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Published online by Cambridge University Press:  14 January 2025

Tomasz Strzalecki
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Harvard University, Massachusetts
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References

Abaluck, J., and Adams-Prassl, A. (2021): “What do consumers consider before they choose? Identification from asymmetric demand responses,” The Quarterly Journal of Economics, 136(3), 16111663. 145, 155CrossRefGoogle Scholar
Abbring, J. H. (2010): “Identification of dynamic discrete choice models,” Annual Review of Economy, 2(1), 367394. 158, 161CrossRefGoogle Scholar
Abbring, J. H., and Daljord, Ø. (2020): “Identifying the discount factor in dynamic discrete choice models,” Quantitative Economics, 11(2), 471501. 161CrossRefGoogle Scholar
Abdulkadiroglu, A., Angrist, J. D., Narita, Y., and Pathak, P. A. (2017): “Research design meets market design: Using centralized assignment for impact evaluation,” Econometrica. Vol. 85, No. 5 (September, 2017), 13731432. 108CrossRefGoogle Scholar
Ackerberg, D., Benkard, C. L., Berry, S., and Pakes, A. (2007): “Econometric tools for analyzing market outcomes,” Handbook of Econometrics, 6, 41714276. 149CrossRefGoogle Scholar
Afriat, S. N. (1967): “The construction of utility functions from expenditure data,” International Economic Review, 8(1), 6777. 140CrossRefGoogle Scholar
Agranov, M., Healy, P. J., and Nielsen, K. (2023): “Stable randomisation,” The Economic Journal, 133, 25532579. 9CrossRefGoogle Scholar
Agranov, M., and Ortoleva, P. (2017): “Stochastic choice and preferences for randomization,” Journal of Political Economy, 125(1), 4068. 9, 60CrossRefGoogle Scholar
Agranov, M., and Ortoleva, P. (in press): “Ranges of preferences and randomization,” Review of Economics and Statistics. 60Google Scholar
Aguiar, V. H. (2017): “Random categorization and bounded rationality,” Economics Letters, 159, 4652. 152, 153CrossRefGoogle Scholar
Aguiar, V. H., Boccardi, M. J., and Dean, M. (2016): “Satisficing and stochastic choice,” Journal of Economic Theory, 166, 445482. 156CrossRefGoogle Scholar
Aguiar, V. H., Boccardi, M. J., Kashaev, N., and Kim, J. (2023): “Random utility and limited consideration,” Quantitative Economics, 14(1), 71116. 154CrossRefGoogle Scholar
Aguirregabiria, V., and Mira, P. (2010): “Dynamic discrete choice structural models: A survey,” Journal of Econometrics, 156(1), 3867. 158, 160CrossRefGoogle Scholar
Ahn, D., Iijima, R., Sarver, T., and Yaouanq, Y. L. (2019): “Behavioral characterizations of Naiveté for time-inconsistent preferences,” Review of Economic Studies, 86, 23192355. 119CrossRefGoogle Scholar
Ahn, D. S., Iijima, R., and Sarver, T. (2020): “Naivete about temptation and self-control: Foundations for recursive naive quasi-hyperbolic discounting,” Journal of Economic Theory, 189, 105087. 119CrossRefGoogle Scholar
Ahn, D. S., and Sarver, T. (2013): “Preference for flexibility and random choice,” Econometrica, 81(1), 341361. 55, 56, 117, 118, 119Google Scholar
Ahumada, A., and Ülkü, L. (2018): “Luce rule with limited consideration,” Mathematical Social Sciences, 93, 5256. 30CrossRefGoogle Scholar
Allais, M. (1953): “Le Comportment de 1’Homme Rational devant 1e Risque, Critique des Postulates et Axiomes de 1’Eco1e Americaine,” Econometrica, 21, 803815. 51Google Scholar
Allen, R., and Rehbeck, J. (2019): “Revealed stochastic choice with attributes,” Econometrica, 87(3), 10211054. 150CrossRefGoogle Scholar
Alós-Ferrer, C., Fehr, E., and Netzer, N. (2021): “Time will tell: Recovering preferences when choices are noisy,” Journal of Political Economy, 129(6), 18281877. 133CrossRefGoogle Scholar
Anderson, S., de Palma, A., and Thisse, J. (1992): Discrete Choice Theory of Product Differentiation. MIT Press, Cambridge, MA. 42, 147, 168, 174CrossRefGoogle Scholar
Angrist, J., Hull, P., Pathak, P. A., and Walters, C. (2017): “Leveraging lotteries for school value-added: Testing and estimation,” Quarterly Journal of Economics. Volume 132, Issue 2, 871919. 108CrossRefGoogle Scholar
Anscombe, F. J., and Aumann, R. J. (1963): “A definition of subjective probability,” The Annals of Mathematical Statistics, 34(1), 199205. 74CrossRefGoogle Scholar
Apesteguia, J., and Ballester, M. A. (2017a): “Stochastic representative agent,” Working Paper. 34Google Scholar
Apesteguia, J., and Ballester, M. A. (2017b): “Monotone stochastic choice models: The case of risk and time preferences,” Journal of Political Economy, 126(1), 74106. 59CrossRefGoogle Scholar
Apesteguia, J., Ballester, M. A., and Lu, J. (2017): “Single-crossing random utility models,” Econometrica. 43CrossRefGoogle Scholar
Apostol, T. M. (1969): Calculus, vol. 2. John Wiley & Sons, New York, 2nd edn. 144, 175Google Scholar
Arrow, K. J. (1959): “Rational choice functions and orderings,” Economica, 26(102), 121127. 5CrossRefGoogle Scholar
Arrow, K. J., Blackwell, D., and Girshick, M. (1949): “Bayes and minimax solutions of sequential decision problems,” Econometrica, 17, 213244. 125CrossRefGoogle Scholar
Audley, R. (1960): “A stochastic model for individual choice behavior,” Psychological Review, 67(1), 1. 132CrossRefGoogle ScholarPubMed
Aumann, R. J., and Savage, L. (1987): “Letter from Robert Aumann to Leonard Savage and Letter from Leonard Savage to Robert Aumann,” in Essays on Economic Decisions under Uncertainty, ed. by Drèze, J. H., pp. 7678. Cambridge University Press, Cambridge. 74Google Scholar
Auster, S., Che, Y.-K., and Mierendorff, K. (2022): “Prolonged learning and Hasty Stopping: The Wald Problem with Ambiguity,” 133Google Scholar
Baldassi, C., Cerreia-Vioglio, S., Maccheroni, F., Marinacci, M., and Pirazzini, M. (2020): “A behavioral characterization of the drift diffusion model and its multialternative extension for choice under time pressure,” Management Science, 66(11), 50755093. 134CrossRefGoogle Scholar
Ballinger, T. P., and Wilcox, N. T. (1997): “Decisions, error and heterogeneity,” The Economic Journal, 107(443), 10901105. 9, 61CrossRefGoogle Scholar
Bandyopadhyay, T., Dasgupta, I., and Pattanaik, P. K. (1999): “Stochastic revealed preference and the theory of demand,” Journal of Economic Theory, 84(1), 95110. 4CrossRefGoogle Scholar
Barberá, S., and Pattanaik, P. (1986): “Falmagne and the rationalizability of stochastic choices in terms of random orderings,” Econometrica, 54(3), 707715. 15, 26CrossRefGoogle Scholar
Barlow, H. B. (1961): “Possible principles underlying the transformation of sensory messages,” Sensory Communication, 1(01). 74Google Scholar
Barseghyan, L., Coughlin, M., Molinari, F., and Teitelbaum, J. C. (2021): “Heterogeneous choice sets and preferences,” Econometrica, 89(5), 20152048. 155CrossRefGoogle Scholar
Barseghyan, L., Molinari, F., O’Donoghue, T., and Teitelbaum, J. C. (2013): “The nature of risk preferences: Evidence from insurance choices,” American Economic Review, 103(6), 24992529. 61CrossRefGoogle Scholar
Barseghyan, L., Molinari, F., O’Donoghue, T., and Teitelbaum, J. C. (2018): “Estimating risk preferences in the field,” Journal of Economic Literature, 56(2), 501–64. 59CrossRefGoogle Scholar
Barseghyan, L., Molinari, F., and Thirkettle, M. (2021): “Discrete choice under risk with limited consideration,” American Economic Review, 111(6), 19722006. 155CrossRefGoogle Scholar
Becker, G. M., DeGroot, M. H., and Marschak, J. (1963): “Stochastic models of choice behavior,” Behavioral Science, 8(1), 4155. 52, 57, 59, 156CrossRefGoogle Scholar
Becker, G. M., DeGroot, M. H., and Marschak, J. (1964): “Measuring utility by a single-response sequential method,” Behavioral Science, 9(3), 226232. 52CrossRefGoogle ScholarPubMed
Becker, G. S., and Murphy, K. M. (1988): “A theory of rational addiction,” The Journal of Political Economy, pp. 675700. 105CrossRefGoogle Scholar
Ben-Akiva, M., and Boccara, B. (1995): “Discrete choice models with latent choice sets,” International Journal of Research in Marketing, 12(1), 924. 155CrossRefGoogle Scholar
Ben-Akiva, M., and Lerman, S. R. (1985): Discrete Choice Analysis. MIT Press, Cambridge, MA. 42Google Scholar
Bergemann, D., and Morris, S. (2016): “Bayes correlated equilibrium and the comparison of information structures in games,” Theoretical Economics, 11(2), 487522. 68CrossRefGoogle Scholar
Bergemann, D., and Välimäki, J. (2002): “Information acquisition and efficient mechanism design,” Econometrica, 70(3), 10071033. 84CrossRefGoogle Scholar
Berry, S., Levinsohn, J., and Pakes, A. (1995): “Automobile prices in market equilibrium,” Econometrica, 63(4), 841890. 142, 149CrossRefGoogle Scholar
Berry, S., Linton, O. B., and Pakes, A. (2004): “Limit theorems for estimating the parameters of differentiated product demand systems,” The Review of Economic Studies, 71(3), 613654. 148CrossRefGoogle Scholar
Berry, S., and Pakes, A. (2007): “The pure characteristics demand model,” International Economic Review, 48(4), 11931225. 147, 149CrossRefGoogle Scholar
Berry, S. T. (1994): “Estimating discrete-choice models of product differentiation,” The RAND Journal of Economics, 242262. Discussion paper number 15276. 140, 142, 149CrossRefGoogle Scholar
Berry, S. T., and Haile, P. A. (2009): “Nonparametric identification of multinomial choice demand models with heterogeneous consumers,” Discussion paper, National Bureau of Economic Research. 149Google Scholar
Berry, S. T., and Haile, P. A. (2014): “Identification in differentiated products markets using market level data,” Econometrica, 82(5), 17491797. 149Google Scholar
Berry, S. T., and Haile, P. A. (2021): “Foundations of demand estimation,” in Handbook of Industrial Organization, eds. Ho, K., Hortacsu, A., & Lizzeri, A., North-Holland, Elsevier. 145, 149Google Scholar
Blackwell, D. (1951): “Comparison of experiments, proceedings of the second berkeley symposium on mathematical statistics and probability,” ed. by Neyman, J. vol. 4, pp. 162. Elsevier, University of California Press Berkeley and Los Angeles. 64, 77, 84, 172Google Scholar
Blackwell, D. (1953): “Equivalent comparisons of experiments,” The Annals of Mathematical Statistics, 44(11), 265272. 172CrossRefGoogle Scholar
Blavatskyy, P. R. (2008): “Stochastic utility theorem,” Journal of Mathematical Economics, 44(11), 10491056. 57CrossRefGoogle Scholar
