Political Prediction and the Wisdom of Crowds
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3715928.3737483.pdf
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1.44 MB
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Adobe PDF
Checksum (MD5)
ce4e12a7cb616e84fce28ad2b1372fe0
Author(s)
Sethi, Rajiv
Seager, Julie
Morstatter, Fred
Benjamin, Daniel
Hammell, Anna
Liu, Tianshuo
Patel, Sachi
Subramanian, Ramya
Date Issued
August 3, 2025
Publisher
ACM|Collective Intelligence Conference
Citation
Rajiv Sethi, Julie Seager, Fred Morstatter, Daniel Benjamin, Anna Hammell, Tianshuo Liu, Sachi Patel, and Ramya Subramanian. 2025. Political Prediction and the Wisdom of Crowds. In Proceedings of the ACM Collective Intelligence Conference (CI '25). Association for Computing Machinery, New York, NY, USA, 214–225.
Version
Final published version
Abstract
We evaluate the relative forecasting performance of three statistical models and a prediction market for several outcomes decided during the November 2024 elections in the United States—the winner of the presidency, the popular vote, fifteen competitive states in the Electoral College, eleven Senate races, and thirteen House races. We argue that conventional measures of predictive accuracy such as the average daily Brier score reward modeling flaws that result in predicable reversals, as long as such movements are in a direction that is aligned with the eventual outcome. Instead, we adopt a test based on the idea that the strength of a model can be measured by the profitability of a trader who believes its forecasts and bets on the market based on this belief. The results of this test depend on the risk preferences with which the trader is endowed, but we show that within a large parameter range this does not lead to ranking reversals. We find that all models failed to beat the market in the headline contract but some did so convincingly in contracts referencing less visible races.
Description
CI 2025, San Diego, CA, USA
MIT Department
Sloan School of Management
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3715928.3737483
