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The wisdom of crowds: Applying Condorcet’s jury theorem to forecasting US presidential elections

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  • Murr, Andreas E.

Abstract

Increasingly, professional forecasters rely on citizen forecasts when predicting election results. Following this approach, forecasters predict the winning party to be the one which most citizens have said will win. This approach predicts winners and vote shares well, but related research has shown that some citizens forecast better than others. Extensions of Condorcet’s jury theorem suggest that naïve citizen forecasting can be improved by delegating the forecasting to the most competent citizens and by weighting their forecasts by their level of competence. Indeed, doing so increases both the accuracy of vote share predictions and the number of states forecast correctly. Allocating the state’s electoral votes to the candidate who the most weighted delegates say will win yields a simple but successful forecasting model of the US Presidency. The ‘wisdom of crowds’ model predicts eight presidential elections out of nine correctly. The results suggest that delegating and weighting provide easy ways to improve citizen forecasting.

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  • Murr, Andreas E., 2015. "The wisdom of crowds: Applying Condorcet’s jury theorem to forecasting US presidential elections," International Journal of Forecasting, Elsevier, vol. 31(3), pages 916-929.
    • Handle: RePEc:eee:intfor:v:31:y:2015:i:3:p:916-929
    DOI: 10.1016/j.ijforecast.2014.12.002
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    3. Liu, Yezheng & Ye, Chang & Sun, Jianshan & Jiang, Yuanchun & Wang, Hai, 2021. "Modeling undecided voters to forecast elections: From bandwagon behavior and the spiral of silence perspective," International Journal of Forecasting, Elsevier, vol. 37(2), pages 461-483.
    4. Leiter, Debra & Murr, Andreas & Rascón Ramírez, Ericka & Stegmaier, Mary, 2018. "Social networks and citizen election forecasting: The more friends the better," International Journal of Forecasting, Elsevier, vol. 34(2), pages 235-248.
    5. Khan, Urmee & Lieli, Robert P., 2018. "Information flow between prediction markets, polls and media: Evidence from the 2008 presidential primaries," International Journal of Forecasting, Elsevier, vol. 34(4), pages 696-710.
    6. Temporão, Mickael & Dufresne, Yannick & Savoie, Justin & Linden, Clifton van der, 2019. "Crowdsourcing the vote: New horizons in citizen forecasting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 1-10.
    7. Steve Alpern & Bo Chen, 2022. "Optimizing voting order on sequential juries: a median voter theorem and beyond," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 58(3), pages 527-565, April.

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