CDR Seminar: Renato Frey - Risk preference: Its psychometric structure, measurement, and an outlook on testing its generalizability to important life outcomes

Abstract

Risk preference is considered to be a key building block of human behavior, particularly so in psychology and economics. Yet little research has been conducted that integrates different measurement traditions, which have co-evolved largely in isolation from each other. Fundamental questions regarding the nature of the construct risk preference have thus remained unaddressed to date. I present evidence from a large-scale study, in which we collected 39 risk-taking measures from 1,507 participants (spanning three different measurement traditions, and including a retest subsample of 109 participants), which suggests that risk preference shares its psychometric structure with that of other major psychological traits: A general and stable factor of risk preference (“R”) as well as a set of more specific factors each accounted for about half of the explained variance. Our findings also highlight severe problems with using incentivized behavioral methods for eliciting risk preference. In the second part of the talk, I discuss potential sources underlying the general factor of risk preference, and give a preview of future work in which I will test the generalizability of risk preference to important life outcomes by means of predictive modeling.

About the speaker

Renato Frey received his Ph.D. from the University of Basel (with a dissertation on search and learning processes in decisions from experience) and spent two years as a post-doctoral researcher at the Max Planck Institute of Human Development, Berlin. He is now a researcher at the Center for Cognitive and Decision Sciences at the University of Basel, Switzerland, and an SNSF Ambizione fellow. Renato is interested in how people make decisions under risk and uncertainty, and studies in particular the nature of the construct risk preference (including its genetic underpinnings) and its generalizability to important life outcomes. In so doing, he implements psychometric, cognitive, and predictive modeling techniques, using data from both lab experiments as well as ecological assessments.