Block, D., and Marschak, J. (1960): “Random orderings and stochastic theories of responses,” in Contributions to Probability and Statistics, pp. 97132, ed. by Olkin, G., Hoeffding Stanford, M. University Press, Stanford. 12, 21, 23, 24, 26, 52Google Scholar
Bloedel, A. W., and Zhong, W. (2021): “The cost of optimally-acquired information,” mimeo. 91, 93Google Scholar
Blume, L. E. (2008): “Duality,” in The New Palgrave Dictionary of Economics. New York: Palgrave Macmillan. Available at: www.dictionaryofeconomics.com/article. 174Google Scholar
Bogacz, R., Brown, E., Moehlis, J., Holmes, P., and Cohen, J. D. (2006): “The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks,” Psychological Review, 113(4), 700. 7, 131, 133CrossRefGoogle ScholarPubMed
Bohnenblust, H. F., Shapley, L. S., and Sherman, S. (1949): Reconnaissance in Game Theory. Rand Corporation, Santa Monica, CA. 84, 172Google Scholar
Bordalo, P., Gennaioli, N., and Shleifer, A. (2012): “Salience theory of choice under risk,” Quarterly Journal of Economics, 127, 12431285. 8CrossRefGoogle Scholar
Bordalo, P., Gennaioli, N., and Shleifer, A. (2013): “Salience and consumer choice,” Journal of Political Economy, 121(5), 803843. 151CrossRefGoogle Scholar
Bordalo, P., Gennaioli, N., and Shleifer, A. (2020): “Memory, attention, and choice,” The Quarterly Journal of Economics, 1399, 13991442. 156CrossRefGoogle Scholar
Boyd, J. H., and Mellman, R. E. (1980): “The effect of fuel economy standards on the US automotive market: An hedonic demand analysis,” Transportation Research Part A: General, 14(5–6), 367378. 146CrossRefGoogle Scholar
Brady, R. L., and Rehbeck, J. (2016): “Menu-dependent stochastic feasibility,” Econometrica, 84(3), 12031223. 153CrossRefGoogle Scholar
Branco, F., Sun, M., and Villas-Boas, J. M. (2012): “Optimal search for product information,” Management Science, 58(11), 20372056. 133CrossRefGoogle Scholar
Brehm, J. W. (1956): “Postdecision changes in the desirability of alternatives,” The Journal of Abnormal and Social Psychology, 52(3), 384. 106CrossRefGoogle ScholarPubMed
Brown, S. D., and Heathcote, A. (2008): “The simplest complete model of choice response time: Linear ballistic accumulation,” Cognitive Psychology, 57(3), 153178. 133CrossRefGoogle ScholarPubMed
Brownstone, D., and Train, K. (1998): “Forecasting new product penetration with flexible substitution patterns,” Journal of Econometrics, 89(1–2), 109129. 146CrossRefGoogle Scholar
Bucher, S., and Brandenburger, A. (2021): “Divisive normalization is an efficient code for multivariate Pareto-distributed environments,” in 50th Annual Meeting of the Society for Neuroscience (Neuroscience 2021). 74Google Scholar
Buchholz, N., Doval, L., Kastl, J., Matějka, F., and Salz, T. (2020): “The value of time: Evidence from auctioned cab rides,” Discussion paper, National Bureau of Economic Research. 139Google Scholar
Busemeyer, J. R., and Townsend, J. T. (1993): “Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment,” Psychological Review, 100(3), 432. 133CrossRefGoogle Scholar
Calastri, C., Hess, S., Choudhury, C., Daly, A., and Gabrielli, L. (2019): “Mode choice with latent availability and consideration: Theory and a case study,” Transportation Research Part B: Methodological, 123, 374385. 155CrossRefGoogle Scholar
Callaway, F., Rangel, A., and Griffiths, T. L. (2020): “Fixation patterns in simple choice reflect optimal information sampling,” PLoS Computational Biology, 17(3), e1008863. 132Google Scholar
Camerer, C. F. (1989): “An experimental test of several generalized utility theories,” Journal of Risk and Uncertainty, 2(1), 61104. 9CrossRefGoogle Scholar
Campbell, J. Y., and Cochrane, J. H. (1999): “By force of habit: A consumption-based explanation of aggregate stock market behavior,” Journal of Political Economy, 107, 205251. 105CrossRefGoogle Scholar
Cantillo, V., and de Dios Ortúzar, J. (2005): “A semi-compensatory discrete choice model with explicit attribute thresholds of perception,” Transportation Research Part B: Methodological, 39(7), 641657. 155CrossRefGoogle Scholar
Caplin, A. (2016): “Measuring and modeling attention,” Annual Review of Economics, 8, 379403. 95CrossRefGoogle Scholar
Caplin, A., and Dean, M. (2013): “Behavioral implications of rational inattention with shannon entropy,” Discussion paper, National Bureau of Economic Research. 89Google Scholar
Caplin, A., and Dean, M. (2015): “Revealed preference, rational inattention, and costly information acquisition,” The American Economic Review, 105(7), 21832203. 93, 94, 95CrossRefGoogle Scholar
Caplin, A., Dean, M., and Leahy, J. (2017): “Rationally inattentive behavior: Characterizing and generalizing Shannon entropy,” Working Paper 23652, National Bureau of Economic Research. 94, 95Google Scholar
Caplin, A., Dean, M., and Leahy, J. (2019): “Rational inattention, optimal consideration sets, and stochastic choice,” The Review of Economic Studies, 86(3), 10611094. 151CrossRefGoogle Scholar
Caplin, A., Dean, M., and Leahy, J. (2022): “Rationally inattentive behavior: Characterizing and generalizing Shannon entropy,” Journal of Political Economy, 130(6), 000000. 90, 95CrossRefGoogle Scholar
Caplin, A., and Martin, D. (2015): “A testable theory of imperfect perception,” The Economic Journal, 125(582), 184202. 68CrossRefGoogle Scholar
Caplin, A., and Martin, D. (2016): “The dual-process drift diffusion model: Evidence from response times,” Economic Inquiry, 54(2), 12741282. 121CrossRefGoogle Scholar
Cardell, N. S., and Dunbar, F. C. (1980): “Measuring the societal impacts of automobile downsizing,” Transportation Research Part A: General, 14(5–6), 423434. 146CrossRefGoogle Scholar
Cascetta, E., and Papola, A. (2001): “Random utility models with implicit availability/perception of choice alternatives for the simulation of travel demand,” Transportation Research Part C: Emerging Technologies, 9(4), 249263. 155CrossRefGoogle Scholar
Cattaneo, M. D., Ma, X., Masatlioglu, Y., and Suleymanov, E. (2020): “A random attention model,” Journal of Political Economy, 128(7), 27962836. 153, 154CrossRefGoogle Scholar
Cerreia-Vioglio, S., Dillenberger, D., and Ortoleva, P. (2015): “Cautious expected utility and the certainty effect,” Econometrica, 83(2), 693728. 52CrossRefGoogle Scholar
Cerreia-Vioglio, S., Dillenberger, D., Ortoleva, P., and Riella, G. (2019): “Deliberately stochastic,” American Economic Review, 109(7), 2425–45. 60CrossRefGoogle Scholar
Cerreia-Vioglio, S., Maccheroni, F., Marinacci, M., and Rustichini, A. (2017): “Multinomial logit processes and preference discovery: Inside and outside the black box.” The Review of Economic Studies 90(3): 11551194. 30CrossRefGoogle Scholar
Chade, H., and Schlee, E. (2002): “Another look at the Radner–Stiglitz nonconcavity in the value of information,” Journal of Economic Theory, 107(2), 421452. 86CrossRefGoogle Scholar
Chamberlain, G. (1984): “Panel data,” Handbook of Econometrics, 2, 12471318. 157CrossRefGoogle Scholar
Chamberlain, G. (1993): “Feedback in panel data models,” Discussion paper, mimeo, Harvard University. 105Google Scholar
Chamberlain, G. (2010): “Binary response models for panel data: Identification and information,” Econometrica, 78(1), 159168. 157Google Scholar
Chambers, C. P., and Echenique, F. (2016): Revealed Preference Theory, vol. 56. Cambridge University Press, Cambridge. 25, 26Google Scholar
Chambers, C. P., Liu, C., and Rehbeck, J. (2020): “Costly information acquisition,” Journal of Economic Theory, 186, 104979. 95CrossRefGoogle Scholar
Chambers, C. P., Masatlioglu, Y., and Turansick, C. (2021): “Correlated choice,” arXiv preprint arXiv:2103.05084. 102, 105Google Scholar
Chambers, C. P., Masatlioglu, Y., and Turansick, C. (in press): “Correlated choice,” Theoretical Economics. 102, 103Google Scholar
Chang, H., Narita, Y., and Saito, K. (2022): “Approximating choice data by discrete choice models,” mimeo. 146Google Scholar
Che, Y.-K., and Mierendorff, K. (2019): “Optimal dynamic allocation of attention,” American Economic Review, 109(8), 29933029. 91, 135CrossRefGoogle Scholar
Chen, M. K. (2008): “Rationalization and cognitive dissonance: Do choices affect or reflect preferences?Cowles Foundation Discussion Paper No. 1669. 106Google Scholar
Chen, M. K., and Risen, J. L. (2010): “How choice affects and reflects preferences: Revisiting the free-choice paradigm,” Journal of Personality and Social Psychology, 99(4), 573. 106CrossRefGoogle ScholarPubMed
Chernoff, H. (1954): “Rational selection of decision functions,” Econometrica, 22(4), 422443. 5CrossRefGoogle Scholar
Chernoff, H. (1961): “Sequential tests for the mean of a normal distribution,” in Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 7991. University of California Press, Berkeley, CA. 130Google Scholar
Chew, S. H. (1983): “A generalization of the quasilinear mean with applications to the measurement of income inequality and decision theory resolving the Allais paradox,” Econometrica, 51(4), 10651092. 52Google Scholar
Chew, S. H., Epstein, L. G., and Segal, U. (1991): “Mixture symmetry and quadratic utility,” Econometrica, 59(1), 139163. 52CrossRefGoogle Scholar
Chiong, K., Shum, M., Webb, R., and Chen, R. (2018): “Split-second decision-making in the field: Response times in mobile advertising,” Available at SSRN. 132Google Scholar
Chiong, K. X., Galichon, A., and Shum, M. (2016): “Duality in dynamic discrete-choice models,” Quantitative Economics, 7(1), 83115. 142CrossRefGoogle Scholar
Cicchini, G. M., Anobile, G., and Burr, D. C. (2014): “Compressive mapping of number to space reflects dynamic encoding mechanisms, not static logarithmic transform,” Proceedings of the National Academy of Sciences, 111(21), 78677872. 72CrossRefGoogle Scholar
Clark, S. A. (1996): “The random utility model with an infinite choice space,” Economic Theory, 7(1), 179189. 25, 26CrossRefGoogle Scholar
Clithero, J. A., and Rangel, A. (2013): “Combining Response times and choice data using a neuroeconomic model of the decision process improves out-of-sample predictions,” mimeo. 127Google Scholar
Cohen, M., and Falmagne, J.-C. (1990): “Random utility representation of binary choice probabilities: A new class of necessary conditions,” Journal of Mathematical Psychology, 34(1), 8894. 41CrossRefGoogle Scholar
