# A General Framework for Estimating Preferences Using Response Time Data

arXiv: https://arxiv.org/abs/2507.20403 · HTML: https://mlsanity.com/paper/2507.20403/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 55 references that are in the map.
- First posted on arXiv 27 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [Size-Independent Sample Complexity of Neural Networks](https://mlsanity.com/paper/1712.06541/) (2017)
- [Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition](https://mlsanity.com/paper/1503.02101/) (2015)
- [Deep Learning without Poor Local Minima](https://mlsanity.com/paper/1605.07110/) (2016)
- [Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)](https://mlsanity.com/paper/1306.2119/) (2013)
- [Distribution-Independent PAC Learning of Halfspaces with Massart Noise](https://mlsanity.com/paper/1906.10075/) (2019)
- [Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regression](https://mlsanity.com/paper/1303.6149/) (2013)
- [Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noise](https://mlsanity.com/paper/2306.16352/) (2023)
- [A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noise](https://mlsanity.com/paper/2501.09691/) (2025)
- [Learning Noisy Halfspaces with a Margin: Massart is No Harder than Random](https://mlsanity.com/paper/2501.09851/) (2025)
- [Preference Learning with Response Time: Robust Losses and Guarantees](https://mlsanity.com/paper/2505.22820/) (2025)

## Built on by (most cited first, 3 of 3)

- [AI Alignment From Social Choice Perspectives](https://mlsanity.com/paper/2606.21550/) (2026)
- [Response Time Enhances Alignment with Heterogeneous Preferences](https://mlsanity.com/paper/2605.06987/) (2026)
- [Designing Agentic AI Workflow Portfolios under Imperfect Selection and Compute Cost](https://mlsanity.com/paper/2609.18126/) (2026)

## Abstract (arXiv)

We propose a general methodology for recovering preference parameters from data on choices and response times. Our methods yield estimates with fast (1/n for n data points) convergence rates when specialized to the popular Drift Diffusion Model (DDM), but are broadly applicable to generalizations of the DDM as well as to alternative models of decision making that make use of response time data. The paper develops an empirical application to an experiment on intertemporal choice, showing that the use of response times delivers predictive accuracy and matters for the estimation of economically relevant parameters.