The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Predicting Experimental Results: Who Knows What?
Abstract
We analyze how academic experts and nonexperts forecast the results of 15 piece-rate and behavioral treatments in a real-effort task. The average forecast of experts closely predicts the experimental results, with a strong wisdom-of-crowds effect: the average forecast outperforms 96 percent of individual forecasts. Citations, academic rank, field, and contextual experience do not correlate with accuracy. Experts as a group do better than nonexperts, but not if accuracy is defined as rank-ordering treatments. Measures of effort, confidence, and revealed ability are predictive of forecast accuracy to some extent and allow us to identify “superforecasters” among the nonexperts.
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Cites
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BackgroundInside the Turk: Understanding Mechanical Turk as a Participant Pool
Paolacci, G., & Chandler, J. (2014) · Current Directions in Psychological Science · doi:10.1177/0963721414531598 · Cited by 2,136
Evaluating replicability of laboratory experiments in economics
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Using prediction markets to estimate the reproducibility of scientific research
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Cited by
Identification of and correction for publication bias
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Crowdsourcing hypothesis tests: Making transparent how design choices shape research results.
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What Do Editors Maximize? Evidence from Four Economics Journals
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Why Prosocial Referral Incentives Work: The Interplay of Reputational Benefits and Action Costs
Gershon, R., Cryder, C., & John, L. K. (2020) · Journal of Marketing Research · doi:10.1177/0022243719888440 · Cited by 60
Metadata
- Authors
- DellaVigna, S., & Pope, D.
- Venue
- Journal of Political Economy
- Publisher
- University of Chicago Press
- Published
- 2018-12
- Volume
- 126
- Issue
- 6
- Pages
- 2410-2456
- ISSN
- 0022-3808 (ISSN-L)
- 1537-534X
- OpenAlex
- W2892216368
- Cited by
- 183
- References
- 10
- FWCI
- 15.59
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
