IDEAS home Printed from https://ideas.repec.org/f/plu413.html
   My authors  Follow this author

Jay Lu

Personal Details

First Name:Jay
Middle Name:
Last Name:Lu
Suffix:
RePEc Short-ID:plu413
http://www.econ.ucla.edu/jay/
Terminal Degree:2014 Department of Economics; Princeton University (from RePEc Genealogy)

Affiliation

Department of Economics
University of California-Los Angeles (UCLA)

Los Angeles, California (United States)
http://www.econ.ucla.edu/
RePEc:edi:deuclus (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Annie Liang & Jay Lu & Xiaosheng Mu, 2021. "Algorithm Design: A Fairness-Accuracy Frontier," Papers 2112.09975, arXiv.org, revised Jul 2023.
  2. Jay Lu & Simon Board, 2015. "Information Provision and Consumer Search," 2015 Meeting Papers 1427, Society for Economic Dynamics.

Articles

  1. Lu, Jay, 2021. "Random ambiguity," Theoretical Economics, Econometric Society, vol. 16(2), May.
  2. Jay Lu, 2019. "Bayesian Identification: A Theory for State-Dependent Utilities," American Economic Review, American Economic Association, vol. 109(9), pages 3192-3228, September.
  3. Lu, Jay & Saito, Kota, 2018. "Random intertemporal choice," Journal of Economic Theory, Elsevier, vol. 177(C), pages 780-815.
  4. Simon Board & Jay Lu, 2018. "Competitive Information Disclosure in Search Markets," Journal of Political Economy, University of Chicago Press, vol. 126(5), pages 1965-2010.
  5. Jose Apesteguia & Miguel A. Ballester & Jay Lu, 2017. "Single‐Crossing Random Utility Models," Econometrica, Econometric Society, vol. 85, pages 661-674, March.
  6. Jay Lu, 2016. "Random Choice and Private Information," Econometrica, Econometric Society, vol. 84, pages 1983-2027, November.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Annie Liang & Jay Lu & Xiaosheng Mu, 2021. "Algorithm Design: A Fairness-Accuracy Frontier," Papers 2112.09975, arXiv.org, revised Jul 2023.

    Cited by:

    1. Marie Obidzinski & Yves Oytana, 2022. "Advisory algorithms and liability rules," Working Papers hal-04222291, HAL.
    2. Kai Hao Yang & Philipp Strack, 2023. "Privacy Preserving Signals," Cowles Foundation Discussion Papers 2379, Cowles Foundation for Research in Economics, Yale University.

  2. Jay Lu & Simon Board, 2015. "Information Provision and Consumer Search," 2015 Meeting Papers 1427, Society for Economic Dynamics.

    Cited by:

    1. Strulik, Holger, 2018. "I shouldn't eat this donut: Self-control, body weight, and health in a life cycle model," University of Göttingen Working Papers in Economics 360, University of Goettingen, Department of Economics.

Articles

  1. Jay Lu, 2019. "Bayesian Identification: A Theory for State-Dependent Utilities," American Economic Review, American Economic Association, vol. 109(9), pages 3192-3228, September.

    Cited by:

    1. Jonathan Libgober, 2021. "Identifying Wisdom (of the Crowd): A Regression Approach," Papers 2105.07097, arXiv.org, revised Apr 2023.
    2. Elias Tsakas, 2022. "Belief identification with state-dependent utilities," Papers 2203.10505, arXiv.org, revised Nov 2022.
    3. Wei Ma, 2023. "Random dual expected utility," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 75(2), pages 293-315, February.
    4. Elias Tsakas, 2021. "Identification of misreported beliefs," Papers 2112.12975, arXiv.org.
    5. Wei Wang & Huifu Xu, 2023. "Preference robust state-dependent distortion risk measure on act space and its application in optimal decision making," Computational Management Science, Springer, vol. 20(1), pages 1-51, December.
    6. Elias Tsakas, 2023. "Belief identification by proxy," Papers 2311.13394, arXiv.org.

  2. Lu, Jay & Saito, Kota, 2018. "Random intertemporal choice," Journal of Economic Theory, Elsevier, vol. 177(C), pages 780-815.

