消费者跨场景决策依赖粗略的高阶不确定性表征，而非完全理性学习。
Boundedly Rational Meta-Learning in Sequential Consumer Choice
- 用分层实验模拟跨路线航空选择，研究经验如何迁移。
- 低阶近似元学习（如BRMDP(1)）比完全整合模型更贴近真实行为。
- 适合研究消费者行为建模与有限理性决策的学者参考。
许多消费者决策涉及在不确定环境下重复选择，一种情境中的经验可能影响另一情境的判断。例如，一个品牌在某个市场的使用体验会影响其在新场景下的认知。本文研究这种跨情境信息转移是否表现为元学习——即不同情境的经验更新更高阶信念，从而指导新情境的学习。在一项分层实验室任务中，参与者在多条航线上选择航空公司并观察噪声二值结果。结果显示，参与者在单一路线及跨路线均表现出改进，表明存在跨路线知识迁移。将人类选择与无转移、全集成元学习及受限理性元动态规划（BRMDP(D)，D为超后验抽样次数）模型对比，试次级似然分析显示，低D策略（尤其是BRMDP(1)）最佳预测人类行为。结果表明，消费者通过粗糙的高阶不确定性表征实现跨情境信息转移。
原文摘要 · Abstract (English)
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.
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