The nature of the construct risk preference and its measurement

Risk preference is considered to be a key building block of human behavior, in particular in psychology and economics. But to what extent is there a general factor of risk preference, R, akin to g, the general factor of intelligence? And can risk preference be regarded a stable psychological trait? These conceptual issues have persisted until recently because few attempts have been made to bring together risk-taking measures from different measurement traditions (self-reported propensity measures assessing "stated preferences"; incentivized behavioral measures eliciting "revealed preferences"; frequency measures assessing actual risky activities).

In the Basel-Berlin Risk Study, we thus employed a comprehensive psychometric approach and collected a battery of 39 risk-taking measures from 1,507 healthy adults (with a subsample of 109 participants completing a retest session after six months). The 39 measures were sampled from all three measurement traditions.

Network plot showing the correlations between different risk-taking measures.

Our findings suggest a profound gap between incentivized behavioral measures on the one hand, and propensity and frequency measures on the other. Moreover, consistency across different behavioral measures was surprisingly weak. Psychometric modeling analyses (see figure below) revealed a general factor of risk preference, R, which emerged from stated preferences, and generalized to specific and actual real-world risky activities (e.g., smoking). This general factor accounted for 61% of the explained variance. Moroever, R proved highly reliable across time (in the retest sample, across a period of six months), indicative of a stable psychological trait.

Bifactor model showing how much variance the general factor of risk preference (R) and the specific factors (F1-F7) explain in each of the different measures.

In a second paper, we exclusively focused on the behavioral measures (using the same dataset) and report follow-up analyses on the potential reasons underlying the observed inconcistencies between the behavioral elcitiation methods. However, extensive cognitive modeling analyses did not help to overcome the poor consistency of these tasks, and even on the level of model parameters this picture did not change. A potential reason for this "risk elicitation puzzle" is that people recruit different cognitive strategies in the different behavioral tasks.

In sum, our results offer a first step towards a general mapping of the construct risk preference, which encompasses both general and domain-specific components. In particular the observation of the general and stable factor may have important implications for future investigations of the biological foundations of risk preference (i.e., its genetic underpinnings and neural architecture). Moroever, these results have direct implications for the measurement of risk preference, both in the laboratory and "in the wild": Ideally, risk preference is assessed with multiple self-report measures, and our findings would advise against using most of the (incentivized) behavioral measures. Finally, should a comprehensive assessment not be feasible, some of the individual (self-report) measures may be used as proxies for R.

Key references

Frey, R., Pedroni, A., Mata, R., Rieskamp, J., & Hertwig, R. (2017). Risk preference shares the psychometric structure of major psychological traits. Science Advances, 3, e1701381. doi:10.1126/sciadv.1701381 View online | Download PDF

Pedroni, A., Frey, R., Bruhin, A., Dutilh, G., Hertwig, R., & Rieskamp, J. (2017). The risk elicitation puzzle. Nature Human Behaviour. doi:10.1038/s41562-017-0219-x

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