Inverse Active Sensing: Modeling and Understanding Timely Decision-Making
This work addresses the challenge of interpreting decision-making behavior in contexts like healthcare or finance, though it appears incremental as it builds on existing active sensing concepts.
The paper tackles the problem of modeling and understanding timely decision-making under time pressure and cost constraints, developing a unified framework for both forward (optimal strategy design) and inverse (preference inference) aspects of evidence-based decision-making.
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).