    Cited by:

    1. Haoge Chang & Yusuke Narita & Kota Saito, 2022. "Approximating Choice Data by Discrete Choice Models," Papers 2205.01882, arXiv.org, revised Dec 2023.
    2. Chambers, Christopher P. & Masatlioglu, Yusufcan & Turansick, Christopher, 0. "Correlated choice," Theoretical Economics, Econometric Society.
      • Christopher P. Chambers & Yusufcan Masatlioglu & Christopher Turansick, 2021. "Correlated Choice," Papers 2103.05084, arXiv.org, revised Mar 2023.
    3. Benjamin Enke & Thomas W. Graeber, 2021. "Cognitive Uncertainty in Intertemporal Choice," CESifo Working Paper Series 9472, CESifo.
    4. Li, Boyao, 2023. "Random utility models with status quo bias," Journal of Mathematical Economics, Elsevier, vol. 105(C).
    5. Benjamin Enke & Thomas Graeber & Ryan Oprea & Thomas W. Graeber, 2023. "Complexity and Hyperbolic Discounting," CESifo Working Paper Series 10861, CESifo.
    6. Benjamin Enke & Thomas Graeber & Ryan Oprea, 2023. "Complexity and Time," CESifo Working Paper Series 10327, CESifo.
    7. D. Pennesi, 2016. "Intertemporal discrete choice," Working Papers wp1061, Dipartimento Scienze Economiche, Universita' di Bologna.

  3. Simon Board & Jay Lu, 2018. "Competitive Information Disclosure in Search Markets," Journal of Political Economy, University of Chicago Press, vol. 126(5), pages 1965-2010.

    Cited by:

    1. Zhao, Ju & Qiu, Ju & Zhou, Yong-Wu & Hu, Xiao-Jian & Yang, Ai-Feng, 2020. "Quality disclosure in the presence of strategic consumers," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    2. Yong Chen & Li Liu & Chao Li & Yangfei Huang & Qiaojie Luo, 2023. "Information Disclosure Impacts Intention to Consume Man-Made Meat: Evidence from Urban Residents’ Intention to Man-Made Meat in China," IJERPH, MDPI, vol. 20(4), pages 1-17, February.
    3. Armstrong, Mark & Zhou, Jidong, 2021. "Consumer Information and the Limits to Competition," MPRA Paper 108395, University Library of Munich, Germany.
    4. Piotr Dworczak & Alessandro Pavan, 2022. "Preparing for the Worst but Hoping for the Best: Robust (Bayesian) Persuasion," Econometrica, Econometric Society, vol. 90(5), pages 2017-2051, September.
    5. de Pedraza, Pablo & Vollbracht, Ian, 2020. "The Semicircular Flow of the Data Economy and the Data Sharing Laffer curve," GLO Discussion Paper Series 515, Global Labor Organization (GLO).
    6. Inderst, Roman & Hoffmann, Florian & Ottaviani, Marco, 2022. "Persuasion Through Selective Disclosure: Implications for Marketing, Campaigning, and Privacy Regulation," CEPR Discussion Papers 16901, C.E.P.R. Discussion Papers.
