Distributional Approach to Risk Preferences

Distributional Approach to Risk Preferences | 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 Distributional Approach to Risk Preferences Nir Chemaya, Charles Johnson, Brian Jabarian, Enoch Yeung, Gary Charness This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6787323/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 We propose a distributional framework for eliciting risk preferences that treats an individual’s attitude toward risk as a full probability distribution rather than a point estimate. By parameterising preferences with the flexible beta family, our approach encompasses the entire spectrum from extreme risk aversion to risk neutrality and even risk-seeking behaviour, while simultaneously allowing for heterogeneous stability of those attitudes across contexts. Our agent-based simulations show that (i) the true underlying preference distribution is recoverable with negligible bias and (ii) the precision of recovery is a systematic function of elicitation design richness, providing clear guidance for experimental design. Benchmarking on the comprehensive laboratory dataset of Holzmeister Schmidt (2021) confirms two central results: (1) out-of-sample predictive accuracy is at least on par with canonical point-estimate methods, and (2) our method delivers a second, policy-relevant moment—the subject-specific variance of risk taking—without sacrificing parsimony. Social science/Economics Business and commerce/Economics Full Text Additional Declarations There is NO Competing Interest. 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-6787323","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":469353237,"identity":"c0f861c3-f232-4fa6-a280-97852e3486dc","order_by":0,"name":"Nir Chemaya","email":"","orcid":"","institution":"UCSB","correspondingAuthor":false,"prefix":"","firstName":"Nir","middleName":"","lastName":"Chemaya","suffix":""},{"id":469353238,"identity":"16d87ed9-064d-41c5-bc12-806cd23f254b","order_by":1,"name":"Charles 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