Cohen, M. A. (1980): “Random utility systems – The infinite case,” Journal of Mathematical Psychology, 22(1), 123. 13, 26CrossRefGoogle Scholar
Constantinides, G. M. (1990): “Habit formation: A resolution of the equity premium puzzle,” Journal of Political Economy, 98(3), 519543. 105CrossRefGoogle Scholar
Cooke, K. (2017): “Preference discovery and experimentation,” Theoretical Economics, 12(3), 13071348. 121CrossRefGoogle Scholar
Cover, T. M., and Thomas, J. A. (2006): Elements of Information Theory. John Wiley and Sons, New York, 2nd edn. 88, 89, 93Google Scholar
Crawford, G. S., Griffith, R., and Iaria, A. (2021): “A survey of preference estimation with unobserved choice set heterogeneity,” Journal of Econometrics, 222(1), 443. 154CrossRefGoogle Scholar
Crawford, G. S., and Shum, M. (2005): “Uncertainty and learning in pharmaceutical demand,” Econometrica, 73(4), 11371173. 106CrossRefGoogle Scholar
Dagsvik, J. K. (1995): “How large is the class of generalized extreme value random utility models?Journal of Mathematical Psychology, 39(1), 9098. 36CrossRefGoogle Scholar
Dagsvik, J. K. (2008): “Axiomatization of stochastic models for choice under uncertainty,” Mathematical Social Sciences, 55(3), 341370. 57CrossRefGoogle Scholar
Dagsvik, J. K. (2015): “Stochastic models for risky choices: A comparison of different axiomatizations,” Journal of Mathematical Economics, 60, 8188. 57CrossRefGoogle Scholar
Daly, A., and Zachary, S. (1975): “Commuters’ values of time,” LGORU Report T55, Reading. 146Google Scholar
Daly, A., and Zachary, S. (1979): “Improved multiple choice models,” in Identifying and Measuring the Determinants of Mode Choice, ed. by Hensher, D., and Dalvi, Q., pp. 335357. Teakfield, London. 142Google Scholar
van Damme, E. (1991): Stability and Perfection of Nash Equilibria. Springer, New York. 41CrossRefGoogle Scholar
van Damme, E., and Weibull, J. (2002): “Evolution in games with endogenous mistake probabilities,” Journal of Economic Theory, 106(2), 296315. 149CrossRefGoogle Scholar
Davidson, D., and Marschak, J. (1959): “Experimental tests of stochastic decision theory,” in Measurement Definitions and Theories, ed. by Churchman, C. W.. Wiley, John & Sons. 38Google Scholar
De Clippel, G., and Rozen, K. (2021): “Bounded rationality and limited data sets,” Theoretical Economics, 16(2), 359380. 21CrossRefGoogle Scholar
De Jong, G., Daly, A., Pieters, M., and Van der Hoorn, T. (2007): “The logsum as an evaluation measure: Review of the literature and new results,” Transportation Research Part A: Policy and Practice, 41(9), 874889. 31Google Scholar
de Oliveira, H. (2019): “Axiomatic foundations for entropic costs of attention,” Discussion paper, mimeo. 84Google Scholar
De Oliveira, H., Denti, T., Mihm, M., and Ozbek, M. K. (2016): “Rationally inattentive preferences and hidden information costs,” Theoretical Economics, 12(2), 214. 84, 95Google Scholar
De Palma, A., Ben-Akiva, M., Brownstone, D., Holt, C., Magnac, T., McFadden, D., Moffatt, P., Picard, N., Train, K., and Wakker, P. (2008): “Risk, uncertainty and discrete choice models,” Marketing Letters, 19(3–4), 269285. 61CrossRefGoogle Scholar
Dean, M., and Neligh, N. L. (2023): “Experimental tests of rational inattention,” Journal of Political Economy, 131(12), 34153461. 7, 83, 85, 95CrossRefGoogle Scholar
Debreu, G. (1958): “Stochastic choice and cardinal utility,” Econometrica, 26(3), 440444. 38, 169CrossRefGoogle Scholar
Debreu, G. (1960): “Review of RD Luce, individual choice behavior: A theoretical analysis,” American Economic Review, 50(1), 186188. 32Google Scholar
Dekel, E. (1986): “An axiomatic characterization of preferences under uncertainty: Weakening the independence axiom,” Journal of Economic Theory, 40(2), 304318. 52CrossRefGoogle Scholar
Dekel, E., Lipman, B., and Rustichini, A. (2001): “Representing preferences with a unique subjective state space,” Econometrica, 69(4), 891934. 116CrossRefGoogle Scholar
Dekel, E., Lipman, B. L., and Rustichini, A. (2009): “Temptation-driven preferences,” The Review of Economic Studies, 76(3), 937971. 162CrossRefGoogle Scholar
Dekel, E., Lipman, B. L., Rustichini, A., and Sarver, T. (2007): “Representing preferences with a unique subjective state space: A corrigendum,” Econometrica, 75(2), 591600. 116CrossRefGoogle Scholar
DellaVigna, S. (2018): “Structural Behavioral Economics,” in Handbook of Behavioral Economics: Applications and Foundations 1, vol. 1, pp. 613723, ed. by Douglas Bernheim, B., DellaVigna, Stefano, and David Laibson. Elsevier, Amsterdam. 61Google Scholar
Deming, D. J. (2011): “Better schools, less crime?The Quarterly Journal of Economics, 126(4), 20632115 qjr036. 108CrossRefGoogle Scholar
Deming, D. J., Hastings, J. S., Kane, T. J., and Staiger, D. O. (2014): “School choice, school quality, and postsecondary attainment,” The American Economic Review, 104(3), 9911013. 108CrossRefGoogle ScholarPubMed
Denti, T. (2022): “Posterior-separable cost of information,” Discussion paper. 69, 95Google Scholar
Denti, T. (2023): Private Communication. 95Google Scholar
Denti, T., Marinacci, M., and Rustichini, A. (2022a): “Experimental cost of information,” American Economic Review, 112, 31063123. 66, 91CrossRefGoogle Scholar
Denti, T., Marinacci, M., and Rustichini, A. (2022b): “The experimental order on random posteriors,” mimeo. 93Google Scholar
Dew, R., Ansari, A., and Li, Y. (2020): “Modeling dynamic heterogeneity using Gaussian processes,” Journal of Marketing Research, 57(1), 5577. 101CrossRefGoogle Scholar
Dillenberger, D., Lleras, J. S., Sadowski, P., and Takeoka, N. (2014): “A theory of subjective learning,” Journal of Economic Theory, 152, 287312. 117CrossRefGoogle Scholar
Doval, L. (2018): “Whether or not to open Pandora’s box,” Journal of Economic Theory, 175, 127158. 121CrossRefGoogle Scholar
Doya, K., Ishii, S., Pouget, A., and Rao, Rajesh P. N. (eds) (2007): Bayesian Brain: Probabilistic Approaches to Neural Coding. The MIT Press, Cambridge, MA.Google Scholar
Drugowitsch, J., Moreno-Bote, R., Churchland, A. K., Shadlen, M. N., and Pouget, A. (2012): “The cost of accumulating evidence in perceptual decision making,” The Journal of Neuroscience, 32(11), 36123628. 7, 130CrossRefGoogle ScholarPubMed
Dubé, J.-P., Hitsch, G. J., and Rossi, P. E. (2010): “State dependence and alternative explanations for consumer inertia,” The RAND Journal of Economics, 41(3), 417445. 101CrossRefGoogle Scholar
Duraj, J. (2018): “Dynamic random subjective expected utility,” Discussion paper.Google Scholar
Duraj, J., and Lin, Y.-H. (2019): “Costly information and random choice,” Discussion paper. 134CrossRefGoogle Scholar
Duraj, J., and Lin, Y.-H. (2021): “Identification and welfare evaluation in sequential sampling models,” Theory and Decision 92, 407431. 134CrossRefGoogle Scholar
Dwenger, N., Kübler, D., and Weizsäcker, G. (2018): “Flipping a coin: Evidence from university applications,” Journal of Public Economics, 167, 240250. 60CrossRefGoogle Scholar
Echenique, F., and Saito, K. (2019): “General Luce model,” Economic Theory, 68(4), 811826. 30CrossRefGoogle Scholar
Echenique, F., Saito, K., and Tserenjigmid, G. (2018): “The perception-adjusted Luce model,” Mathematical Social Sciences, 93, 6776. 156CrossRefGoogle Scholar
Edwards, W. (1965): “Optimal strategies for seeking information: Models for statistics, choice reaction times, and human information processing,” Journal of Mathematical Psychology, 2(2), 312329. 126CrossRefGoogle Scholar
Einav, L., Finkelstein, A., Ryan, S. P., Schrimpf, P., and Cullen, M. R. (2013): “Selection on moral hazard in health insurance,” American Economic Review, 103(1), 178219. 59CrossRefGoogle ScholarPubMed
Ellis, A. (2018): “Foundations for optimal inattention,” Journal of Economic Theory, 173, 5694. 95CrossRefGoogle Scholar
Enke, B., and Graeber, T. (2019): “Cognitive uncertainty,” Discussion paper, National Bureau of Economic Research. 81Google Scholar
Epstein, L. G., and Ji, S. (2020): “Optimal learning under robustness and time-consistency,” Operations Research, 70(3), 13171329. 133CrossRefGoogle Scholar
Epstein, L. G., and Zin, S. (1989): “Substitution, risk aversion, and the temporal behavior of consumption and asset returns: A theoretical framework,” Econometrica, 57(4), 937969. 119CrossRefGoogle Scholar
Erdem, T., and Keane, M. P. (1996): “Decision-making under uncertainty: Capturing dynamic brand choice processes in turbulent consumer goods markets,” Marketing Science, 15(1), 120. 106CrossRefGoogle Scholar
Ergin, H. (2003): “Costly contemplation,” mimeo. 84Google Scholar
Ergin, H., and Sarver, T. (2010): “A unique costly contemplation representation,” Econometrica, 78(4), 12851339. 84Google Scholar
Falmagne, J. (1978): “A representation theorem for finite random scale systems,” Journal of Mathematical Psychology, 18(1), 5272. 26, 27CrossRefGoogle Scholar
Falmagne, J.-C. (1983): “A random utility model for a belief function,” Synthese, 57(1), 3548. 14CrossRefGoogle Scholar
Fechner, Gustav, T. (1860): Elemente der psychophysik, 2 Vols. Breitkopf und Har̈tel, Leipzig. 7Google Scholar
Fehr, E., and Rangel, A. (2011): “Neuroeconomic foundations of economic choice – Recent advances,” The Journal of Economic Perspectives, 25(4), 330. 120CrossRefGoogle Scholar
Feldman, P., and Rehbeck, J. (2022): “Revealing a preference for mixtures: An experimental study of risk,” Quantitative Economics, 13(2), 761786. 57CrossRefGoogle Scholar
Feller, W. (1957): An Introduction to Probability Theory and Its Applications, vol. 2. John Wiley & Sons, New York, 2nd edn. 32, 127Google Scholar
Fenchel, W. (1953): Convex cones, sets, and functions. Lecture notes, Princeton University, Department of Mathematics. From notes taken by D. W. Blackett, Spring 1951. 144, 175Google Scholar
Fiorini, S. (2004): “A short proof of a theorem of Falmagne,” Journal of Mathematical Psychology, 48(1), 8082. 24, 26CrossRefGoogle Scholar
Fishburn, P. (1970): Utility Theory for Decision Making, John Wiley & Sons, Inc. New York. 74CrossRefGoogle Scholar
Fishburn, P. (1998): “Stochastic utility,” in Handbook of Utility Theory: Volume 1: Principles, p. 273, ed. by Barbera, Salvador, Hammond, Peter, and Seidl, Christian. Springer, Amsterdam. 27Google Scholar
