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Prediction Markets
Open accessAbstract
We analyze the extent to which simple markets can be used to aggregate disperse information into efficient forecasts of uncertain future events. Drawing together data from a range of prediction contexts, we show that market-generated forecasts are typically fairly accurate, and that they outperform most moderately sophisticated benchmarks. Carefully designed contracts can yield insight into the market's expectations about probabilities, means and medians, and also uncertainty about these parameters. Moreover, conditional markets can effectively reveal the market's beliefs about regression coefficients, although we still have the usual problem of disentangling correlation from causation. We discuss a number of market design issues and highlight domains in which prediction markets are most likely to be useful.
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“Prediction markets have also been successfully used in several other fields such as sports, entertainment, and politics ( 25 , 59-62 ).”
From Evaluating replicability of laboratory experiments in economics (2016) · cites this paper as [59]
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Metadata
- Authors
- Wolfers, J., & Zitzewitz, E.
- Affiliations
- National Bureau of Economic Research (US)
- Stanford University (US)
- ORCID
- Wolfers, Justin
- Venue
- Journal of Economic Perspectives
- Publisher
- American Economic Association
- Published
- 2004-05-01
- Volume
- 18
- Issue
- 2
- Pages
- 107-126
- ISSN
- 0895-3309 (ISSN-L)
- OpenAlex
- W4256232447
- Cited by
- 889
- References
- 17
- Citations by year
- 2026: 85 · 2025: 36 · 2024: 33 · 2023: 31 · 2022: 44 · 2021: 31 · 2020: 28 · 2019: 33 · 2018: 42 · 2017: 39 · 2016: 25 · 2015: 44 · 2014: 42 · 2013: 56 · 2012: 67
- FWCI
- 43.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Journal of Economic Perspectives (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Sports Analytics and Performance
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- prediction markets, information aggregation, forecast accuracy, market design, regression coefficients, correlation versus causation, probability elicitation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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