Boundedly Rational Meta-Learning in Sequential Consumer Choice

Many consumer decisions involve repeated choices under uncertainty, where experience in one context may inform decisions in another. For example, experience with a brand in one market or usage context may shape beliefs about that brand in a new context. We study whether such cross-context transfer takes the form of meta-learning, in which experience across contexts updates higher-order beliefs that guide learning in a new context. In a hierarchical laboratory task, participants choose among airlines across routes and observe noisy binary outcomes. Participants improve both within and across routes, indicating cross-route knowledge transfer. We compare human choices with no-transfer, fully integrated meta-learning, and boundedly rational meta dynamic programming policies, BRMDP(D), where D is the number of hyper-posterior draws used to approximate integration. Trial-by-trial likelihood comparisons show that low-D policies, especially BRMDP(1), best predict participant choices. The results suggest that consumers transfer information across contexts using coarse representations of higher-order uncertainty.