The investigation of decisions under risk has mainly followed one of two approaches.One relies on observing choices between lotteries in which economic primitives (outcome magnitudes, probabilities, and domains (i.e., gains and losses)) are varied systematically, and this information is described to participants. The systematic variation of the economic primitives allows to formally describe behavior with expectation-based models such as expected utility theory or cumulative prospect theory (CPT), arguably the most prominent descriptive theories of risky choice. One drawback, however, is that lottery tasks can seem artificial, likely reducing the external or ecological validity. A second more naturalistic approach employs dynamic paradigms that mimic features of real-life risky situations and are assumed to have higher ecological validity. Because key information are often not provided to the decision maker, it is impossible to apply the same models as in the first approach. The goal of the present work is to integrate both approaches, by developing models for the "hot" Columbia Card Task (CCT), a task that combines a dynamic decision situation with systematic trial-to-trial variation in economic primitives. In a model comparison on the basis of the data of 191 participants, we identified a best-performing model that describes behavior as a function of CPT’s main components, outcome sensitivity, probability weighting, and loss aversion. Our work therefore provides a framework that allows the description of risk-taking behavior in a naturalistic dynamic task based on key psychological constructs (e.g., loss aversion, probability weighting) that are rooted in the factorial variation of economic primitives.
Fitness-Maximizers Employ Pessimistic Probability Weighting for Decisions Under Risk