Inverse Active Sensing: Modeling and Understanding Timely Decision-Making

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

Bibtex »Metadata »Paper »Supplemental »

Bibtek download is not availble in the pre-proceeding


Authors

Daniel Jarrett, Mihaela van der Schaar

Abstract

<p>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, <em>active sensing</em> is the goal-oriented problem of efficiently selecting which acquisitions to make, and when and what decision to settle on. As its complement, <em>inverse active sensing</em> 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 <em>modeling</em> intuitive notions of surprise, suspense, and optimality in decision strategies (the forward problem). Finally, we illustrate how this formulation enables <em>understanding</em> decision-making behavior by quantifying preferences implicit in observed decision strategies (the inverse problem).</p>