mlsanityData to 14 Oct 2026

A General Framework for Estimating Preferences Using Response Time Data

arXiv 2507.20403 ↗First posted 27 Jul 2025econ.TH cs.LGcited by 3

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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.

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

10 of 55 references.

References · 10

  1. 2017Size-Independent Sample Complexity of Neural Networkscited by 290
  2. 2015Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decompositioncited by 243
  3. 2016Deep Learning without Poor Local Minimacited by 234
  4. 2013Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)cited by 111
  5. 2019Distribution-Independent PAC Learning of Halfspaces with Massart Noisecited by 54
  6. 2013Adaptivity of averaged stochastic gradient descent to local strong convexity for logistic regressioncited by 32
  7. 2023Information-Computation Tradeoffs for Learning Margin Halfspaces with Random Classification Noisecited by 9
  8. 2025A Near-optimal Algorithm for Learning Margin Halfspaces with Massart Noisecited by 6
  9. 2025Learning Noisy Halfspaces with a Margin: Massart is No Harder than Randomcited by 5
  10. 2025Preference Learning with Response Time: Robust Losses and Guaranteescited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026AI Alignment From Social Choice Perspectivescited by 2
  2. 2026Response Time Enhances Alignment with Heterogeneous Preferencescited by 1
  3. 2026Designing Agentic AI Workflow Portfolios under Imperfect Selection and Compute Costcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Enhancing Preference-based Linear Bandits via Human Response Time
  2. 2026Evaluating LLM-Generated Preference Distributions
  3. 2025Rethinking Direct Preference Optimization in Diffusion Models
  4. 2025Preference learning made easy: Everything should be understood through win rate
  5. 2023Can LLMs Capture Human Preferences?
  6. 2025Beyond Reward Margin: Rethinking and Resolving Likelihood Displacement in Diffusion Models via Video Generation
  7. 2026LLM-Derived Preference Judgments Are Not Self-Consistent
  8. 2026Less Data, Better Alignment: Data-Centric Multi-Evaluator Agreement for Preference Optimization