Flynn, J. P., and Sastry, K. A. (2023): “Strategic mistakes,” Journal of Economic Theory, 212, 105704. 43CrossRefGoogle Scholar
Fosgerau, M., McFadden, D., and Bierlaire, M. (2013): “Choice probability generating functions,” Journal of Choice Modelling, 8, 118. 144, 174CrossRefGoogle Scholar
Fosgerau, M., Melo, E., De Palma, A., and Shum, M. (2020): “Discrete choice and rational inattention: A general equivalence result,” International Economic Review, 61(4), 15691589. 148CrossRefGoogle ScholarPubMed
Fox, J. T., K. il Kim, S. P. Ryan, and P. Bajari (2011): “A simple estimator for the distribution of random coefficients,” Quantitative Economics, 2(3), 381418. 146CrossRefGoogle Scholar
Fox, J. T., K. il Kim, S. P. Ryan, and P. Bajari (2012): “The random coefficients logit model is identified,” Journal of Econometrics, 166(2), 204212. 146CrossRefGoogle Scholar
Fox, J. T., K. il Kim, and C. Yang (2016): “A simple nonparametric approach to estimating the distribution of random coefficients in structural models,” Journal of Econometrics, 195(2), 236254. 146CrossRefGoogle Scholar
Frick, M. (2016): “Monotone threshold representations,” Theoretical Economics, 11(3), 757772. 37CrossRefGoogle Scholar
Frick, M., Iijima, R., and Strzalecki, T. (2019): “Dynamic random utility,” Econometrica, 87(6), 19412002. 56, 105, 110, 119, 163, 165CrossRefGoogle Scholar
Frydman, C., and Jin, L. J. (2022): “Efficient coding and risky choice,” The Quarterly Journal of Economics, 137(1), 161213. 81CrossRefGoogle Scholar
Fudenberg, D., Iijima, R., and Strzalecki, T. (2014): “Stochastic choice and revealed perturbed utility,” Working Paper version. 36, 43Google Scholar
Fudenberg, D., Iijima, R., and Strzalecki, T. (2015): “Stochastic choice and revealed perturbed utility,” Econometrica, 83(6), 23712409. 29, 43CrossRefGoogle Scholar
Fudenberg, D., and Levine, D. K. (1995): “Consistency and cautious fictitious play,” Journal of Economic Dynamics and Control, 19, 10651089. 41CrossRefGoogle Scholar
Fudenberg, D., and Levine, D. K. (1998): The Theory of Learning in Games. MIT Press, Cambridge, MA. 15Google Scholar
Fudenberg, D., Newey, W., Strack, P., and Strzalecki, T. (2020): “Testing the drift-diffusion model,” Proceedings of the National Academy of Sciences, 117(52), 33141–33148. 134CrossRefGoogle ScholarPubMed
Fudenberg, D., Strack, P., and Strzalecki, T. (2015): “Stochastic choice and optimal sequential sampling,” arXiv preprint arXiv:1505.03342. 127Google Scholar
Fudenberg, D., Strack, P., and Strzalecki, T. (2018): “Speed, accuracy, and the optimal timing of choices,” American Economic Review, 108, 36513684. 129, 130, 131, 135CrossRefGoogle Scholar
Fudenberg, D., and Strzalecki, T. (2015): “Dynamic logit with choice aversion,” Econometrica, 83(2), 651691. 161, 162, 165CrossRefGoogle Scholar
Gabaix, X., and Laibson, D. (2017): “Myopia and Discounting,” 81CrossRefGoogle Scholar
Lombardi, Gaia, Ernst Fehr, T. H. (2020): “Attentional foundations of framing effects,” mimeo. 132Google Scholar
Gentzkow, M., and Kamenica, E. (2014): “Costly persuasion,” American Economic Review: Papers & Proceedings, 104(5), 457–62. 91CrossRefGoogle Scholar
Gescheider, G. (1997): Psychophysics: The Fundamentals. L. Erlbaum Associates, Mahwah, NJ. 8, 71Google Scholar
Gibbard, P. (2021): “Disentangling preferences and limited attention: Random-utility models with consideration sets,” Journal of Mathematical Economics, 94, 102468. 154CrossRefGoogle Scholar
Gilboa, I. (1990): “A necessary but insufficient condition for the stochastic binary choice problem,” Journal of Mathematical Psychology, 34(4), 371392. 41CrossRefGoogle Scholar
Gilboa, I., and Pazgal, A. (2001): “Cumulative discrete choice,” Marketing Letters, 12(2), 119130. 105CrossRefGoogle Scholar
Gilboa, I., Postlewaite, A., and Samuelson, L. (2016): “Memorable consumption,” Journal of Economic Theory, 165, 414455. 106CrossRefGoogle Scholar
Gilboa, I., and Schmeidler, D. (1989): “Maxmin expected utility with non-unique prior,” Journal of Mathematical Economics, 18(2), 141153. 85CrossRefGoogle Scholar
Gittins, J., Glazebrook, K., and Weber, R. (2011): Multi-armed Bandit Allocation Indices. John Wiley & Sons, New York. 121CrossRefGoogle Scholar
Gittins, J. C. (1979): “Bandit processes and dynamic allocation indices,” Journal of the Royal Statistical Society: Series B (Methodological), 41(2), 148164. 121CrossRefGoogle Scholar
Goeree, M. S. (2008): “Limited information and advertising in the US personal computer industry,” Econometrica, 76(5), 10171074. 155Google Scholar
Gold, J. I., and Shadlen, M. N. (2007): “The neural basis of decision making,” Annual Review of Neuroscience, 30, 535574. 120CrossRefGoogle ScholarPubMed
Gonczarowski, Y. A., Kominers, S. D., and Shorrer, R. I. (2020): “Infinity and beyond: Scaling economic theories via logical compactness,” 26, 95Google Scholar
Gowrisankaran, G., and Rysman, M. (2012): “Dynamics of consumer demand for new durable goods,” mimeo. 159Google Scholar
Grandmont, J.-M. (1972): “Continuity properties of a von Neumann-Morgenstern utility,” Journal of Economic Theory, 4(1), 4557. 48CrossRefGoogle Scholar
Gravner, J. (2017): Lecture Notes for Introductory Probability. 174Google Scholar
Green, D. M., and Swets, J. A. (1966): Signal Detection Theory and Psychophysics, vol. 1. Wiley, New York. 71Google Scholar
Greenwald, A. G., McGhee, D. E., and Schwartz, J. L. (1998): “Measuring individual differences in implicit cognition: The implicit association test,” Journal of Personality and Social Psychology, 74(6), 1464. 120CrossRefGoogle ScholarPubMed
Griliches, Z. (1961): “Hedonic Price Indexes for Automobiles: An Econometric of Quality Change,” in The Price Statistics of the Federal Government, pp. 173196. NBER, Cambridge, MA. 139Google Scholar
Gul, F. (1991): “A theory of disappointment aversion,” Econometrica, 59(3), 667686. 52CrossRefGoogle Scholar
Gul, F., Natenzon, P., and Pesendorfer, W. (2014): “Random choice as behavioral optimization,” Econometrica, 82(5), 18731912. 29, 31, 34, 35, 36, 156, 162Google Scholar
Gul, F., and Pesendorfer, W. (2001): “Temptation and self-control,” Econometrica, 69(6), 14031435. 115, 162, 165CrossRefGoogle Scholar
Gul, F., and Pesendorfer, W. (2004): “Self-control and the theory of consumption,” Econometrica, 72(1), 119158. 109CrossRefGoogle Scholar
Gul, F., and Pesendorfer, W. (2006): “Random expected utility,” Econometrica, 74(1), 121146. 14, 53, 55, 56CrossRefGoogle Scholar
Gul, F., and Pesendorfer, W. (2007): “Harmful addiction,” The Review of Economic Studies, 74(1), 147172. 105CrossRefGoogle Scholar
Gul, F., and Pesendorfer, W. (2013): “Random utility maximization with indifference,” mimeo. 15Google Scholar
Handel, B. R. (2013): “Adverse selection and inertia in health insurance markets: When nudging hurts,” The American Economic Review, 103(7), 26432682. 59CrossRefGoogle ScholarPubMed
Hanes, D. P., and Schall, J. D. (1996): “Neural control of voluntary movement initiation,” Science, 274(5286), 427430. 73CrossRefGoogle ScholarPubMed
Hansen, L. P., and Sargent, T. J. (2008): Robustness. Princeton University Press, Princeton. 85Google Scholar
Harless, D. W., and Camerer, C. F. (1994): “The predictive utility of generalized expected utility theories,” Econometrica, 62(6), 12511289. 37, 61CrossRefGoogle Scholar
Harmon-Jones, E. E., and Mills, J. E. (1999): “Cognitive dissonance: Progress on a pivotal theory in social psychology,” in Scientific Conferences Program, 1997, U Texas, Arlington, TX, US; This volume is based on papers presented at a 2-day conference at the University of Texas at Arlington, Winter 1997. American Psychological Association. 106Google Scholar
Harsanyi, J. (1973a): “Games with randomly disturbed payoffs: A new rationale for mixed-strategy equilibrium points,” International Journal of Game Theory, 2(1), 123. 9CrossRefGoogle Scholar
Harsanyi, J. (1973b): “Oddness of the number of equilibrium points: A new proof,” International Journal of Game Theory, 2(1), 235250. 42CrossRefGoogle Scholar
Hauser, J. R., and Wernerfelt, B. (1990): “An evaluation cost model of consideration sets,” Journal of Consumer Research, 16(4), 393408. 151CrossRefGoogle Scholar
Hausman, J. A., and McFadden, D. (1984): “Specification tests for the multinomial logit model, Econometrica 52(5), 12191240. 30, 139CrossRefGoogle Scholar
Hausman, J. A., and Wise, D. A. (1978): “A conditional probit model for qualitative choice: Discrete decisions recognizing interdependence and heterogeneous preferences,” Econometrica, 46(2), 403426. 146CrossRefGoogle Scholar
He, J., and Natenzon, P. (2024): “Moderate utility,” American Economic Review: Insights, 6(2), 176195. 40, 41Google Scholar
Hébert, B., and Woodford, M. (2017): “Rational inattention and sequential information sampling,” Discussion paper, National Bureau of Economic Research. 134CrossRefGoogle Scholar
Hébert, B., and Woodford, M. (2021): “Neighborhood-based information costs,” American Economic Review, 111(10), 32253255. 93CrossRefGoogle Scholar
Heckman, J. J. (1981): “Heterogeneity and state dependence,” in Studies in Labor Markets, pp. 91140, ed. by Rosen, Sherwin. University of Chicago Press, Chicago. 105, 107Google Scholar
Heiss, F., McFadden, D., Winter, J., Wuppermann, A., and Zhou, B. (2016): “Inattention and switching costs as sources of inertia in medicare Part D,” Discussion paper, National Bureau of Economic Research. 155Google Scholar
Hendel, I., and Nevo, A. (2006): “Measuring the implications of sales and consumer inventory behavior,” Econometrica, 74(6), 16371673. 159CrossRefGoogle Scholar
Hey, J. D., and Carbone, E. (1995): “Stochastic choice with deterministic preferences: An experimental investigation,” Economics Letters, 47(2), 161167. 60CrossRefGoogle Scholar
Hey, J. D., and Orme, C. (1994): “Investigating generalizations of expected utility theory using experimental data,” Econometrica, 62(6), 12911326. 9, 61CrossRefGoogle Scholar
Ho, K., Hogan, J., and Scott Morton, F. (2017): “The impact of consumer inattention on insurer pricing in the Medicare Part D program,” The RAND Journal of Economics, 48(4), 877905. 155CrossRefGoogle Scholar
Ho, K., and Lee, R. S. (2017): “Insurer competition in health care markets,” Econometrica, 85(2), 379417. 59CrossRefGoogle Scholar
