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A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment

Author

Listed:
  • Patrick Afflerbach

    (University of Augsburg)

  • Christopher Dun

    (University of Bayreuth
    Fraunhofer FIT)

  • Henner Gimpel

    (University of Augsburg
    Fraunhofer FIT)

  • Dominik Parak

    (University of Augsburg)

  • Johannes Seyfried

    (University of Augsburg)

Abstract

Research has shown that aggregation of independent expert judgments significantly improves the quality of forecasts as compared to individual expert forecasts. This “wisdom of crowds” (WOC) has sparked substantial interest. However, previous studies on strengths and weaknesses of aggregation algorithms have been restricted by limited empirical data and analytical complexity. Based on a comprehensive analysis of existing knowledge on WOC and aggregation algorithms, this paper describes the design and implementation of a static stochastic simulation model to emulate WOC scenarios with a wide range of parameters. The model has been thoroughly evaluated: the assumptions are validated against propositions derived from literature, and the model has a computational representation. The applicability of the model is demonstrated by investigating aggregation algorithm behavior on a detailed level, by assessing aggregation algorithm performance, and by exploring previously undiscovered suppositions on WOC. The simulation model helps expand the understanding of WOC, where previous research was restricted. Additionally, it gives directions for developing aggregation algorithms and contributes to a general understanding of the WOC phenomenon.