    7. Seungjin Han, 2019. "General Competing Mechanisms with Frictions," Department of Economics Working Papers 2019-09, McMaster University.
    8. Zhou, Jidong, 2020. "Improved Information in Search Markets," MPRA Paper 100509, University Library of Munich, Germany.
    9. Mustafa Dogan & Ju Hu, 2022. "Consumer search and optimal information," RAND Journal of Economics, RAND Corporation, vol. 53(2), pages 386-403, June.
    10. Han, Seungjin, 2022. "General competing mechanism games with strategy-proof punishment," Journal of Mathematical Economics, Elsevier, vol. 102(C).
    11. Jidong Zhou, 2021. "Mixed Bundling in Oligopoly Markets," Cowles Foundation Discussion Papers 2270, Cowles Foundation for Research in Economics, Yale University.
    12. Au, Pak Hung & Kawai, Keiichi, 2020. "Competitive information disclosure by multiple senders," Games and Economic Behavior, Elsevier, vol. 119(C), pages 56-78.
    13. E. Carroni & L. Ferrari & S. Righi, 2018. "The Price of Discovering Your Needs Online," Working Papers wp1116, Dipartimento Scienze Economiche, Universita' di Bologna.
    14. Stefan Terstiege & Cédric Wasser, 2018. "Buyer-Optimal Robust Information Structures," CRC TR 224 Discussion Paper Series crctr224_2018_034, University of Bonn and University of Mannheim, Germany.
    15. Mark Whitmeyer, 2020. "Persuasion Produces the (Diamond) Paradox," Papers 2011.13900, arXiv.org, revised Apr 2021.
    16. Kemal Kivanc Akoz & Arseniy Samsonov, 2023. "Bargaining over information structures," Discussion Papers 2301, Budapest University of Technology and Economics, Quantitative Social and Management Sciences.
    17. Krishnamurthy Iyer & Haifeng Xu & You Zu, 2023. "Markov Persuasion Processes with Endogenous Agent Beliefs," Papers 2307.03181, arXiv.org, revised Jul 2023.
    18. He, Wei & Li, Jiangtao, 2023. "Competitive information disclosure in random search markets," Games and Economic Behavior, Elsevier, vol. 140(C), pages 132-153.
    19. Wu, Jiemai, 2020. "Non-competing persuaders," European Economic Review, Elsevier, vol. 127(C).
    20. Pak Hung Au & Keiichi Kawai, 2021. "Competitive disclosure of correlated information," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 72(3), pages 767-799, October.
    21. Pablo Pedraza & Ian Vollbracht, 2023. "General theory of data, artificial intelligence and governance," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    22. Teddy Mekonnen & Zeky Murra-Anton & Bobak Pakzad-Hurson, 2023. "Persuaded Search," Papers 2303.13409, arXiv.org, revised Oct 2023.
    23. Lang, Ruitian, 2019. "Try before you buy: A theory of dynamic information acquisition," Journal of Economic Theory, Elsevier, vol. 183(C), pages 1057-1093.