Ho, K., and Lee, R. S. (2020): “Health Insurance menu design for large employers,” Working Paper 27868, National Bureau of Economic Research. 59Google Scholar
Hofbauer, J., and Sandholm, W. (2002): “On the global convergence of stochastic fictitious play,” Econometrica, 70(6), 22652294. 15, 144, 149, 150CrossRefGoogle Scholar
Honoré, B. E., and Kyriazidou, E. (2000): “Panel data discrete choice models with lagged dependent variables,” Econometrica, 68(4), 839874. 105CrossRefGoogle Scholar
Honoré, B. E., and Tamer, E. (2006): “Bounds on parameters in panel dynamic discrete choice models,” Econometrica, 74(3), 611629. 161CrossRefGoogle Scholar
Horan, S. (2019): “Random consideration and choice: A case study of ‘default’ options,” Mathematical Social Sciences, 102, 7384. 152CrossRefGoogle Scholar
Horan, S. (2021): “Stochastic semi-orders,” Journal of Economic Theory, 192, 105171. 30CrossRefGoogle Scholar
Hortaçsu, A., Madanizadeh, S. A., and Puller, S. L. (2017): “Power to choose? An analysis of consumer inertia in the residential electricity market,” American Economic Journal: Economic Policy, 9(4), 192226. 155Google Scholar
Hotelling, H. (1929): “Stability in competition,” The Economic Journal, 39(153), 4157. 9, 147, 174CrossRefGoogle Scholar
Hotelling, H. (1932): “Edgeworth’s taxation paradox and the nature of demand and supply functions,” Journal of Political Economy, 40(5), 577616. 143CrossRefGoogle Scholar
Hotz, V. J., and Miller, R. A. (1993): “Conditional choice probabilities and the estimation of dynamic models,” The Review of Economic Studies, 60(3), 497529. 142, 159, 161CrossRefGoogle Scholar
Hotz, V. J., Miller, R. A., Sanders, S., and Smith, J. (1994): “A simulation estimator for dynamic models of discrete choice,” The Review of Economic Studies, 61(2), 265289. 159CrossRefGoogle Scholar
Hsiao, C. (2022): Analysis of Panel Data. Cambridge University Press, Cambridge. 157CrossRefGoogle Scholar
Hu, Y., and Shum, M. (2012): “Nonparametric identification of dynamic models with unobserved state variables,” Journal of Econometrics, 171(1), 3244. 160CrossRefGoogle Scholar
Huber, J., Payne, J. W., and Puto, C. (1982): “Adding asymmetrically dominated alternatives: Violations of regularity and the similarity hypothesis,” Journal of Consumer Research, 9, 9098. 22CrossRefGoogle Scholar
Huber, J., Payne, J. W., and Puto, C. P. (2014): “Let’s be honest about the attraction effect,” Journal of Marketing Research, 51(4), 520525. 22CrossRefGoogle Scholar
Hyogo, K. (2007): “A subjective model of experimentation,” Journal of Economic Theory, 133(1), 316330. 121CrossRefGoogle Scholar
Iyengar, S. S., and Lepper, M. R. (2000): “When choice is demotivating: Can one desire too much of a good thing?Journal of Personality and Social Psychology, 79(6), 9951006. 22CrossRefGoogle ScholarPubMed
Jaffe, S., and Weyl, E. G. (2010): “Linear demand systems are inconsistent with discrete choice,” The BE Journal of Theoretical Economics, 10(1) (Advances), Article 52. 141Google Scholar
Jazayeri, M., and Shadlen, M. N. (2010): “Temporal context calibrates interval timing,” Nature Neuroscience, 13(8), 1020. 81CrossRefGoogle ScholarPubMed
Jeuland, A. P. (1979): “Brand choice inertia as one aspect of the notion of brand loyalty,” Management Science, 25(7), 671682. 101CrossRefGoogle Scholar
Jones, M., and Dzhafarov, E. N. (2014): “Unfalsifiability and mutual translatability of major modeling schemes for choice reaction time,” Psychological Review, 121(1), 1. 127, 133CrossRefGoogle ScholarPubMed
Jung, C. G. (1910): “The association method,” The American Journal of Psychology, 21(2), 219269. 120CrossRefGoogle Scholar
Kahana, M. J. (2012): Foundations of Human Memory. Oxford University Press USA, New York. 156Google Scholar
Kahneman, D. (2011): Thinking, Fast and Slow. Farrar, Straus and Giroux, New York. 121Google Scholar
Kahneman, D., and Tversky, A. (1979): “Prospect theory: An analysis of decision under risk,” Econometrica, 47(2), 263291. 8, 51, 52CrossRefGoogle Scholar
Kallenberg, O. (2001): Foundations of Modern Probability. Springer, New York, 2nd edn. 63, 67Google Scholar
Kalouptsidi, M., Kitamura, Y., Lima, L., and Souza-Rodrigues, E. (2021): “Counterfactual analysis for structural dynamic discrete choice models,” Review of Economic Studies. 161Google Scholar
Kalouptsidi, M., Scott, P. T., and Souza-Rodrigues, E. (2021): “Identification of counterfactuals in dynamic discrete choice models,” Quantitative Economics, 12(2), 351403. 161CrossRefGoogle Scholar
Kamenica, E. (2008): “Contextual inference in markets: On the informational content of product lines,” American Economic Review, 98, 21272149. 81CrossRefGoogle Scholar
Karni, E., Schmeidler, D., and Vind, K. (1983): “On state dependent preferences and subjective probabilities,” Econometrica, 51(4), 10211031. 74CrossRefGoogle Scholar
Kasahara, H., and Shimotsu, K. (2009): “Nonparametric identification of finite mixture models of dynamic discrete choices,” Econometrica, 77(1), 135175. 161Google Scholar
Kashaev, N., and Aguiar, V. H. (2021): “A random attention and utility model,” arXiv preprint arXiv:2105.11268. 154Google Scholar
Kashaev, N., and Aguiar, V. H. (2022): “Random rank-dependent expected utility,” Games, 13(1), 13. 61CrossRefGoogle Scholar
Ke, S. (2018): “Rational expectation of mistakes and a measure of error-proneness,” Theoretical Economics, 13(2), 527552. 18, 162CrossRefGoogle Scholar
Ke, T., and Villas-Boas, M. (2016): “Optimal learning before choice,” mimeo. 135Google Scholar
Ke, T. T., Shen, Z.-J. M., and Villas-Boas, J. M. (2016): “Search for information on multiple products,” Management Science, 62(12), 35763603. 134CrossRefGoogle Scholar
Keane, M. P. (1997): “Modeling heterogeneity and state dependence in consumer choice behavior,” Journal of Business & Economic Statistics, 15(3), 310327. 101CrossRefGoogle Scholar
Keller, G., Rady, S., and Cripps, M. (2005): “Strategic experimentation with exponential bandits,” Econometrica, 73(1), 3968. 121CrossRefGoogle Scholar
Khan, S., Ouyang, F., and Tamer, E. (2021): “Inference on semiparametric multinomial response models,” Quantitative Economics, 12(3), 743777. 142CrossRefGoogle Scholar
Khaw, M. W., Li, Z., and Woodford, M. (2021): “Cognitive imprecision and small-stakes risk aversion,” The Review of Economic Studies, 88(4), 19792013. 73, 81, 124CrossRefGoogle Scholar
Kiani, R., and Shadlen, M. N. (2009): “Representation of confidence associated with a decision by neurons in the parietal cortex,” Science, 324(5928), 759764. 73CrossRefGoogle ScholarPubMed
Kitamura, Y., and Stoye, J. (2018): “Nonparametric analysis of random utility models,” Econometrica, 86(6), 18831909. 140CrossRefGoogle Scholar
Koning, R. H., and Ridder, G. (2003): “Discrete choice and stochastic utility maximization,” The Econometrics Journal, 6(1), 127. 28, 141, 145, 174CrossRefGoogle Scholar
Koopmans, T. C. (1964): “On the flexibility of future preferences,” in Human Judgments and Optimality, pp. 243254, ed. by Shelly, M. W., and Bryan, G. L.. Wiley, John and Sons, New York. 115Google Scholar
Kovach, M., and Suleymanov, E. (2021): “Reference dependence and random attention,” arXiv preprint arXiv:2106.13350. 153Google Scholar
Kovach, M., and Tserenjigmid, G. (2022a): “Behavioral foundations of nested stochastic choice and nested logit,” Journal of Political Economy, 130(9), 24112461. 36CrossRefGoogle Scholar
Kovach, M., and Tserenjigmid, G. (2022b): “The focal Luce model,” American Economic Journal: Microeconomics, 14(3), 378413. 156Google Scholar
Krajbich, I., Armel, C., and Rangel, A. (2010): “Visual fixations and the computation and comparison of value in simple choice,” Nature Neuroscience, 13(10), 12921298. 120, 124, 128, 132CrossRefGoogle ScholarPubMed
Krajbich, I., Lu, D., Camerer, C., and Rangel, A. (2012): “The attentional drift-diffusion model extends to simple purchasing decisions,” Frontiers in Psychology, 3, 193. 128, 132CrossRefGoogle ScholarPubMed
Krajbich, I., and Rangel, A. (2011): “Multialternative drift-diffusion model predicts the relationship between visual fixations and choice in value-based decisions,” Proceedings of the National Academy of Sciences, 108(33), 13852–13857. 132CrossRefGoogle ScholarPubMed
Kreps, D., and Porteus, E. (1978): “Temporal resolution of uncertainty and dynamic choice theory,” Econometrica, 46(1), 185200. 108, 119CrossRefGoogle Scholar
Kreps, D. M. (1979): “A representation theorem for ‘preference for flexibility’,” Econometrica, 565577. 115CrossRefGoogle Scholar
Kreps, D. M. (1988): Notes on the Theory of Choice, vol. 2. Westview Press, Boulder. 5, 6, 49Google Scholar
Lai, L., and Gershman, S. J. (2021): “Policy compression: An information bottleneck in action selection,” in Psychology of Learning and Motivation, vol. 74, pp. 195232, ed. by Kara, D. Federmeier. Elsevier, New York. 89CrossRefGoogle Scholar
Lancaster, K. J. (1966): “A new approach to consumer theory,” Journal of Political Economy, 74(2), 132157. 139CrossRefGoogle Scholar
Le Cam, L. (1996): “Comparison of experiments: A short review,” Lecture Notes-Monograph Series, 30, 127138. 172CrossRefGoogle Scholar
Lee, R. S. (2013): “Vertical integration and exclusivity in platform and two-sided markets,” American Economic Review, 103(7), 29603000. 159CrossRefGoogle Scholar
Lensman, T. A. (2023): Private Communication. 132Google Scholar
Li, R. (2022): “An dynamic random choice,” arXiv:2102.00143v2. 17, 102, 103Google Scholar
Liang, A., and Mu, X. (2020): “Complementary information and learning traps,” The Quarterly Journal of Economics, 135(1), 389448. 135CrossRefGoogle Scholar
Liang, A., Mu, X., and Syrgkanis, V. (2022): “Dynamically aggregating diverse information,” Econometrica, 90(1), 4780. 135CrossRefGoogle Scholar
Lin, Y.-H. (2018): “Random expected utility with revealed indifference in choice,” mimeo. 15Google Scholar
Lin, Y.-H. (2019a): “Random non-expected utility: Non-uniqueness,” mimeo. 61Google Scholar
Lin, Y.-H. (2019b): “Revealed ‘Betweenness’ preference over lotteries,” mimeo. 61Google Scholar
Lin, Y.-H. (2022): “Stochastic choice and rational inattention,” Journal of Economic Theory, 202, 105450. 95CrossRefGoogle Scholar
