  Evidence-based decision-making entails collecting (costly) observations about
an underlying phenomenon of interest, and subsequently committing to an
(informed) decision on the basis of accumulated evidence. In this setting,
active sensing is the goal-oriented problem of efficiently selecting which
acquisitions to make, and when and what decision to settle on. As its
complement, inverse active sensing seeks to uncover an agent's preferences and
strategy given their observable decision-making behavior. In this paper, we
develop an expressive, unified framework for the general setting of
evidence-based decision-making under endogenous, context-dependent time
pressure---which requires negotiating (subjective) tradeoffs between accuracy,
speediness, and cost of information. Using this language, we demonstrate how it
enables modeling intuitive notions of surprise, suspense, and optimality in
decision strategies (the forward problem). Finally, we illustrate how this
formulation enables understanding decision-making behavior by quantifying
preferences implicit in observed decision strategies (the inverse problem).
