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Forecasting elections with agent-based modeling: Two live experiments

Author

Listed:
  • Ming Gao
  • Zhongyuan Wang
  • Kai Wang
  • Chenhui Liu
  • Shiping Tang

Abstract

Election forecasting has been traditionally dominated by subjective surveys and polls or methods centered upon them. We have developed a novel platform for forecasting elections based on agent-based modeling (ABM), which is entirely independent from surveys and polls. The platform uses statistical results from objective data along with simulation models to capture how voters have voted in past elections and how they are likely to vote in an upcoming election. We screen for models that can reproduce results that are very close to the actual results of historical elections and then deploy these selected models to forecast an upcoming election with simulations by combining extrapolated data from historical demographic record and more updated data on economic growth, employment, shock events, and other factors. Here, we report the results of two recent experiments of real-time election forecasting: the 2020 general election in Taiwan and six states in the 2020 general election in the United States. Our mostly objective method using ABM may transform how elections are forecasted and studied.

Suggested Citation

  • Ming Gao & Zhongyuan Wang & Kai Wang & Chenhui Liu & Shiping Tang, 2022. "Forecasting elections with agent-based modeling: Two live experiments," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-11, June.
    • Handle: RePEc:plo:pone00:0270194
    DOI: 10.1371/journal.pone.0270194
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    References listed on IDEAS

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    2. Gelman, Andrew & King, Gary, 1993. "Why Are American Presidential Election Campaign Polls So Variable When Votes Are So Predictable?," British Journal of Political Science, Cambridge University Press, vol. 23(4), pages 409-451, October.
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