Lipman, B., and Pesendorfer, W. (2013): “Temptation,” in Advances in Economics and Econometrics: Tenth World Congress, Volume 1, pp. 243288, ed. by Acemoglu, Daron, Arellano, Manuel, and Dekel, Eddie. Cambridge University Press, Cambridge. 115CrossRefGoogle Scholar
Loève, M. (1978): “Review: Ju. V. Linnik and I. V. Ostrovskĭ, Decomposition of random variables and vectors,” Bulletin of the American Mathematical Society, 84(4), 638642. 32CrossRefGoogle Scholar
Loomes, G. (2005): “Modelling the stochastic component of behaviour in experiments: Some issues for the interpretation of data,” Experimental Economics, 8(4), 301323. 58CrossRefGoogle Scholar
Loomes, G., and Sugden, R. (1995): “Incorporating a stochastic element into decision theories,” European Economic Review, 39(3–4), 641648. 55, 57, 59CrossRefGoogle Scholar
Lu, J. (2016): “Random choice and private information,” Econometrica, 84(6), 19832027. 15, 76, 79, 95, 159CrossRefGoogle Scholar
Lu, J. (2019): “Bayesian identification: A theory for state-dependent utilities,” American Economic Review, 109(9), 31923228. 76CrossRefGoogle Scholar
Lu, J., and Saito, K. (2018): “Random intertemporal choice,” Journal of Economic Theory, 177, 780815. 119CrossRefGoogle Scholar
Lu, J., and Saito, K. (2019): “Repeated choice: A theory of stochastic intertemporal preferences,” mimeo. 56, 119Google Scholar
Lu, J., and Saito, K. (2022): “Mixed logit and pure characteristics models,” mimeo. 147Google Scholar
Luce, R. D. (1956): “Semiorders and a theory of utility discrimination,” Econometrica, 24(2), 178191. 37CrossRefGoogle Scholar
Luce, R. D. (1959): Individual Choice Behavior. John Wiley, New York. 29Google Scholar
Luce, R. D. (1986): Response Times. Oxford University Press, Oxford. 120, 133Google Scholar
Ma, W. (2018): “Random expected utility theory with a continuum of prizes,” Annals of Operations Research, 271(2), 787809. 56CrossRefGoogle Scholar
Ma, W. J., Kording, K. P., and Goldreich, D. (2022): Bayesian Models of Perception and Action. The MIT Press, Cambridge, MA.Google Scholar
Maccheroni, F., Marinacci, M., and Rustichini, A. (2006): “Ambiguity aversion, robustness, and the variational representation of preferences,” Econometrica, 74(6), 14471498. 85CrossRefGoogle Scholar
Machina, M. J. (1982): “‘Expected Utility’ analysis without the independence axiom,” Econometrica, 50(2), 277323. 52CrossRefGoogle Scholar
Machina, M. J. (1985): “Stochastic choice functions generated from deterministic preferences over lotteries,” The Economic Journal, 95(379), 575594. 59CrossRefGoogle Scholar
Mackowiak, B., Matejka, F., and Wiederholt, M. (2018): “Rational inattention: A disciplined behavioral model,” mimeo, Goethe University Frankfurt. 95Google Scholar
Magnac, T., and Thesmar, D. (2002): “Identifying dynamic discrete decision processes,” Econometrica, 70(2), 801816. 161CrossRefGoogle Scholar
Manski, C. F. (1977): “The structure of random utility models,” Theory and Decision, 8(3), 229254. 140, 152CrossRefGoogle Scholar
Manski, C. F. (1987): “Semiparametric analysis of random effects linear models from binary panel data,” Econometrica, 357–362. 157CrossRefGoogle Scholar
Manski, C. F. (1988): “Identification of binary response models,” Journal of the American Statistical Association, 83(403), 729738. 28, 142CrossRefGoogle Scholar
Manski, C. F. (1993): “Dynamic choice in social settings: Learning from the experiences of others,” Journal of Econometrics, 58(1–2), 121136. 160CrossRefGoogle Scholar
Manski, C. F. (2003): Partial Identification of Probability Distributions. Springer Science & Business Media, New York. 20Google Scholar
Manski, C. F. (2007): “Partial identification of counterfactual choice probabilities,” International Economic Review, 48(4), 13931410. 20CrossRefGoogle Scholar
Manzini, P., and Mariotti, M. (2014): “Stochastic choice and consideration sets,” Econometrica, 82(3), 11531176. 29, 152, 153Google Scholar
Manzini, P., and Mariotti, M. (2018): ‘Dual random utility maximisation,Journal of Economic Theory, 177, 162182. 156CrossRefGoogle Scholar
Manzini, P., Mariotti, M., and Petri, H. (2019): “Corrigendum to ‘Dual random utility maximisation’ [J. Econ. Theory 177 (2018) 162–182],” Journal of Economic Theory, 184, 104944. 156CrossRefGoogle Scholar
Marley, A. (1989): “A random utility family that includes many of the ‘classical’ models and has closed form choice probabilities and choice reaction times,” British Journal of Mathematical and Statistical Psychology, 42(1), 1336. 133CrossRefGoogle Scholar
Marley, A. (1990): “A historical and contemporary perspective on random scale representations of choice probabilities and reaction times,” Journal of Mathematical Psychology, 34(1), 8187. 41CrossRefGoogle Scholar
Marley, A. (1997): “Probabilistic choice as a consequence of nonlinear (sub) optimization,” Journal of Mathematical Psychology, 41(4), 382391. 61CrossRefGoogle ScholarPubMed
Marley, A. A. J., and Colonius, H. (1992): “The ‘horse race’ random utility model for choice probabilities and reaction times, and its compering risks interpretation,” Journal of Mathematical Psychology, 36(1), 120. 133CrossRefGoogle Scholar
Marschak, J. (1959): “Binary choice constraints on random utility indicators,” Cowles Foundation Discussion Papers 74, Cowles Foundation for Research in Economics, Yale University. 37, 41Google Scholar
Martins, A., and Astudillo, R. (2016): “From softmax to sparsemax: A sparse model of attention and multi-label classification,” in International Conference on Machine Learning, pp. 16141623. PMLR. 42Google Scholar
Mas-Colell, A., Whinston, M. D., Green, J. R., et al. (1995): Microeconomic Theory, vol. 1. Oxford University Press, New York. 5Google Scholar
Masatlioglu, Y., Nakajima, D., and Ozbay, E. Y. (2012): “Revealed attention,” The American Economic Review, 102(5), 21832205. 151CrossRefGoogle Scholar
Matejka, F., and McKay, A. (2015): “Rational inattention to discrete choices: A new foundation for the multinomial logit model,” The American Economic Review, 105(1), 272298. 89, 95CrossRefGoogle Scholar
Mattsson, L.-G., and Weibull, J. W. (2002): “Probabilistic choice and procedurally bounded rationality,” Games and Economic Behavior, 41, 6178. 149CrossRefGoogle Scholar
Matzkin, R. L. (1992): “Nonparametric and distribution-free estimation of the binary threshold crossing and the binary choice models,” Econometrica, 60(2), 239270. 142CrossRefGoogle Scholar
Matzkin, R. L. (1993): “Nonparametric identification and estimation of polychotomous choice models,” Journal of Econometrics, 58(1–2), 137168. 142CrossRefGoogle Scholar
Matzkin, R. L. (2013): “Nonparametric identification in structural economic models,” Annual Review of Economics, 5(1), 457486. 142CrossRefGoogle Scholar
McAlister, L. (1982): “A dynamic attribute satiation model of variety-seeking behavior,” Journal of Consumer Research, 9(2), 141150. 106CrossRefGoogle Scholar
McClellon, M. (2015): “Unique random utility representations,” Discussion paper. 27Google Scholar
McFadden, D. (1973): “Conditional logit analysis of qualitative choice behavior,” in Frontiers in Econometrics, pp. 105142 ed. by Zarembka, P.. Institute of Urban and Regional Development, University of California, New York. 15, 17, 18, 139, 141Google Scholar
McClellon, M. (1974): “The measurement of urban travel demand,” Journal of Public Economics, 3(4), 303328. 7Google Scholar
McClellon, M. (1975): “On independence, structure, and simultaneity in transportation demand analysis,” Discussion paper. 142Google Scholar
McClellon, M. (1978): ‘Modeling the choice of residential location’, in Karlqvist, A., Lundqvist, L., Snickars, F., and Weibull, J., eds., Spatial Interaction Theory and Planning Models, North-Holland, Amsterdam, pp. 7596. 35Google Scholar
McClellon, M. (1981): “Econometric models of probabilistic choice,” in Structural Analysis of Discrete Data, pp. 198272 ed. by Manski, C., and McFadden, D.. MIT Press, Cambridge, Massachusetts. 35, 36, 140, 142, 144, 174Google Scholar
McFadden, D., and Richter, M. (1971): “On the extension of a set function on a set of events to a probability on the generated Boolean σ-algebra,” Working Paper, University of California, Berkeley. 25, 26Google Scholar
McFadden, D., and Richter, M. (1990): “Stochastic rationality and revealed stochastic preference,” in Preferences, Uncertainty, and Optimality, Essays in Honor of Leo Hurwicz, pp. 161186, ed. by Chipman, J. S., McFadden, D., and Richter, M. K., pp. 161186. Westview Press Inc., Boulder, CO. 25, 26Google Scholar
McFadden, D., and Train, K. (2000): “Mixed MNL models for discrete response,” Journal of Applied Econometrics, 15, 447470. 34, 1463.0.CO;2-1>CrossRefGoogle Scholar
McFadden, D. L. (2005): “Revealed stochastic preference: A synthesis,” Economic Theory, 26(2), 245264. 26, 140CrossRefGoogle Scholar
Melkonyan, T., and Safra, Z. (2016): “Intrinsic variability in group and individual decision making,” Management Science, 62(9), 26512667. 61CrossRefGoogle Scholar
Mellers, B. A., and Biagini, K. (1994): “Similarity and choice,” Psychological Review, 101(3), 505. 58CrossRefGoogle Scholar
Mensch, J. (2018): “Cardinal representations of information,” Available at SSRN 3148954. 91Google Scholar
Miao, J., and Xing, H. (2023): “Dynamic discrete choice under rational inattention,” Economic Theory, 77(3), 156. 135CrossRefGoogle Scholar
Miller, R. (1984): “Job matching and occupational choice,” The Journal of Political Economy, 92, 10861120. 108, 159CrossRefGoogle Scholar
Milosavljevic, M., Malmaud, J., Huth, A., Koch, C., and Rangel, A. (2010): “The drift diffusion model can account for value-based choice response times under high and low time pressure,” Judgement & Decision Making, 5, 437449. 128CrossRefGoogle Scholar
Monderer, D. (1992): “The stochastic choice problem: A game-theoretic approach,” Journal of Mathematical Psychology, 36(4), 547554. 26CrossRefGoogle Scholar
Morris, S., and Strack, P. (2019): “The Wald Problem and the equivalence of sequential sampling and static information costs,” mimeo. 93, 134Google Scholar
Mosteller, F., and Nogee, P. (1951): “An experimental measurement of utility,” Journal of Political Economy, 59(5), 371404. 9, 52CrossRefGoogle Scholar
