Submission 453
What Drives Loss Aversion Across Learning Contexts in Risky Choice?
SymposiumTalk-02
Presented by: Nuno Busch
Loss aversion is one of the most prominent concepts in the study of decision-making under risk. Traditionally, previous research has investigated loss aversion in contexts where participants are aware of the payoff distributions of risky options between which they have to choose (i.e., decisions from description). But to what extent does loss aversion also emerge in situations in which the payoff distributions are initially unknown, and only learned from experiential sampling—that is, in decisions from experience? To investigate this question, we first conducted a meta-analysis, synthesizing and re-analyzing all existing datasets (total n > 430 per condition) that allow for a direct comparison of decisions from description and decisions from experience based on mixed gambles (i.e., where the options can lead to either a gain or a loss). Analyzing choices with cumulative prospect theory through a Bayesian multi-level modeling approach revealed substantial differences between learning modes, demonstrating that loss aversion is more pronounced in decisions made from experience compared to decisions made from description. Second, and building on these findings, we ran a large-scale study to address potential caveats of the meta-analysis (e.g., few mixed choice problems, small samples). Our results corroborate the novel and robust description-experience gap. In further analyses, we illuminate potential contributors to the gap in loss aversion between description and experiential paradigms.