论文标题
深入的学习使解决丰富的离散选择生命周期模型可以分析社会保障改革
Deep reinforced learning enables solving rich discrete-choice life cycle models to analyze social security reforms
论文作者
论文摘要
劳动力供应的离散选择生命周期模型可用于估计社会保障改革如何影响就业率。在生命周期模型中，必须解决个人生命过程中最佳的就业选择。大多数情况下，生命周期模型是通过动态编程来解决的，当状态空间很大时，这是不可行的，就像现实的生命周期模型中一样。解决复杂的生命周期模型需要使用近似方法，例如增强学习算法。我们比较了深入的学习算法ACKTR和动态编程如何解决相对简单的生命周期模型。为了分析结果，我们使用一系列统计信息，并比较各个州所得的最佳就业选择。统计数据表明，ACKTR的产生几乎与动态编程一样好。在定性上，动态编程比ACKTR产生的总体就业概况更多。用ACKTR获得的结果为动态编程的结果提供了良好但并不完美的近似值。除了基线案例外，我们还分析了两项社会保障改革：（1）退休年龄增加，以及（2）普遍的基本收入。我们的结果表明，加强学习算法对于发展社会保障改革可能具有重要价值。
Discrete-choice life cycle models of labor supply can be used to estimate how social security reforms influence employment rate. In a life cycle model, optimal employment choices during the life course of an individual must be solved. Mostly, life cycle models have been solved with dynamic programming, which is not feasible when the state space is large, as often is the case in a realistic life cycle model. Solving a complex life cycle model requires the use of approximate methods, such as reinforced learning algorithms. We compare how well a deep reinforced learning algorithm ACKTR and dynamic programming solve a relatively simple life cycle model. To analyze results, we use a selection of statistics and also compare the resulting optimal employment choices at various states. The statistics demonstrate that ACKTR yields almost as good results as dynamic programming. Qualitatively, dynamic programming yields more spiked aggregate employment profiles than ACKTR. The results obtained with ACKTR provide a good, yet not perfect, approximation to the results of dynamic programming. In addition to the baseline case, we analyze two social security reforms: (1) an increase of retirement age, and (2) universal basic income. Our results suggest that reinforced learning algorithms can be of significant value in developing social security reforms.