Myerson, R. B. (1982): “Optimal coordination mechanisms in generalized principal–agent problems,” Journal of Mathematical Economics, 10(1), 6781. 68CrossRefGoogle Scholar
Natenzon, P. (2019): “Random choice and learning,” Journal of Political Economy, 127(1), 419457. 80CrossRefGoogle Scholar
Nevo, A. (2000): “A practitioner’s guide to estimation of random-coefficients logit models of demand,” Journal of Economics & Management Strategy, 9(4), 513548. 149Google Scholar
Newsome, W. T., Britten, K. H., and Movshon, J. A. (1989): “Neuronal correlates of a perceptual decision,” Nature, 341(6237), 5254. 7CrossRefGoogle ScholarPubMed
Nocke, V., and Schutz, N. (2017): “Quasi-linear integrability,” Journal of Economic Theory, 169, 603628. 175CrossRefGoogle Scholar
Norets, A. (2009): “Inference in dynamic discrete choice models with serially orrelated unobserved state variables,” Econometrica, 77(5), 16651682. 160Google Scholar
Norets, A., and Tang, X. (2013): “Semiparametric Inference in dynamic binary choice models,” The Review of Economic Studies, 81(3), 12291262. 161CrossRefGoogle Scholar
Ok, E. (2014): Elements of Order Theory. 6Google Scholar
Ok, E. A., and Tserenjigmid, G. (2022): “Indifference, indecisiveness, experimentation, and stochastic choice,” Theoretical Economics, 17(2), 651686. 6CrossRefGoogle Scholar
Osborne, M. J., and Rubinstein, A. (2020): Models in Microeconomic Theory. Open Book Publisher, Cambridge. 5Google Scholar
Oud, B., Krajbich, I., Miller, K., Cheong, J. H., Botvinick, M., and Fehr, E. (2016): “Irrational time allocation in decision-making,” Proceedings of the Royal Society B: Biological Sciences, 283(1822), 20151439. 124, 131CrossRefGoogle ScholarPubMed
Pakes, A. (1986): “Patents as options: Some estimates of the value of holding European patent stocks,” Econometrica, 54, 755784. 108, 160, 164CrossRefGoogle Scholar
Pakes, A. (2010): “Alternative models for moment inequalities,” Econometrica, 78(6), 17831822. 142Google Scholar
Pakes, A., Porter, J., Ho, K., and Ishii, J. (2015): “Moment inequalities and their application,” Econometrica, 83(1), 315334. 142CrossRefGoogle Scholar
Pakes, A., Porter, J., Shepard, M., and Calder-Wang, S. (2020): “Unobserved heterogeneity, state dependence, and health plan choices,” mimeo. 106Google Scholar
Pavan, A., Segal, I., and Toikka, J. (2014): “Dynamic mechanism design: A myersonian approach,” Econometrica, 82(2), 601653. 112Google Scholar
Persico, N. (2000): “Information acquisition in auctions,” Econometrica, 68(1), 135148. 84CrossRefGoogle Scholar
Petzschner, F. H., and Glasauer, S. (2011): “Iterative Bayesian estimation as an explanation for range and regression effects: A study on human path integration,” Journal of Neuroscience, 31(47), 17220–17229. 72CrossRefGoogle Scholar
Piermont, E., and Teper, R. (2018): “Disentangling strict and weak choice in random expected utility models,” 15Google Scholar
Piermont, E., and Teper, R. (2019): “Exploration and correlation,” Games and Economic Behavior, 116, 96104. 121CrossRefGoogle Scholar
Pike, A. (1966): “Stochastic models of choice behaviour: Response probabilities and latencies of finite Markov chain systems 1,” British Journal of Mathematical and Statistical Psychology, 19(1), 1532. 132CrossRefGoogle Scholar
Podczeck, K. (2010): “On existence of rich Fubini extensions,” Economic Theory, 45(1), 122. 57CrossRefGoogle Scholar
Polanía, R., Woodford, M., and Ruff, C. C. (2019): “Efficient coding of subjective value,” Nature Neuroscience, 22(1), 134142. 74CrossRefGoogle ScholarPubMed
Pollard, D. (2002): A User’s Guide to Measure Theoretic Probability, vol. 8. Cambridge University Press, Cambridge. 25Google Scholar
Pomatto, L., Strack, P., and Tamuz, O. (2023): “The cost of information,” American Economic Review, 113, 13601393. 92, 93CrossRefGoogle Scholar
Quiggin, J. (1982): “A theory of anticipated utility,” Journal of Economic Behavior & Organization, 3(4), 323343. 52CrossRefGoogle Scholar
Radner, R., and Stiglitz, J. (1984): “A nonconcavity in the value of information,” Bayesian Models in Economic Theory, 5(3), 3352. 86Google Scholar
Raiffa, H. (1968): Decision Analysis. Introductory Lectures on Choices Under Uncertainty. Addison-Wesley, Reading, MA. 107Google Scholar
Raiffa, H., and Schlaifer, R. (1961): Applied Statistical Decision Theory. Division of Research, Harvard Business School, Boston. 84Google Scholar
Rambachan, A. (2021): “Identifying prediction mistakes in observational data,” in press, The Quarterly Journal of Economics. 70, 71Google Scholar
Ratcliff, R. (1978): “A theory of memory retrieval,” Psychological Review, 85(2), 59. 126, 131CrossRefGoogle Scholar
Ratcliff, R., Cherian, A., and Segraves, M. (2003): “A comparison of macaque behavior and superior colliculus neuronal activity to predictions from models of two-choice decisions,” Journal of Neurophysiology, 90(3), 13921407. 73CrossRefGoogle ScholarPubMed
Ratcliff, R., and McKoon, G. (2008): “The diffusion decision model: Theory and data for two-choice decision tasks,” Neural Computation, 20(4), 873922. 120, 131CrossRefGoogle ScholarPubMed
Ratcliff, R., and Smith, P. L. (2004): “A comparison of sequential sampling models for two-choice reaction time,” Psychological Review, 111(2), 333. 131CrossRefGoogle ScholarPubMed
Regenwetter, M., Davis-Stober, C. P., Lim, S. H., Guo, Y., Popova, A., Zwilling, C., Cha, Y.-S., and Messner, W. (2014): “QTest: Quantitative testing of theories of binary choice,” Decision, 1(1), 2. 61CrossRefGoogle ScholarPubMed
Regenwetter, M., and Marley, A. (2001): “Random relations, random utilities, and random functions,” Journal of Mathematical Psychology, 45(6), 864912. 12CrossRefGoogle Scholar
Reutskaja, E., Nagel, R., Camerer, C. F., and Rangel, A. (2011): “Search dynamics in consumer choice under time pressure: An eye-tracking study,” The American Economic Review, 101(2), 900926. 120, 128CrossRefGoogle Scholar
Rieskamp, J., Busemeyer, J. R., and Mellers, B. A. (2006): “Extending the bounds of rationality: Evidence and theories of preferential choice,” Journal of Economic Literature, 44(3), 631661. 41CrossRefGoogle Scholar
Rockafellar, R. T. (1966): “Characterization of the subdifferentials of convex functions,” Pacific Journal of Mathematics, 17(3), 497510. 174CrossRefGoogle Scholar
Rockafellar, R. T. (1970): Convex Analysis, no. 28. Princeton University Press, Princeton. 42, 175CrossRefGoogle Scholar
Rockafellar, R. T., and Wets, R. J.-B. (2009): Variational Analysis, vol. 317. Springer Science & Business Media, New York. 175Google Scholar
Roe, R. M., Busemeyer, J. R., and Townsend, J. T. (2001): “Multialternative decision field theory: A dynamic connectionist model of decision making,” Psychological Review, 108(2), 370. 128CrossRefGoogle ScholarPubMed
Rosenthal, A. (1989): “A bounded-rationality approach to the study of noncooperative games,” International Journal of Game Theory, 18, 273292. 42CrossRefGoogle Scholar
Roy, R. (1947): “La distribution du revenu entre les divers biens,” Econometrica, 15(3), 205225. 143CrossRefGoogle Scholar
Rozen, K. (2010): “Foundations of intrinsic habit formation,” Econometrica, 78(4), 13411373. 105Google Scholar
Rubinstein, A. (2007): “Instinctive and cognitive reasoning: A study of response times,” The Economic Journal, 117(523), 12431259. 121CrossRefGoogle Scholar
Rubinstein, A., and Salant, Y. (2006): “A model of choice from lists,” Theoretical Economics, 1(1), 317. 156Google Scholar
Rudin, W. (1976): Principles of Mathematical Analysis (3rd edition), vol. 3. McGraw-hill, New York. 175Google Scholar
Rust, J. (1987): “Optimal replacement of GMC bus engines: An empirical model of Harold Zurcher,” Econometrica, 55(5) 9991033. 108, 159, 163CrossRefGoogle Scholar
Rust, J. (1989): “A dynamic programming model of retirement behavior,” in The Economics of Aging, ed. by Wise, D., pp. 359398. University of Chicago Press, Chicago. 159Google Scholar
Rust, J. (1994): “Structural estimation of Markov decision processes,” Handbook of Econometrics, 4, 30813143. 158, 160CrossRefGoogle Scholar
Rustichini, A., Conen, K. E., Cai, X., and Padoa-Schioppa, C. (2017): “Optimal coding and neuronal adaptation in economic decisions,” Nature Communications, 8(1), 114. 74CrossRefGoogle ScholarPubMed
Rustichini, A., and Siconolfi, P. (2014): “Dynamic theory of preferences: Habit formation and taste for variety,” Journal of Mathematical Economics, 55, 5568. 106CrossRefGoogle Scholar
Ryan, M. (2015): “A strict stochastic utility theorem,” Economics Bulletin, 35(4), 26642672. 57Google Scholar
Safonov, E. (2017): “Random choice with framing effects: A Bayesian model,” mimeo. 80, 81Google Scholar
Saito, K. (2018): “Axiomatizations of the mixed Logit model,” 146Google Scholar
Samuelson, P. A. (1938): “A note on the pure theory of consumer’s behaviour,” Economica, 5(17), 6171. 4CrossRefGoogle Scholar
Savage, L. J. (1972): The Foundations of Statistics. Courier Corporation. 74Google Scholar
Scott, D. (1964): “Measurement structures and linear inequalities,” Journal of Mathematical Psychology, 1(2), 233247. 38, 170CrossRefGoogle Scholar
Seetharaman, P. (2004): “Modeling multiple sources of state dependence in random utility models: A distributed lag approach,” Marketing Science, 23(2), 263271. 101CrossRefGoogle Scholar
Sen, A. K. (1971): “Choice functions and revealed preference,” The Review of Economic Studies, 38(3), 307317. 5CrossRefGoogle Scholar
Shadlen, M. N., Hanks, T. D., Churchland, A. K., Kiani, R., and Yang, T. (2006): “The speed and accuracy of a simple perceptual decision: A mathematical primer,” in Bayesian Brain: Probabilistic Approaches to Neural Coding, pp. 209237, ed. by Doya, Kenji, Ishii, Shin, Pouget, Alexandre, and Rajesh, P. N. Rao. The MIT Press Cambridge, MA. 120CrossRefGoogle Scholar
Shaked, M., and Shanthikumar, J. G. (2007): Stochastic Orders. Springer Science & Business Media, New York. 50CrossRefGoogle Scholar
Shi, X., Shum, M., and Song, W. (2018): “Estimating semi-parametric panel multinomial choice models using cyclic monotonicity,” Econometrica, 86(2), 737761. 143, 145CrossRefGoogle Scholar
Shum, M. (2016): Econometric Models for Industrial Organization, World Scientific, Singapore. 149, 159Google Scholar
Simon, H. A. (1956): “Rational choice and the structure of the environment,” Psychological Review, 63(2), 129. 156CrossRefGoogle ScholarPubMed
