Privacy, Voting, and the Wisdom of Crowds

Published Online:https://doi.org/10.1287/opre.2023.0671

References

  • Abernathy JR, Greenberg BG, Horvitz DG (1970) Estimates of induced abortion in urban North Carolina. Demography 7(1):19–29.CrossrefGoogle Scholar
  • Austen-Smith D, Banks JS (1996) Information aggregation, rationality, and the Condorcet Jury Theorem. Amer. Political Sci. Rev. 90(1):34–45.CrossrefGoogle Scholar
  • Blair G, Imai K, Zhou YY (2015) Design and analysis of the randomized response technique. J. Amer. Statist. Assoc. 110(511):1304–1319.CrossrefGoogle Scholar
  • Blume A, Lai EK, Lim W (2019) Eliciting private information with noise: The case of randomized response. Games Econom. Behav. 113:356–380.CrossrefGoogle Scholar
  • Boudreau L, Chassang S, González-Torres A, Heath R (2022) Monitoring harassment in organizations. Department of Economics, Princeton University. https://economics.princeton.edu/working-papers/monitoring-harassment-in-organizations/.Google Scholar
  • Bresman H, Edmondson AC (2022) Exploring the relationship between team diversity, psychological safety and team performance: Evidence from pharmaceutical drug development. Harvard Business School Working Paper, No. 22-055, Harvard Business School, Boston. Google Scholar
  • Chassang S, Zehnder C (2024) Secure survey design in organizations: Theory and experiments. Amer. Econom. J. Microeconomics 16(4):371–405.CrossrefGoogle Scholar
  • Chen X, Miao S, Wang Y (2022a) Differential privacy in personalized pricing with nonparametric demand models. Oper. Res. 71(2):581–602.LinkGoogle Scholar
  • Chen X, Simchi-Levi D, Wang Y (2022b) Privacy-preserving dynamic personalized pricing with demand learning. Management Sci. 68(7):4878–4898.LinkGoogle Scholar
  • Clemen RT, Winkler RL (1985) Limits for the precision and value of information from dependent sources. Oper. Res. 33(2):427–442.LinkGoogle Scholar
  • Crawford VP, Sobel J (1982) Strategic information transmission. Econometrica 50(6):1431–1451.CrossrefGoogle Scholar
  • Dwork C, Roth A (2014) The algorithmic foundations of differential privacy. Foundations Trends Theoret. Comput. Sci. 9(3–4):211–407.CrossrefGoogle Scholar
  • Edmondson AC, Lei Z (2014) Psychological safety: The history, renaissance, and future of an interpersonal construct. Annual Rev. Organ. Psych. Organ. Behav. 1(1):23–43.CrossrefGoogle Scholar
  • Fallah A, Makhdoumi A, Malekian A, Ozdaglar A (2022) Optimal and differentially private data acquisition: Central and local mechanisms. Preprint, submitted January 10, https://arxiv.org/abs/2201.03968.Google Scholar
  • Feddersen T, Pesendorfer W (1999) Elections, information aggregation, and strategic voting. Proc. Natl. Acad. Sci. USA 96(19):10572–10574.CrossrefGoogle Scholar
  • Gaba A, Popescu DG, Chen Z (2019) Assessing uncertainty from point forecasts. Management Sci. 65(1):90–106.LinkGoogle Scholar
  • Ghosh A, Roth A (2015) Selling privacy at auction. Games Econom. Behav. 91:334–346.CrossrefGoogle Scholar
  • Gneiting T, Raftery AE (2007) Strictly proper scoring rules, prediction, and estimation. J. Amer. Statist. Assoc. 102(477):359–378.CrossrefGoogle Scholar
  • Gonçalves C, Bessa RJ, Pinson P (2021) A critical overview of privacy-preserving approaches for collaborative forecasting. Internat. J. Forecast. 37(1):322–342.CrossrefGoogle Scholar
  • Goodwin P, Moritz B, Siemsen E (2018) Forecast decisions. Katok E, Leider S, Donohue K, eds. The Handbook of Behavioral Operations, chapter 12 (John Wiley & Sons, Ltd, Hoboken, NJ), 433–458.CrossrefGoogle Scholar
  • Hardesty L (2013) How Hard Is It to “De-Anonymize” Cellphone Data? (MIT News, Cambridge, MA).Google Scholar
