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Methods to Elicit Forecasts from Groups: Delphi and Prediction Markets Compared

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  • Kesten Green
  • J. Scott Armstrong
  • Andreas Graefe

Abstract

The Delphi technique is better than traditional group meetings for forecasting and has some advantages over another promising alternative to meetings, prediction markets. In this article, Kesten, Scott, and Andreas observe the increasing popularity of Delphi, describe the benefits of using this method to obtain forecasts from experts, compare it with prediction markets, and conclude that Delphi should be used more widely. Copyright International Institute of Forecasters, 2007

Suggested Citation

  • Kesten Green & J. Scott Armstrong & Andreas Graefe, 2007. "Methods to Elicit Forecasts from Groups: Delphi and Prediction Markets Compared," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 8, pages 17-20, Fall.
    • Handle: RePEc:for:ijafaa:y:2007:i:8:p:17-20
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    1. Kesten C. Green & J. Scott Armstrong, 2004. "Value of Expertise For Forecasting Decisions in Conflicts," Monash Econometrics and Business Statistics Working Papers 27/04, Monash University, Department of Econometrics and Business Statistics.
    2. Sandra Hoffmann & Paul Fischbeck & Alan Krupnick & Michael McWilliams, 2007. "Elicitation from Large, Heterogeneous Expert Panels: Using Multiple Uncertainty Measures to Characterize Information Quality for Decision Analysis," Decision Analysis, INFORMS, vol. 4(2), pages 91-109, June.
    3. Gene Rowe, 2007. "A Guide to Delphi," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 8, pages 11-16, Fall.
    4. Paul W. Rhode & Koleman S. Strumpf, 2004. "Historical Presidential Betting Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 127-141, Spring.
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    Cited by:

    1. Graefe, Andreas & Armstrong, J. Scott, 2011. "Comparing face-to-face meetings, nominal groups, Delphi and prediction markets on an estimation task," International Journal of Forecasting, Elsevier, vol. 27(1), pages 183-195, January.
    2. Robert J. MacCoun, 2010. "Comment on "Rethinking America's Illegal Drug Policy"," NBER Chapters, in: Controlling Crime: Strategies and Tradeoffs, pages 281-289, National Bureau of Economic Research, Inc.
    3. Milan Daus & Katharina Koberger & Kaan Koca & Felix Beckers & Jorge Encinas Fernández & Barbara Weisbrod & Daniel Dietrich & Sabine Ulrike Gerbersdorf & Rüdiger Glaser & Stefan Haun & Hilmar Hofmann &, 2021. "Interdisciplinary Reservoir Management—A Tool for Sustainable Water Resources Management," Sustainability, MDPI, vol. 13(8), pages 1-21, April.
    4. Angela Dalton & Alan Brothers & Stephen Walsh & Paul Whitney, 2010. "Expert Elicitation Method Selection Process and Method Comparison," Labsi Experimental Economics Laboratory University of Siena 030, University of Siena.
    5. Liu, Yaqin & Zhao, Guohao & Zhao, Yushan, 2016. "An analysis of Chinese provincial carbon dioxide emission efficiencies based on energy consumption structure," Energy Policy, Elsevier, vol. 96(C), pages 524-533.
    6. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    7. Joshua Becker & Abdullah Almaatouq & EmH{o}ke-'Agnes Horv'at, 2020. "Network Structures of Collective Intelligence: The Contingent Benefits of Group Discussion," Papers 2009.07202, arXiv.org, revised Mar 2021.
    8. Geoff Woolcott & Dan Chamberlain & Zachary Hawes & Michelle Drefs & Catherine D. Bruce & Brent Davis & Krista Francis & David Hallowell & Lynn McGarvey & Joan Moss & Joanne Mulligan & Yukari Okamoto &, 2020. "The central position of education in knowledge mobilization: insights from network analyses of spatial reasoning research across disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2323-2347, December.
    9. Kerr, Norbert L. & Tindale, R. Scott, 2011. "Group-based forecasting?: A social psychological analysis," International Journal of Forecasting, Elsevier, vol. 27(1), pages 14-40, January.
    10. Maria Jose Marques & Gudrun Schwilch & Nina Lauterburg & Stephen Crittenden & Mehreteab Tesfai & Jannes Stolte & Pandi Zdruli & Claudio Zucca & Thorunn Petursdottir & Niki Evelpidou & Anna Karkani & Y, 2016. "Multifaceted Impacts of Sustainable Land Management in Drylands: A Review," Sustainability, MDPI, vol. 8(2), pages 1-34, February.
    11. Kerr, Norbert L. & Tindale, R. Scott, 2011. "Group-based forecasting?: A social psychological analysis," International Journal of Forecasting, Elsevier, vol. 27(1), pages 14-40.
    12. Keyvanfar, Ali & Shafaghat, Arezou & Abd Majid, Muhd Zaimi & Bin Lamit, Hasanuddin & Warid Hussin, Mohd & Binti Ali, Kherun Nita & Dhafer Saad, Alshahri, 2014. "User satisfaction adaptive behaviors for assessing energy efficient building indoor cooling and lighting environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 277-295.
    13. Samir Mili & Maria Bouhaddane, 2021. "Forecasting Global Developments and Challenges in Olive Oil Supply and Demand: A Delphi Survey from Spain," Agriculture, MDPI, vol. 11(3), pages 1-25, February.
    14. Bloem da Silveira Junior, Luiz A. & Vasconcellos, Eduardo & Vasconcellos Guedes, Liliana & Guedes, Luis Fernando A. & Costa, Renato Machado, 2018. "Technology roadmapping: A methodological proposition to refine Delphi results," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 194-206.
    15. Ricardo Gomes & Alfeu Marques & Joaquim Sousa, 2013. "District Metered Areas Design Under Different Decision Makers’ Options: Cost Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(13), pages 4527-4543, October.
    16. Vicente Coll-Serrano & Salvador Carrasco-Arroyo & Olga Blasco-Blasco & Luis Vila-Lladosa, 2012. "Design of a Basic System of Indicators for Monitoring and Evaluating Spanish Cooperation’s Culture and Development Strategy," Evaluation Review, , vol. 36(4), pages 272-302, August.
    17. Palma, David & Dios Ortuzar, Juan de & Casaubon, Gerard & Rizzi, Luis I. & Agosin, Eduardo, 2013. "Measuring consumer preferences using hybrid discrete choice models," Working Papers 164855, American Association of Wine Economists.
    18. Shin, Dong-Hee, 2015. "Effect of the customer experience on satisfaction with smartphones: Assessing smart satisfaction index with partial least squares," Telecommunications Policy, Elsevier, vol. 39(8), pages 627-641.
    19. Lang, Mark & Bharadwaj, Neeraj & Di Benedetto, C. Anthony, 2016. "How crowdsourcing improves prediction of market-oriented outcomes," Journal of Business Research, Elsevier, vol. 69(10), pages 4168-4176.
    20. Robert Reig & Ramona Schoder, 2010. "Forecasting Accuracy: Comparing Prediction Markets And Surveys – An Experimental Study," Journal of Prediction Markets, University of Buckingham Press, vol. 4(3), pages 1-19.
    21. Soyeon Caren Han & Yulu Liang & Hyunsuk Chung & Hyejin Kim & Byeong Ho Kang, 2016. "Chinese trending search terms popularity rank prediction," Information Technology and Management, Springer, vol. 17(2), pages 133-139, June.
    22. Sungchul Kim & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Novel Forecasting Methodology for Sustainable Management of Defense Technology," Sustainability, MDPI, vol. 7(12), pages 1-17, December.

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    More about this item

    JEL classification:

    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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    1. Delphi method in Wikipedia English
    2. デルファイ法 in Wikipedia Japanese
    3. Metoda Delphi in Wikipedia Romanian

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