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The (Neural) Dynamics of Stochastic Choice

Citations

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Cited by:

  1. 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.
  2. Strittmatter, Anthony & Sunde, Uwe & Zegners, Dainis, 2022. "Speed, Quality, and the Optimal Timing of Complex Decisions: Field Evidence," Rationality and Competition Discussion Paper Series 317, CRC TRR 190 Rationality and Competition.
  3. Mogens Fosgerau & Emerson Melo & André de Palma & Matthew Shum, 2020. "Discrete Choice And Rational Inattention: A General Equivalence Result," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 61(4), pages 1569-1589, November.
  4. Carlos Alós-Ferrer & Johannes Buckenmaier, 2021. "Cognitive sophistication and deliberation times," Experimental Economics, Springer;Economic Science Association, vol. 24(2), pages 558-592, June.
  5. Jose Apesteguia & Miguel A Ballester, 2021. "Separating Predicted Randomness from Residual Behavior," Journal of the European Economic Association, European Economic Association, vol. 19(2), pages 1041-1076.
  6. Yukalov, V.I., 2021. "A resolution of St. Petersburg paradox," Journal of Mathematical Economics, Elsevier, vol. 97(C).
  7. Brocas, Isabelle & Carrillo, Juan D., 2021. "Value computation and modulation: A neuroeconomic theory of self-control as constrained optimization," Journal of Economic Theory, Elsevier, vol. 198(C).
  8. Ryan Webb & Paul W. Glimcher & Kenway Louie, 2021. "The Normalization of Consumer Valuations: Context-Dependent Preferences from Neurobiological Constraints," Management Science, INFORMS, vol. 67(1), pages 93-125, January.
  9. Fedor Sandomirskiy & Omer Tamuz, 2023. "Decomposable Stochastic Choice," Papers 2312.04827, arXiv.org.
  10. Hirmas, Alejandro & Engelmann, Jan B., 2023. "Impulsiveness moderates the effects of exogenous attention on the sensitivity to gains and losses in risky lotteries," Journal of Economic Psychology, Elsevier, vol. 95(C).
  11. 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.
  12. 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.
  13. 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.
  14. Webb, Ryan & Mehta, Nitin & Levy, Ifat, 2021. "Assessing consumer demand with noisy neural measurements," Journal of Econometrics, Elsevier, vol. 222(1), pages 89-106.
  15. 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.
  16. Pirrone, Angelo & Gobet, Fernand, 2021. "Is attentional discounting in value-based decision making magnitude sensitive?," LSE Research Online Documents on Economics 108608, London School of Economics and Political Science, LSE Library.
  17. Geoffrey Fisher, 2023. "Measuring the Factors Influencing Purchasing Decisions: Evidence From Cursor Tracking and Cognitive Modeling," Management Science, INFORMS, vol. 69(8), pages 4558-4578, August.
  18. 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.
  19. Duffy, Sean & Smith, John, 2020. "An economist and a psychologist form a line: What can imperfect perception of length tell us about stochastic choice?," MPRA Paper 99417, University Library of Munich, Germany.
  20. V. I. Yukalov, 2021. "A Resolution of St. Petersburg Paradox," Papers 2111.14635, arXiv.org.
  21. Taro Ohdoko & Satoru Komatsu, 2023. "Integrating a Pareto-Distributed Scale into the Mixed Logit Model: A Mathematical Concept," Mathematics, MDPI, vol. 11(23), pages 1-22, November.
  22. Chernulich, Aleksei, 2021. "Modelling reference dependence for repeated choices: A horse race between models of normalisation," Journal of Economic Psychology, Elsevier, vol. 87(C).
  23. Linda Q. Yu & Jason Dana & Joseph W. Kable, 2022. "Individuals with ventromedial frontal damage display unstable but transitive preferences during decision making," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
  24. Emerson Melo, 2021. "Learning In Random Utility Models Via Online Decision Problems," CAEPR Working Papers 2022-003 Classification-D, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
  25. Pennesi, Daniele, 2021. "Intertemporal discrete choice," Journal of Economic Behavior & Organization, Elsevier, vol. 186(C), pages 690-706.
  26. Michel Wedel & Rik Pieters & Ralf Lans, 2023. "Modeling Eye Movements During Decision Making: A Review," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 697-729, June.
  27. D. Pennesi, 2016. "Deciding fast and slow," Working Papers wp1082, Dipartimento Scienze Economiche, Universita' di Bologna.
  28. 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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