  • Hu M, Momot R, Wang J (2022) Privacy management in service systems. Manufacturing Service Oper. Management 24(5):2387–2796.LinkGoogle Scholar
  • John LK, Blunden H, Liu H (2019) Shooting the messenger. J. Experiment. Psych. General. 148(4):644–666.CrossrefGoogle Scholar
  • Küçükgül C, Özer Ö, Wang S (2022) Recommender systems with privacy concerns. Preprint, submitted October 31, http://dx.doi.org/10.2139/ssrn.4138757.Google Scholar
  • Lei Y, Miao S, Momot R (2020) Privacy-preserving personalized revenue management. Preprint, submitted October 15, http://dx.doi.org/10.2139/ssrn.3704446.Google Scholar
  • Lichtendahl KC, Grushka-Cockayne Y, Pfeifer PE (2013) The wisdom of competitive crowds. Oper. Res. 61(6):1383–1398.LinkGoogle Scholar
  • Lubarsky B (2017) Re-identification of “anonymized” data. Georgetown Law Technology Review, Washington, DC.Google Scholar
  • Makridakis S, Spiliotis E, Assimakopoulos V (2022) The M5 competition: Background, organization, and implementation. Internat. J. Forecasting 38(4):1325–1336.CrossrefGoogle Scholar
  • Mannes AE, Soll JB, Larrick RP (2014) The wisdom of select crowds. J. Personality Soc. Psych. 107(2):276–299.CrossrefGoogle Scholar
  • Mello J (2009) The impact of sales forecast game playing on supply chains. Foresight Internat. J. Appl. Forecasting (13):13–22.Google Scholar
  • Nanayakkara P, Smart MA, Cummings R, Kaptchuk G, Redmiles E (2023) What are the chances? Explaining the epsilon parameter in differential privacy. Preprint, submitted March 1, https://arxiv.org/abs/2303.00738.Google Scholar
  • Narayanan A, Shmatikov V (2006) How to break anonymity of the Netflix prize dataset. Preprint, submitted October 18, https://arxiv.org/abs/cs/0610105.Google Scholar
  • Nissim K, Orlandi C, Smorodinsky R (2012) Privacy-aware mechanism design. Proc. 13th ACM Conf. Electronic Commerce, 774–789.Google Scholar
  • Ottaviani M, Sørensen PN (2001) Information aggregation in debate: Who should speak first? J. Public Econom. 81(3):393–421.CrossrefGoogle Scholar
  • Ottaviani M, Sørensen PN (2006) Reputational cheap talk. RAND J. Econom. 37(1):155–175.CrossrefGoogle Scholar
  • Özer Ö, Zheng Y, Chen KY (2011) Trust in forecast information sharing. Management Sci. 57(6):1111–1137.LinkGoogle Scholar
  • Palley AB, Soll JB (2019) Extracting the wisdom of crowds when information is shared. Management Sci. 65(5):2291–2309.AbstractGoogle Scholar
  • Piezunka H, Schilke O (2023) The dual function of organizational structure: Aggregating and shaping individuals’ votes. Organ. Sci. 34(5):1914–1937.LinkGoogle Scholar
  • Rueda M, Cobo B, Arcos A, Arnab R (2016) Software for randomized response techniques. Chaudhuri A, Christofides TC, Rao CR, eds. Handbook of Statistics, vol. 34 (Elsevier, Amsterdam), 155–167.Google Scholar
  • Surowiecki J (2004) The Wisdom of Crowds: Why the Many Are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations (Doubleday, New York).Google Scholar
  • Tong J, Feiler D (2017) A behavioral model of forecasting: Naive statistics on mental samples. Management Sci. 63(11):3609–3627.LinkGoogle Scholar
  • Wang X, Xu F, Zhang F (2021) Consumer privacy in online retail supply chains. Preprint, submitted August 31, http://dx.doi.org/10.2139/ssrn.3912642.Google Scholar
  • Warner SL (1965) Randomized response: A survey technique for eliminating evasive answer bias. J. Amer. Statist. Assoc. 60(309):63–69.CrossrefGoogle Scholar
  • Wired (2007) Why “anonymous” data sometimes isn’t. https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/.Google Scholar
  • Wolfers J, Zitzewitz E (2004) Prediction markets. J. Econom. Perspect. 18(2):107–126.CrossrefGoogle 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.