Suggested Citation

  • Patrick Afflerbach & Christopher Dun & Henner Gimpel & Dominik Parak & Johannes Seyfried, 2021. "A Simulation-Based Approach to Understanding the Wisdom of Crowds Phenomenon in Aggregating Expert Judgment," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(4), pages 329-348, August.
    • Handle: RePEc:spr:binfse:v:63:y:2021:i:4:d:10.1007_s12599-020-00664-x
    DOI: 10.1007/s12599-020-00664-x
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    1. Hurley, W. J. & Lior, D. U., 2002. "Combining expert judgment: On the performance of trimmed mean vote aggregation procedures in the presence of strategic voting," European Journal of Operational Research, Elsevier, vol. 140(1), pages 142-147, July.
    2. Stephen C. Hora & Benjamin R. Fransen & Natasha Hawkins & Irving Susel, 2013. "Median Aggregation of Distribution Functions," Decision Analysis, INFORMS, vol. 10(4), pages 279-291, December.
    3. Mohamed N. Jouini & Robert T. Clemen, 1996. "Copula Models for Aggregating Expert Opinions," Operations Research, INFORMS, vol. 44(3), pages 444-457, June.
    4. James K. Hammitt & Yifan Zhang, 2013. "Combining Experts’ Judgments: Comparison of Algorithmic Methods Using Synthetic Data," Risk Analysis, John Wiley & Sons, vol. 33(1), pages 109-120, January.
    5. Marc Keuschnigg & Christian Ganser, 2017. "Crowd Wisdom Relies on Agents’ Ability in Small Groups with a Voting Aggregation Rule," Management Science, INFORMS, vol. 63(3), pages 818-828, March.
    6. Kleijnen, Jack P. C., 1995. "Verification and validation of simulation models," European Journal of Operational Research, Elsevier, vol. 82(1), pages 145-162, April.
    7. Jannis Beese & M. Kazem Haki & Stephan Aier & Robert Winter, 2019. "Simulation-Based Research in Information Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 503-521, August.
    8. Richard P. Larrick & Jack B. Soll, 2006. "Erratum--Intuitions About Combining Opinions: Misappreciation of the Averaging Principle," Management Science, INFORMS, vol. 52(2), pages 309-310, February.
    9. Dalrymple, Douglas J., 1975. "Sales forecasting methods and accuracy," Business Horizons, Elsevier, vol. 18(6), pages 69-73, December.
    10. de Menezes, Lilian M. & W. Bunn, Derek & Taylor, James W., 2000. "Review of guidelines for the use of combined forecasts," European Journal of Operational Research, Elsevier, vol. 120(1), pages 190-204, January.
    11. David V. Budescu & Eva Chen, 2015. "Identifying Expertise to Extract the Wisdom of Crowds," Management Science, INFORMS, vol. 61(2), pages 267-280, February.
    12. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    13. Petrovic, Dobrila & Roy, Rajat & Petrovic, Radivoj, 1998. "Modelling and simulation of a supply chain in an uncertain environment," European Journal of Operational Research, Elsevier, vol. 109(2), pages 299-309, September.
    14. Colson, Abigail R. & Cooke, Roger M., 2017. "Cross validation for the classical model of structured expert judgment," Reliability Engineering and System Safety, Elsevier, vol. 163(C), pages 109-120.
    15. Robin M. Hogarth & Spyros Makridakis, 1981. "Forecasting and Planning: An Evaluation," Management Science, INFORMS, vol. 27(2), pages 115-138, February.
    16. Martin Bichler & Thomas Hess & Ramayya Krishnan & Peter Loos, 2014. "Emerging Research Areas in Business and Information Systems Engineering," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(1), pages 1-2, February.
    17. Eggstaff, Justin W. & Mazzuchi, Thomas A. & Sarkani, Shahram, 2014. "The effect of the number of seed variables on the performance of Cooke′s classical model," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 72-82.
    18. Alison Hubbard Ashton & Robert H. Ashton, 1985. "Aggregating Subjective Forecasts: Some Empirical Results," Management Science, INFORMS, vol. 31(12), pages 1499-1508, December.
    19. Robert Winter, 2009. "What in Fact is Fundamental Research in Business and Information Systems Engineering?," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 1(2), pages 192-199, April.
    20. Lee, Ju-Sung & Filatova, Tatiana & Ligmann-Zielinska, Arika & Hassani-Mahmooei, Behrooz & Stonedahl, Forrest & Lorscheid, Iris & Voinov, Alexey & Polhill, J. Gareth & Sun, Zhanli & Parker, Dawn C., 2015. "The complexities of agent-based modeling output analysis," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 18(4).
    21. Clemon, Robert T & Winkler, Robert L, 1986. "Combining Economic Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 39-46, January.
    22. Robert T. Clemen & Robert L. Winkler, 1999. "Combining Probability Distributions From Experts in Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 19(2), pages 187-203, April.
    23. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    24. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    25. repec:cup:judgdm:v:10:y:2015:i:2:p:130-143 is not listed on IDEAS
    26. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    27. Yates, J. Frank & McDaniel, Linda S. & Brown, Eric S., 1991. "Probabilistic forecasts of stock prices and earnings: The hazards of nascent expertise," Organizational Behavior and Human Decision Processes, Elsevier, vol. 49(1), pages 60-79, June.
    28. Asa B. Palley & Jack B. Soll, 2019. "Extracting the Wisdom of Crowds When Information Is Shared," Management Science, INFORMS, vol. 67(5), pages 2291-2309, May.
    29. Edward D. Van Wesep, 2016. "The Quality of Expertise," Management Science, INFORMS, vol. 62(10), pages 2937-2951, October.
    30. J. Eric Bickel, 2007. "Some Comparisons among Quadratic, Spherical, and Logarithmic Scoring Rules," Decision Analysis, INFORMS, vol. 4(2), pages 49-65, June.
    31. Cooke, Roger M. & Goossens, Louis L.H.J., 2008. "TU Delft expert judgment data base," Reliability Engineering and System Safety, Elsevier, vol. 93(5), pages 657-674.
    32. Eva Chen & David V. Budescu & Shrinidhi K. Lakshmikanth & Barbara A. Mellers & Philip E. Tetlock, 2016. "Validating the Contribution-Weighted Model: Robustness and Cost-Benefit Analyses," Decision Analysis, INFORMS, vol. 13(2), pages 128-152, June.
    33. Iris Lorscheid & Bernd-Oliver Heine & Matthias Meyer, 2012. "Opening the ‘black box’ of simulations: increased transparency and effective communication through the systematic design of experiments," Computational and Mathematical Organization Theory, Springer, vol. 18(1), pages 22-62, March.
    34. repec:cup:judgdm:v:6:y:2011:i:1:p:58-72 is not listed on IDEAS
    35. Clintin P. Davis-Stober & David V. Budescu & Stephen B. Broomell & Jason Dana, 2015. "The Composition of Optimally Wise Crowds," Decision Analysis, INFORMS, vol. 12(3), pages 130-143.
    36. McKenzie, Craig R.M. & Liersch, Michael J. & Yaniv, Ilan, 2008. "Overconfidence in interval estimates: What does expertise buy you?," Organizational Behavior and Human Decision Processes, Elsevier, vol. 107(2), pages 179-191, November.
    37. Richard P. Larrick & Jack B. Soll, 2006. "Intuitions About Combining Opinions: Misappreciation of the Averaging Principle," Management Science, INFORMS, vol. 52(1), pages 111-127, January.
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