# Inverse Active Sensing: Modeling and Understanding Timely Decision-Making

arXiv: https://arxiv.org/abs/2006.14141 · HTML: https://mlsanity.com/paper/2006.14141/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [A Survey on Active Feature Acquisition Strategies](https://mlsanity.com/paper/2502.11067/) (2025)
- [Invariant Causal Imitation Learning for Generalizable Policies](https://mlsanity.com/paper/2311.01489/) (2023)
- [POETREE: Interpretable Policy Learning with Adaptive Decision Trees](https://mlsanity.com/paper/2203.08057/) (2022)
- [Inverse Contextual Bandits: Learning How Behavior Evolves over Time](https://mlsanity.com/paper/2107.06317/) (2021)
- [The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation](https://mlsanity.com/paper/2106.04240/) (2021)
- [Inverse Decision Modeling: Learning Interpretable Representations of Behavior](https://mlsanity.com/paper/2310.18591/) (2023)
- [Evaluation of Active Feature Acquisition Methods for Time-varying Feature Settings](https://mlsanity.com/paper/2312.01530/) (2023)
- [Evaluation of Active Feature Acquisition Methods for Static Feature Settings](https://mlsanity.com/paper/2312.03619/) (2023)
- [Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning](https://mlsanity.com/paper/2310.19831/) (2023)
- [SepsisLab: Early Sepsis Prediction with Uncertainty Quantification and Active Sensing](https://mlsanity.com/paper/2407.16999/) (2024)
- [RGMDT: Return-Gap-Minimizing Decision Tree Extraction in Non-Euclidean Metric Space](https://mlsanity.com/paper/2410.16517/) (2024)
- [Treatment-RSPN: Recurrent Sum-Product Networks for Sequential Treatment Regimes](https://mlsanity.com/paper/2211.07052/) (2022)

## Abstract (arXiv)

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).