  4. Jose Apesteguia & Miguel A. Ballester & Jay Lu, 2017. "Single‐Crossing Random Utility Models," Econometrica, Econometric Society, vol. 85, pages 661-674, March.

    Cited by:

    1. Roy Allen & Pawel Dziewulski & John Rehbeck, 2019. "Revealed Statistical Consumer Theory," University of Western Ontario, Departmental Research Report Series 20195, University of Western Ontario, Department of Economics.
    2. Caliari, Daniele, 2023. "Rationality is not consistency," Discussion Papers, Research Unit: Economics of Change SP II 2023-304, WZB Berlin Social Science Center.
    3. Demirkan, Yusufcan & Kimya, Mert, 2020. "Hazard rate, stochastic choice and consideration sets," Journal of Mathematical Economics, Elsevier, vol. 87(C), pages 142-150.
    4. Andrew Caplin & Mark Dean & John Leahy, 2017. "Rationally Inattentive Behavior: Characterizing and Generalizing Shannon Entropy," NBER Working Papers 23652, National Bureau of Economic Research, Inc.
    5. Christopher Turansick, 2021. "Identification in the Random Utility Model," Papers 2102.05570, arXiv.org, revised May 2022.
    6. Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2017. "Dynamic Random Utility," Cowles Foundation Discussion Papers 2092, Cowles Foundation for Research in Economics, Yale University.
    7. Guo, Liang, 2021. "Contextual deliberation and the choice-valuation preference reversal," Journal of Economic Theory, Elsevier, vol. 195(C).
    8. Duffy, Sean & Gussman, Steven & Smith, John, 2021. "Visual judgments of length in the economics laboratory: Are there brains in stochastic choice?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 93(C).
    9. Victor H. Aguiar & Maria Jose Boccardi & Nail Kashaev & Jeongbin Kim, 2018. "Random Utility and Limited Consideration," Papers 1812.09619, arXiv.org, revised Jul 2022.
    10. Matheus Costa & Paulo Henrique Ramos & Gil Riella, 2020. "Single-crossing choice correspondences," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 54(1), pages 69-86, January.
    11. Carlos Alós-Ferrer & Ernst Fehr & Nick Netzer, 2018. "Time will tell: recovering preferences when choices are noisy," ECON - Working Papers 306, Department of Economics - University of Zurich, revised Jun 2020.
    12. Apesteguia, Jose & Ballester, Miguel A., 2023. "Random utility models with ordered types and domains," Journal of Economic Theory, Elsevier, vol. 211(C).
    13. Levon Barseghyan & Francesca Molinari & Matthew Thirkettle, 2020. "Discrete choice under risk with limited consideration," CeMMAP working papers CWP28/20, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    14. Kashaev, Nail & Aguiar, Victor H., 2022. "A random attention and utility model," Journal of Economic Theory, Elsevier, vol. 204(C).
    15. Manzini, Paola & Mariotti, Marco, 2018. "Dual random utility maximisation," Journal of Economic Theory, Elsevier, vol. 177(C), pages 162-182.
    16. Jose Apesteguia & Miguel A. Ballester, 2016. "Stochastic representatitve agent," Economics Working Papers 1536, Department of Economics and Business, Universitat Pompeu Fabra.
    17. Li, Boyao, 2023. "Random utility models with status quo bias," Journal of Mathematical Economics, Elsevier, vol. 105(C).
    18. Piermont, Evan, 2022. "Disentangling strict and weak choice in random expected utility models," Journal of Economic Theory, Elsevier, vol. 202(C).
    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. Heufer, Jan & van Bruggen, Paul & Yang, Jingni, 2020. "Giving According to Agreement," Other publications TiSEM 19e0d60e-efcb-4e7c-b163-f, Tilburg University, School of Economics and Management.
    21. Yaron Azrieli & John Rehbeck, 2022. "Marginal stochastic choice," Papers 2208.08492, arXiv.org.
    22. Efe A. Ok & Gerelt Tserenjigmid, 2023. "Measuring Stochastic Rationality," Papers 2303.08202, arXiv.org, revised Dec 2023.
    23. Yang, Erya & Kopylov, Igor, 2023. "Random quasi-linear utility," Journal of Economic Theory, Elsevier, vol. 209(C).
    24. Duffy, Sean & Gussman, Steven & Smith, John, 2019. "Judgments of length in the economics laboratory: Are there brains in choice?," MPRA Paper 93126, University Library of Munich, Germany.
    25. Natalia Lazzati & John K.-H. Quah & Koji Shirai, 2018. "Nonparametric analysis of monotone choice," Discussion Paper Series 184, School of Economics, Kwansei Gakuin University.
    26. Petri, Henrik, 2023. "Binary single-crossing random utility models," Games and Economic Behavior, Elsevier, vol. 138(C), pages 311-320.
    27. Lu, Jay & Saito, Kota, 2018. "Random intertemporal choice," Journal of Economic Theory, Elsevier, vol. 177(C), pages 780-815.
    28. D. Pennesi, 2016. "Intertemporal discrete choice," Working Papers wp1061, Dipartimento Scienze Economiche, Universita' di Bologna.
    29. D. Pennesi, 2016. "Deciding fast and slow," Working Papers wp1082, Dipartimento Scienze Economiche, Universita' di Bologna.

  5. Jay Lu, 2016. "Random Choice and Private Information," Econometrica, Econometric Society, vol. 84, pages 1983-2027, November.

    Cited by:

    1. Wei Ma, 2023. "Random dual expected utility," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 75(2), pages 293-315, February.
    2. Ismaël Rafaï & Sébastien Duchêne & Eric Guerci & Irina Basieva & Andrei Khrennikov, 2021. "The Triple-Store Experiment: A First Simultaneous Test of Classical and Quantum Probabilities in Choice over Menus," GREDEG Working Papers 2021-16, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    3. Christopher Turansick, 2021. "Identification in the Random Utility Model," Papers 2102.05570, arXiv.org, revised May 2022.
    4. Duffy, Sean & Gussman, Steven & Smith, John, 2021. "Visual judgments of length in the economics laboratory: Are there brains in stochastic choice?," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 93(C).
    5. Haoge Chang & Yusuke Narita & Kota Saito, 2022. "Approximating Choice Data by Discrete Choice Models," Papers 2205.01882, arXiv.org, revised Dec 2023.
    6. David Dillenberger & Philipp Sadowski, 2019. "Stable behavior and generalized partition," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 68(2), pages 285-302, September.
    7. Rehbeck, John, 2023. "Revealed Bayesian expected utility with limited data," Journal of Economic Behavior & Organization, Elsevier, vol. 207(C), pages 81-95.
    8. Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Aldo Rustichini, 2020. "Multinomial logit processes and preference discovery: inside and outside the black box," Papers 2004.13376, arXiv.org, revised Jan 2021.
    9. Ellis, Andrew, 2017. "Foundations for optimal inattention," LSE Research Online Documents on Economics 85334, London School of Economics and Political Science, LSE Library.
    10. Markus Eyting & Patrick Schmidt, 2019. "Belief Elicitation with Multiple Point Predictions," Working Papers 1818, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, revised 16 Nov 2020.
    11. Li, Boyao, 2023. "Random utility models with status quo bias," Journal of Mathematical Economics, Elsevier, vol. 105(C).
    12. Piermont, Evan, 2022. "Disentangling strict and weak choice in random expected utility models," Journal of Economic Theory, Elsevier, vol. 202(C).
    13. Tommaso Denti, 2022. "Posterior Separable Cost of Information," American Economic Review, American Economic Association, vol. 112(10), pages 3215-3259, October.
    14. Nick Saponara, 2018. "Bayesian optimism," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 66(2), pages 375-406, August.
    15. Cristina Gualdani & Shruti Sinha, 2023. "Identification in Discrete Choice Models with Imperfect Information," Working Papers 949, Queen Mary University of London, School of Economics and Finance.
    16. Lin, Yi-Hsuan, 2022. "Stochastic choice and rational inattention," Journal of Economic Theory, Elsevier, vol. 202(C).
    17. Edi Karni, 2020. "A mechanism for the elicitation of second-order belief and subjective information structure," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 69(1), pages 217-232, February.
    18. Yang, Erya & Kopylov, Igor, 2023. "Random quasi-linear utility," Journal of Economic Theory, Elsevier, vol. 209(C).
    19. Daniele Pennesi, 2020. "Identity and information acquisition," Carlo Alberto Notebooks 610, Collegio Carlo Alberto, revised 2021.
    20. Lang, Ruitian, 2019. "Try before you buy: A theory of dynamic information acquisition," Journal of Economic Theory, Elsevier, vol. 183(C), pages 1057-1093.
    21. Larry G. Epstein & Shaolin Ji, 2017. "Optimal Learning and Ellsberg’s Urns," Boston University - Department of Economics - Working Papers Series WP2017-010, Boston University - Department of Economics.
    22. 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.
    23. Jetlir Duraj & Yi-Hsuan Lin, 2022. "Costly information and random choice," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 74(1), pages 135-159, July.
    24. Lu, Jay & Saito, Kota, 2018. "Random intertemporal choice," Journal of Economic Theory, Elsevier, vol. 177(C), pages 780-815.
    25. Larry G. Epstein & Shaolin Ji, 2022. "Optimal Learning Under Robustness and Time-Consistency," Operations Research, INFORMS, vol. 70(3), pages 1317-1329, May.
    26. Youichiro Higashi & Kazuya Hyogo & Norio Takeoka, 2020. "Costly Subjective Learning," KIER Working Papers 1040, Kyoto University, Institute of Economic Research.
    27. Jetlir Duraj & Yi-Hsuan Lin, 2022. "Identification and welfare evaluation in sequential sampling models," Theory and Decision, Springer, vol. 92(2), pages 407-431, March.
    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.
    29. Andrew Ellis & Heidi Christina Thysen, 2021. "Subjective Causality in Choice," Papers 2106.05957, arXiv.org, revised Dec 2022.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Rankings

This author is among the top 5% authors according to these criteria:
  1. Number of Journal Pages, Weighted by Number of Authors and Simple Impact Factors
  2. Number of Journal Pages, Weighted by Number of Authors and Recursive Impact Factors

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 2 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-CMP: Computational Economics (1) 2022-01-24. Author is listed
  2. NEP-COM: Industrial Competition (1) 2015-12-01. Author is listed
  3. NEP-DGE: Dynamic General Equilibrium (1) 2015-12-01. Author is listed
  4. NEP-MKT: Marketing (1) 2015-12-01. Author is listed
  5. NEP-REG: Regulation (1) 2022-01-24. Author is listed

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Jay Lu should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.