Simonson, I. (1989): “Choice based on reasons: The case of attraction and compromise effects,” Journal of Consumer Research, 16(2), 158174. 22CrossRefGoogle Scholar
Sims, C. A. (2003): “Implications of rational inattention,” Journal of Monetary Economics, 50(3), 665690. 84CrossRefGoogle Scholar
Sims, C. A. (2006): “Rational inattention: Beyond the linear-quadratic case,” The American Economic Review, 96(2), 158163. 84CrossRefGoogle Scholar
Sims, C. A. (2010): “Rational inattention and monetary economics,” in Handbook of Monetary Economics, vol. 3, pp. 155181, ed. By Benjamin, M. Friedman and Woodford, Michael. Elsevier, Amsterdam. 84CrossRefGoogle Scholar
Smeulders, B., Cherchye, L., and Rock, B. D. (2021): “Nonparametric analysis of random utility models: Computational tools for statistical testing,” Econometrica, 89(1). 140CrossRefGoogle Scholar
Smith, P. L. (1990): “A note on the distribution of response times for a random walk with Gaussian increments,” Journal of Mathematical Psychology, 34(4), 445459. 127CrossRefGoogle Scholar
Smith, P. L., and Vickers, D. (1988): “The accumulator model of two-choice discrimination,” Journal of Mathematical Psychology, 32(2), 135168. 133CrossRefGoogle Scholar
Smith, T. E. (1984): “A choice probability characterization of generalized extreme value models,” Applied Mathematics and Computation, 14(1), 3562. 145CrossRefGoogle Scholar
Soerensen, J. R.-V., and Fosgerau, M. (2020): “How McFadden met Rockafellar and learnt to do more with less,” Discussion paper. 142CrossRefGoogle Scholar
Sopher, B., and Narramore, J. M. (2000): “Stochastic choice and consistency in decision making under risk: An experimental study,” Theory and Decision, 48(4), 323350.CrossRefGoogle Scholar
Sprumont, Y. (2020): “The triangular inequalities are suȼ cient for regularity,” mimeo. 41Google Scholar
Stahl, D. O. (1990): “Entropy control costs and entropic equilibria,” International Journal of Game Theory, 19, 129138. 41CrossRefGoogle Scholar
Steiner, J., Stewart, C., and F. Matějka (2017): “Rational inattention dynamics: Inertia and delay in decision-making,” Econometrica, 85(2), 521553. 89, 134CrossRefGoogle Scholar
Stone, M. (1960): “Models for choice-reaction time,” Psychometrika, 25(3), 251260. 126CrossRefGoogle Scholar
Stoye, J. (2019): “Revealed stochastic preference: A one-paragraph proof and generalization,” Economics Letters, 177, 6668. 25, 26CrossRefGoogle Scholar
Strack, P., and Taubinsky, D. (2021): “Dynamic preference ‘Reversals’ and time inconsistency,” mimeo. 119Google Scholar
Strotz, R. H. (1955): “Myopia and inconsistency in dynamic utility maximization,” The Review of Economic Studies, 23(3), 165180. 18, 115, 165CrossRefGoogle Scholar
Suleymanov, E. (2023): Private Communication. 153Google Scholar
Sun, Y. (2006): “The exact law of large numbers via Fubini extension and characterization of insurable risks,” Journal of Economic Theory, 126(1), 3169. 57CrossRefGoogle Scholar
Swait, J. (2001): “Choice set generation within the generalized extreme value family of discrete choice models,” Transportation Research Part B: Methodological, 35(7), 643666. 155CrossRefGoogle Scholar
Swait, J., and Ben-Akiva, M. (1987): “Incorporating random constraints in discrete models of choice set generation,” Transportation Research Part B: Methodological, 21(2), 91102. 155CrossRefGoogle Scholar
Swensson, R. G. (1972): “The elusive tradeoff: Speed vs accuracy in visual discrimination tasks,” Perception & Psychophysics, 12(1), 1632. 120CrossRefGoogle Scholar
Swets, J. A. (1973): “The relative operating characteristic in psychology: A technique for isolating effects of response bias finds wide use in the study of perception and cognition,” Science, 182(4116), 9901000. 71CrossRefGoogle Scholar
Taber, C. R. (2000): “Semiparametric identification and heterogeneity in discrete choice dynamic programming models,” Journal of Econometrics, 96(2), 201229. 161, 164CrossRefGoogle Scholar
Tajima, S., Drugowitsch, J., Patel, N., and Pouget, A. (2019): “Optimal policy for multi-alternative decisions,” Nature Neuroscience, 22(9), 15031511. 130CrossRefGoogle ScholarPubMed
Tanner, W. P., and Swets, J. A. (1954): “A decision-making theory of visual detection,” Psychological Review, 61(6), 401. 71CrossRefGoogle ScholarPubMed
Terry, A., Marley, A., Barnwal, A., Wagenmakers, E.-J., Heathcote, A., and Brown, S. D. (2015): “Generalising the drift rate distribution for linear ballistic accumulators,” Journal of Mathematical Psychology, 68, 4958. 133CrossRefGoogle Scholar
Thurstone, L. (1927): “A law of comparative judgment,” Psychological Review, 34(4), 273. 7, 9, 17CrossRefGoogle Scholar
Todd, P. E., and Wolpin, K. I. (2006): “Assessing the impact of a school subsidy program in Mexico: Using a social experiment to validate a dynamic behavioral model of child schooling and fertility,” American Economic Review, 96(5), 13841417. 108CrossRefGoogle ScholarPubMed
Torgersen, E. (1991): Comparison of Statistical Experiments, no. 36. Cambridge University Press, Cambridge. 91, 172CrossRefGoogle Scholar
Train, K. (1986): Choice Analysis: Theory, Econometrics, and an Application to Automobile Demand, MIT Press, Cambridge, MA. 142Google Scholar
Train, K. (2009): Discrete Choice Methods with Simulation. Cambridge University Press, Cambridge, 2nd edn. 36, 168Google Scholar
Turansick, C. (2021): “Identification in the random utility model,” arXiv preprint arXiv:2102.05570. 27Google Scholar
Tversky, A. (1969): “Intransitivity of preferences,” Psychological Review, 76, 3148. 9CrossRefGoogle Scholar
Tversky, A. (1972a): “Choice by elimination,” Journal of Mathematical Psychology, 9, 341367. 156CrossRefGoogle Scholar
Tversky, A. (1972b): “Elimination by aspects: A theory of choice,” Psychological Review, 79(4), 281299. 156CrossRefGoogle Scholar
Tversky, A., and Kahneman, D. (1992): “Advances in prospect theory: Cumulative representation of uncertainty,” Journal of Risk and Uncertainty, 5(4), 297323. 52CrossRefGoogle Scholar
Tversky, A., and Russo, J. E. (1969): “Substitutability and similarity in binary choice,” Journal of Mathematical Psychology, 6, 112. 40CrossRefGoogle Scholar
Tversky, A., and Thaler, R. H. (1990): “Anomalies: Preference reversals,” Journal of Economic Perspectives, 4(2), 201211. 124CrossRefGoogle Scholar
Van Lint, J. H., and Wilson, R. M. (2001): A Course in Combinatorics. Cambridge University Press, Cambridge. 24CrossRefGoogle Scholar
Van Nierop, E., Bronnenberg, B., Paap, R., Wedel, M., and Franses, P. H. (2010): “Retrieving unobserved consideration sets from household panel data,” Journal of Marketing Research, 47(1), 6374. 155CrossRefGoogle Scholar
Van Zandt, T. (1996): “Hidden information acquisition and static choice,” Theory and Decision, 40(3), 235247. 95CrossRefGoogle Scholar
Verstynen, T., and Sabes, P. N. (2011): “How each movement changes the next: An experimental and theoretical study of fast adaptive priors in reaching,” Journal of Neuroscience, 31(27), 10050–10059. 72CrossRefGoogle ScholarPubMed
Vickers, D. (1970): “Evidence for an accumulator model of psychophysical discrimination,” Ergonomics, 13(1), 3758. 132CrossRefGoogle ScholarPubMed
Von Neumann, J., and Morgenstern, O. (1944): Theory of Games and Economic Behavior. Princeton University Press, Princeton, NJ. 49Google Scholar
Wald, A. (1947): “Foundations of a general theory of sequential decision functions,” Econometrica, 15(4), 279. 84CrossRefGoogle Scholar
Webb, R. (2019): “The (neural) dynamics of stochastic choice,” Management Science, 65(1), 230255. 133CrossRefGoogle Scholar
Weitzman, M. L. (1979): “Optimal search for the best alternative,” Econometrica: 47(3), 641654. 121CrossRefGoogle Scholar
Wen, C.-H., and Koppelman, F. S. (2001): “The generalized nested logit model,” Transportation Research Part B: Methodological, 35(7), 627641. 155CrossRefGoogle Scholar
Wilcox, N. T. (2008): “Stochastic models for binary discrete choice under risk: A critical primer and econometric comparison,” in Risk Aversion in Experiments, vol. 12, pp. 197292, ed. by James, C. Cox and Harrison, G. W.. Emerald Group Publishing Limited, Bingley, UK. 59CrossRefGoogle Scholar
Wilcox, N. T. (2011): “‘Stochastically more risk averse:’A contextual theory of stochastic discrete choice under risk,” Journal of Econometrics, 162(1), 89104. 59CrossRefGoogle Scholar
Williams, D. (1991): Probability with Martingales. Cambridge University Press, Cambridge. 73CrossRefGoogle Scholar
Williams, H. C. (1977): “On the formation of travel demand models and economic evaluation measures of user benefit,” Environment and Planning A, 9(3), 285344. 142CrossRefGoogle Scholar
Woodford, M. (2012): “Inattentive valuation and reference-dependent choice,” https://doi.org/10.7916/D8VD6XVK. 91, 93Google Scholar
Woodford, M. (2014): “An optimizing neuroeconomic model of discrete choice,” Working Paper, Columbia University. 134Google Scholar
Woodford, M. (2020): “Modeling imprecision in perception, valuation, and choice,” Annual Review of Economics, 12, 579601. 81CrossRefGoogle Scholar
Woodrow, H. (1933): “Weight-discrimination with a varying standard,” The American Journal of Psychology, 45(3), 391416. 8CrossRefGoogle Scholar
Yaari, M. E. (1987): “The dual theory of choice under risk,” Econometrica, 55(1), 95115. 52CrossRefGoogle Scholar
Yang, E., and Kopylov, I. (2023). “Random quasi-linear utility,” Journal of Economic Theory, 209, 105650. 174CrossRefGoogle Scholar
Yegane, E. (2021): “Stochastic choice with limited memory,” mimeo. 156Google Scholar
Zhong, W. (2022): “Optimal dynamic information acquisition,” Econometrica, 90(4), 15371582. 135CrossRefGoogle Scholar
Zwilling, C. E., Cavagnaro, D. R., Regenwetter, M., Lim, S. H., Fields, B., and Zhang, Y. (2019): “QTest 2.1: Quantitative testing of theories of binary choice using Bayesian inference,” Journal of Mathematical Psychology, 91, 176194. 61CrossRefGoogle Scholar

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  • Bibliography
  • Tomasz Strzalecki, Harvard University, Massachusetts
  • Book: Stochastic Choice Theory
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  • Chapter DOI: https://doi.org/10.1017/9781009512749.019
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