Abstract
Investigating the bases of inter-individual differences in risk-taking is necessary to refine our cognitive and neural models of decision-making and to ultimately counter risky behaviors in real-life policy settings. However, recent evidence suggests that behavioral tasks fare poorly compared to standard questionnaires to measure individual differences in risk-taking. Crucially, using model-based measures of risk taking does not seem to improve reliability. Here, we put forward two possible – not mutually exclusive – explanations for these results and suggest future avenues of research to improve the assessment of inter-individual differences in risk-taking by combining repeated online testing and mechanistic computational models.
Highlights
In a recent series of studies Frey et al (2017) investigated the relationship between different measures of risk-sensitivity in a laboratory-based experiment involving over a thousand participants (N∼1500) (Frey et al, 2017; Pedroni et al, 2017)
We consider a simplified case involving two possible phenotypes, a risk-seeking phenotype and a risk-averse phenotype, and we propose a multi-layer model in which momentary risk attitude corresponds to the weighted sum of different sources of influence that change with different time constants (Figure 1)
Recent evidence based on large-scale behavioral testing shows that behavioral measures in cognitive tasks are outperformed by propensity measures from personality questionnaires, in terms of external validity and reliability
Summary
In a recent series of studies Frey et al (2017) investigated the relationship between different measures of risk-sensitivity in a laboratory-based experiment involving over a thousand participants (N∼1500) (Frey et al, 2017; Pedroni et al, 2017). Low external validity and-reliability is extremely worrying for the development of behavioral economics applications and (by extension) for the neuroeconomics research framework, where risk preferences are commonly assessed and elicited using behavioral tasks.
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