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Improving out-of-sample predictions using response times and a model of the decision process

Citations

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Cited by:

  1. Carlos Alós-Ferrer & Ernst Fehr & Michele Garagnani, 2022. "Identifying nontransitive preferences," ECON - Working Papers 415, Department of Economics - University of Zurich, revised Jan 2023.
  2. Aleksandr Alekseev, 2019. "Using response times to measure ability on a cognitive task," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 65-75, August.
  3. Ryan Webb, 2019. "The (Neural) Dynamics of Stochastic Choice," Management Science, INFORMS, vol. 65(1), pages 230-255, January.
  4. Clithero, John A., 2018. "Response times in economics: Looking through the lens of sequential sampling models," Journal of Economic Psychology, Elsevier, vol. 69(C), pages 61-86.
  5. Shuo Liu & Nick Netzer, 2023. "Happy Times: Measuring Happiness Using Response Times," American Economic Review, American Economic Association, vol. 113(12), pages 3289-3322, December.
  6. Dewan, Ambuj & Neligh, Nathaniel, 2020. "Estimating information cost functions in models of rational inattention," Journal of Economic Theory, Elsevier, vol. 187(C).
  7. Benjamin Hébert & Michael Woodford, 2021. "Neighborhood-Based Information Costs," American Economic Review, American Economic Association, vol. 111(10), pages 3225-3255, October.
  8. Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Aldo Rustichini, 2023. "Multinomial Logit Processes and Preference Discovery: Inside and Outside the Black Box," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(3), pages 1155-1194.
  9. Andrew Schotter & Isabel Trevino, 2021. "Is response time predictive of choice? An experimental study of threshold strategies," Experimental Economics, Springer;Economic Science Association, vol. 24(1), pages 87-117, March.
  10. Carlos Alós-Ferrer & Ernst Fehr & Nick Netzer, 2021. "Time Will Tell: Recovering Preferences When Choices Are Noisy," Journal of Political Economy, University of Chicago Press, vol. 129(6), pages 1828-1877.
  11. S. Cerreia-Vioglio & F. Maccheroni & M. Marinacci & A. Rustichini, 2017. "Multinomial logit processes and preference discovery: inside and outside the black box," Working Papers 615, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  12. Hebert, Benjamin & Woodford, Michael, 2018. "Information Costs and Sequential Information Sampling," Research Papers 3751, Stanford University, Graduate School of Business.
  13. Huseynov, Samir & Palma, Marco A. & Ahmad, Ghufran, 2021. "Does the magnitude of relative calorie distance affect food consumption?," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 530-551.
  14. Carlos Alós-Ferrer & Michele Garagnani, 2022. "Strength of preference and decisions under risk," Journal of Risk and Uncertainty, Springer, vol. 64(3), pages 309-329, June.
  15. Carlo Baldassi & Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Marco Pirazzini, 2020. "A Behavioral Characterization of the Drift Diffusion Model and Its Multialternative Extension for Choice Under Time Pressure," Management Science, INFORMS, vol. 66(11), pages 5075-5093, November.
  16. Sangil Lee & Chris M. Glaze & Eric T. Bradlow & Joseph W. Kable, 2020. "Flexible Utility Function Approximation via Cubic Bezier Splines," Psychometrika, Springer;The Psychometric Society, vol. 85(3), pages 716-737, September.
  17. Hébert, Benjamin & Woodford, Michael, 2023. "Rational inattention when decisions take time," Journal of Economic Theory, Elsevier, vol. 208(C).
  18. Shen Li & Yuyang Zhang & Zhaolin Ren & Claire Liang & Na Li & Julie A. Shah, 2024. "Enhancing Preference-based Linear Bandits via Human Response Time," Papers 2409.05798, arXiv.org, revised Oct 2024.
  19. Guangzhong Hu & Yuming Liu & Kai Liu & Xiaoxu Yang, 2023. "Research on Data-Driven Dynamic Decision-Making Mechanism of Mega Infrastructure Project Construction," Sustainability, MDPI, vol. 15(12), pages 1-25, June.
  20. Valdes Salvador & Gonzalo ValdesEdwards, 2023. "Microfoundations of Expected Utility and Response Times," Papers 2302.09421, arXiv.org.
  21. Duarte Gonc{c}alves, 2024. "Speed, Accuracy, and Complexity," Papers 2403.11240, arXiv.org, revised Jun 2024.
  22. Konrad Grabiszewski & Alex Horenstein, 2022. "Profiling dynamic decision-makers," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-22, April.
  23. John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Feb 2021.
  24. David J. Cooper & Ian Krajbich & Charles N. Noussair, 2019. "Choice-Process Data in Experimental Economics," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 1-13, August.
  25. Vriens, M. & Vidden, C. & Schomaker, J., 2020. "What I see is what I want: Top-down attention biasing choice behavior," Journal of Business Research, Elsevier, vol. 111(C), pages 262-269.
  26. Cary Frydman & Ian Krajbich, 2022. "Using Response Times to Infer Others’ Private Information: An Application to Information Cascades," Management Science, INFORMS, vol. 68(4), pages 2970-2986, April.
  27. Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci, 2020. "Multinomial logit processes and preference discovery: outside and inside the black box," Working Papers 663, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
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