标题： 强化学习与消费-储蓄行为 显示英文标题

标题： Reinforcement Learning and Consumption-Savings Behavior

摘要： 本文展示了强化学习如何解释经济衰退期间家庭消费行为中的两个令人困惑的实证模式。我构建了一个模型，其中代理人在收入不确定的情况下使用带有神经网络近似的Q学习来做出消费-储蓄决策，这与标准的理性预期假设不同。该模型复制了近期文献中的两个关键发现：(1) 以前流动性资产较低的失业家庭在刺激转移中的边际消费倾向（MPC）明显高于高资产家庭（0.50 vs 0.34），即使两组都没有面临借贷约束，这与Ganong等（2024）一致；以及(2) 在控制当前经济状况后，过去失业经历更多的家庭保持持续较低的消费水平，这是一种“创伤”效应，由Malmendier和Shen（2024）记录。与基于收入风险信念更新或事前异质性的现有解释不同，强化学习机制通过随着经验演变的价值函数近似误差同时产生更高的MPC和更低的消费水平。模拟结果与实证估计 closely 匹配，表明通过强化学习的自适应学习提供了一个统一的框架，以理解过去经历如何塑造当前消费行为，这超出了当前经济状况所能预测的范围。 显示英文摘要

摘要： This paper demonstrates how reinforcement learning can explain two puzzling empirical patterns in household consumption behavior during economic downturns. I develop a model where agents use Q-learning with neural network approximation to make consumption-savings decisions under income uncertainty, departing from standard rational expectations assumptions. The model replicates two key findings from recent literature: (1) unemployed households with previously low liquid assets exhibit substantially higher marginal propensities to consume (MPCs) out of stimulus transfers compared to high-asset households (0.50 vs 0.34), even when neither group faces borrowing constraints, consistent with Ganong et al. (2024); and (2) households with more past unemployment experiences maintain persistently lower consumption levels after controlling for current economic conditions, a "scarring" effect documented by Malmendier and Shen (2024). Unlike existing explanations based on belief updating about income risk or ex-ante heterogeneity, the reinforcement learning mechanism generates both higher MPCs and lower consumption levels simultaneously through value function approximation errors that evolve with experience. Simulation results closely match the empirical estimates, suggesting that adaptive learning through reinforcement learning provides a unifying framework for understanding how past experiences shape current consumption behavior beyond what current economic conditions would predict.