Engineering Social Learning: Information Design of Time-Locked Sales Campaigns for Online Platforms

References

• Acemoglu D, Makhdoumi A, Malekian A, Ozdaglar A (2019) Learning from reviews: The selection effect and the speed of learning. Working paper, Massachusetts Institute of Technology, Cambridge, MA.Google Scholar
• Alizamir S, de Véricourt F, Wang S (2020) Warning against recurring risks: An information design approach. Management Sci. 66(10):4612–4629.Link, Google Scholar
• Araman VF, Caldentey R (2016) Crowdvoting the timing of new product introduction. Working paper, American University of Beirut, Beirut, Lebanon.Google Scholar
• Banerjee AV (1992) A simple model of herd behavior. Quart. J. Econom. 107(3):797–817.Crossref, Google Scholar
• Bergemann D, Castro F, Weintraub G (2020) The scope of sequential screening with ex post participation constraints. J. Econom. Theory 188:105055.Crossref, Google Scholar
• Besbes O, Scarsini M (2018) On information distortions in online ratings. Oper. Res. 66(3):597–610.Link, Google Scholar
• Best J, Quigley D (2020) Persuasion for the long run. Preprint, submitted February 6, http://dx.doi.org/10.2139/ssrn.2908115.Google Scholar
• Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as informational cascades. J. Polit. Econom. 100(5):992–1026.Crossref, Google Scholar
• Bimpikis K, Papanastasiou Y (2019) Inducing exploration in service platforms. Hu M, ed. Sharing Economy: Making Supply Meet Demand, Springer Series in Supply Chain Management, vol. 6 (Springer International Publishing, Cham, Switzerland), 193–216.Crossref, Google Scholar
• Candogan O (2020) Information design in operations. Druehl C, Elmaghraby W, eds. Pushing the Boundaries: Frontiers in Impactful OR/OM Research, TutORials in Operations Research (INFORMS, Catonsville, MD), 176–201.Link, Google Scholar
• Chamley CP (2004) Rational Herds: Economic Models of Social Learning (Cambridge University Press, Cambridge, UK).Google Scholar
• Che YK, Hörner J (2018) Recommender systems as mechanisms for social learning. Quart. J. Econom. 133(2):871–925.Crossref, Google Scholar
• Courty P, Hao L (2000) Sequential screening. Rev. Econom. Stud. 67(4):697–717.Crossref, Google Scholar
• Crapis D, Ifrach B, Maglaras C, Scarsini M (2016) Monopoly pricing in the presence of social learning. Management Sci. 63(11):3586–3608.Link, Google Scholar
• Drakopoulos K, Jain S, Randhawa R (2021) Persuading customers to buy early: The value of personalized information provisioning. Management Sci. 67(2):828–853.Link, Google Scholar
• Ely J (2017) Beeps. Amer. Econom. Rev. 107(1):31–53.Crossref, Google Scholar
• Feldman P, Papanastasiou Y, Segev E (2018) Social learning and the design of new experience goods. Management Sci. 65(4):1502–1519.Link, Google Scholar
• Garg N, Johari R (2019) Designing optimal binary rating systems. Chaudhuri K, Sugiyama M, eds. 22nd Internat. Conf. Artificial Intelligence Statist. (PMLR) (Naha, Okinawa, Japan), 1930–1939.Google Scholar
• Gentzkow M, Kamenica E (2016) A Rothschild-Stiglitz approach to Bayesian persuasion. Amer. Econom. Rev. 106(5):597–601.Crossref, Google Scholar
• Hu M, Milner J, Wu J (2015) Liking and following and the newsvendor: Operations and marketing policies under social influence. Management Sci. 62(3):867–879.Link, Google Scholar
• Ifrach B, Maglaras C, Scarsini M, Zseleva A (2019) Bayesian social learning from consumer reviews. Oper. Res. 67(5):1209–1221.Link, Google Scholar
• Immorlica N, Mao J, Slivkins A, Wu ZS (2018) Incentivizing exploration with selective data disclosure. Preprint, submitted April 29, https://arxiv.org/abs/1811.06026.Google Scholar
• Immorlica N, Mao J, Slivkins A, Wu ZS (2019) Bayesian exploration with heterogeneous agents. Liu L, White R, eds. WWW’19 World Wide Web Conf. (Association for Computing Machinery, New York), 751–761.Google Scholar
• Johari R, Light B, Weintraub G (2019) Quality selection in two-sided markets: A constrained price discrimination approach. Preprint, submitted December 4, https://arxiv.org/abs/1912.02251.Google Scholar
• Kamenica E, Gentzkow M (2011) Bayesian persuasion. Amer. Econom. Rev. 101(6):2590–2615.Crossref, Google Scholar
• Kleinberg RD, Slivkins A (2017) Tutorial: Incentivizing and coordinating exploration. Babaioff M, Moulin H, eds. EC’17 Proc. 18th ACM Conf. Econom. Comput., Cambridge, MA (Association for Computing Machinery, New York).Google Scholar
• Kremer I, Mansour Y, Perry M (2014) Implementing the “wisdom of the crowd”. J. Polit. Econom. 122(5):988–1012.Crossref, Google Scholar
• Lingenbrink D, Iyer K (2018) Signaling in online retail: Efficacy of public signals. Working paper, Cornell University, Ithaca, NY.Google Scholar
• Özer Ö, Phillips R (2012) The Oxford Handbook of Pricing Management (Oxford University Press, Oxford, UK).Crossref, Google Scholar
• Papanastasiou Y, Savva N (2016) Dynamic pricing in the presence of social learning and strategic consumers. Management Sci. 63(4):919–939.Link, Google Scholar
• Papanastasiou Y, Bimpikis K, Savva N (2018) Crowdsourcing exploration. Management Sci. 64(4):1727–1746.Link, Google Scholar
• Renault J, Solan E, Vieille N (2017) Optimal dynamic information provision. Games Econom. Behav. 104:329–349.Crossref, Google Scholar
• Rothschild M, Stiglitz JE (1970) Increasing risk: I. A definition. J. Econom. Theory 2(3):225–243.Crossref, Google Scholar
• Talluri KT, Van Ryzin GJ (2006) The Theory and Practice of Revenue Management, International Series in Operations Research & Management Science, vol. 68 (Springer Science & Business Media, Boston).Google Scholar
• Veeraraghavan S, Debo L (2009) Joining longer queues: Information externalities in queue choice. Manufacturing Service Oper. Management 11(4):543–562.Link, Google Scholar
• Yu M, Debo L, Kapuscinski R (2015) Strategic waiting for consumer-generated quality information: Dynamic pricing of new experience goods. Management Sci. 62(2):410–435.Link, Google Scholar