Meta-Predictions Enhance Social Learning

Meta-Predictions Enhance Social Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Meta-Predictions Enhance Social Learning Yunhao Zhang, Eaman Jahani, Douglas Guilbeault, Juliana Schroeder This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7539099/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract People learn from others to improve their judgments across a wide range of tasks, from financial forecasting to medical diagnosis. To facilitate social learning, people have historically either relied on simple aggregation methods, such as the average or majority opinion of a group, or on noisy signals of others’ expertise (e.g., confidence levels). However, these methods can be unreliable and inaccurate. Here, we test the theory that social learning can be enhanced through a signaling mechanism that indicates which group members are better at predicting others’ predictions, which we term “meta-prediction accuracy”. Although recently built algorithms leverage meta-predictions to improve the aggregation of people’s estimates, these algorithms are highly complex and difficult to communicate to individuals, and thus not well-suited for social learning. We develop a more intuitive yet equally accurate algorithm that harnesses the power of meta-predictions to improve real-time social learning among interacting decision-makers. Across two pre-registered experiments, we show that providing individual decision-makers with group members’ meta-prediction accuracy consistently improves individuals’ estimation accuracy more than providing other popular signaling mechanisms. This holds regardless of the complexity of the problem and whether estimates are binary or probabilistic. Together, these findings demonstrate that signaling meta-prediction accuracy in social groups reliably enhances social learning. Scientific community and society/Social sciences/Decision making Scientific community and society/Social sciences/Interdisciplinary studies Wisdom of Crowds Social Influence Social Learning Meta-prediction Prediction Full Text Additional Declarations There is NO Competing Interest. Supplementary Files MetaSocialInfluenceNCommSI.pdf Supplementary Information Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7539099","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":526137067,"identity":"0d1f270a-91e3-4de1-869b-59a194848079","order_by":0,"name":"Yunhao 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