Inverse Active Sensing: Modeling and Understanding Timely Decision-Making

TL;DR

This paper introduces a unified framework for modeling and understanding decision-making processes under time pressure, enabling the analysis of both optimal strategies and inverse inference of preferences from observed behavior.

Contribution

It develops an expressive, unified model for evidence-based decision-making under endogenous time constraints, addressing both forward and inverse problems.

Findings

Modeling of surprise, suspense, and optimality in decision strategies.

Framework for inverse inference of agent preferences from observed decisions.

Application to understanding decision-making behavior under time pressure.

Abstract

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).