to provide
readers with a glimpse of the range of frameworks, methodologies, and
actionable insights that lie at the intersection of cognitive science and
control research.
