Crowdsourcing Exploration

Published Online:https://doi.org/10.1287/mnsc.2016.2697

References

  • Acemoglu D, Dahleh MA, Lobel I, Ozdaglar A (2011) Bayesian learning in social networks. Rev. Econom. Stud. 78(4):1201–1236.CrossrefGoogle Scholar
  • Alizamir S, de Véricourt F, Sun P (2013) Diagnostic accuracy under congestion. Management Sci. 59(1):157–171.LinkGoogle Scholar
  • Allon G, Bassamboo A, Gurvich I (2011) “We will be right with you”: Managing customer expectations with vague promises and cheap talk. Oper. Res. 59(6):1382–1394.LinkGoogle Scholar
  • Altman E (1999) Constrained Markov Decision Processes (CRC Press, Boca Raton, FL).Google Scholar
  • Anand KS, Pac MF, Veeraraghavan S (2011) Quality-speed conundrum: Trade-offs in customer-intensive services. Management Sci. 57(1):40–56.LinkGoogle Scholar
  • Balseiro SR, Feldman J, Mirrokni V, Muthukrishnan S (2014) Yield optimization of display advertising with ad exchange. Management Sci. 60(12):2886–2907.LinkGoogle Scholar
  • Banerjee AV (1992) A simple model of herd behavior. Quart. J. Econom. 107(3):797–817.CrossrefGoogle Scholar
  • Bellman R (1956) A problem in the sequential design of experiments. Sankhya: Indian J. Statist. 16(3/4):221–229.Google Scholar
  • Bergemann D, Välimäki J (1997) Market diffusion with two-sided learning. RAND J. Econom. 28(4):773–795.CrossrefGoogle Scholar
  • Bertsimas D, Mersereau A (2007) A learning approach for interactive marketing to a customer segment. Oper. Res. 55(6):1120–1135.LinkGoogle Scholar
  • Besbes O, Gur Y, Zeevi A (2014) Stochastic multi-armed-bandit problem with non-stationary rewards. Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, eds. Advances in Neural Information Processing Systems 27 (NIPS 2014), 199–207.Google Scholar
  • Bikhchandani S, Hirshleifer D, Welch I (1992) A theory of fads, fashion, custom, and cultural change as informational cascades. J. Political Econom. 100(5):992–1026.CrossrefGoogle Scholar
  • Bimpikis K, Drakopoulos K (2015) Disclosing information in strategic experimentation. Working paper, Stanford University, Stanford, CA.Google Scholar
  • Bimpikis K, Ehsani S, Mostagir M (2015) Designing dynamic contests. Working paper, Stanford University, Stanford, CA.CrossrefGoogle Scholar
  • Bose S, Orosel G, Ottaviani M, Vesterlund L (2006) Dynamic monopoly pricing and herding. RAND J. Econom. 37(4):910–928.CrossrefGoogle Scholar
  • Caro F, Gallien J (2007) Dynamic assortment with demand learning for seasonal consumer goods. Management Sci. 53(2):276–292.LinkGoogle Scholar
  • Che Y, Hörner J (2014) Optimal design for social learning. Working paper, Columbia University, New York.Google Scholar
  • Crawford VP, Sobel J (1982) Strategic information transmission. Econometrica 50(6):1431–1451.CrossrefGoogle Scholar
  • Debo L, Parlour C, Rajan U (2012) Signaling quality via queues. Management Sci. 58(5):876–891.LinkGoogle Scholar
  • DeGroot M (2005) Optimal Statistical Decisions (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Frazier P, Kempe D, Kleinberg J, Kleinberg R (2014) Incentivizing exploration. Technical report, Cornell University, Ithaca, NY.Google Scholar
  • Gittins J, Jones D (1974) A dynamic allocation index for the sequential design of experiments. Gani J, ed. Progress in Statistics (North-Holland, Amsterdam), 241–266.Google Scholar
  • Gittins J, Glazebrook K, Weber R (2011) Multi-Armed Bandit Allocation Indices (John Wiley & Sons, Hoboken, NJ).CrossrefGoogle Scholar
  • Glazebrook KD (1982) On the evaluation of suboptimal strategies for families of alternative bandit processes. J. Appl. Probab. 19(3):716–722.CrossrefGoogle Scholar
  • Ifrach B, Maglaras C, Scarsini M (2014) Bayesian social learning from consumer reviews. Working paper, Columbia University, New York.Google Scholar
  • Kamenica E, Gentzkow M (2011) Bayesian persuasion. Amer. Econom. Rev. 101(6):2590–2615.CrossrefGoogle Scholar
  • Kostami V, Rajagopalan S (2013) Speed–quality trade-offs in a dynamic model. Manufacturing Service Oper. Management 16(1):104–118.LinkGoogle Scholar
  • Kremer I, Mansour Y, Perry M (2014) Implementing the “wisdom of the crowd.” J. Political Econom. 122(5):988–1012.CrossrefGoogle Scholar
  • Lobel I, Sadler E (2016) Preferences, homophily, and social learning. Oper. Res. 64(3):564–584.LinkGoogle Scholar
  • Lobel I, Mani A, Reed J (2015) Learning via external sales networks. Working paper, New York University, New York.Google Scholar
  • Marinesi S, Girotra K (2013) Information acquisition through customer voting systems. Working paper, INSEAD, Fontainebleau, France.Google Scholar
  • Marschak J, Miyasawa K (1968) Economic comparability of information systems. Internat. Econom. Rev. 9(2):137–174.CrossrefGoogle Scholar
  • Papanastasiou Y, Savva N (2017) Dynamic pricing in the presence of social learning and strategic consumers. Management Sci. 63(4):919–939.LinkGoogle Scholar
  • Papanastasiou Y, Bakshi N, Savva N (2014) Scarcity strategies under quasi-Bayesian social learning. Working paper, London Business School, London.Google Scholar
  • Rayo L, Segal I (2010) Optimal information disclosure. J. Political Econom. 118(5):949–987.CrossrefGoogle Scholar
  • Swinney R (2011) Selling to strategic consumers when product value is uncertain: The value of matching supply and demand. Management Sci. 57(10):1737–1751.LinkGoogle Scholar
  • Veeraraghavan S, Debo L (2009) Joining longer queues: Information externalities in queue choice. Manufacturing Service Oper. Management 11(4):543–562.LinkGoogle Scholar
  • Ye S, Aydin G, Hu S (2015) Sponsored search marketing: Dynamic pricing and advertising for an online retailer. Management Sci. 61(6):1255–1274.LinkGoogle 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.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.