论文标题
部分可观测时空混沌系统的无模型预测
Back to the Surplus: An Unorthodox Neoclassical Model of Growth, Distribution and Unemployment with Technical Change
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
The article examines how institutions, automation, unemployment and income distribution interact in the context of a neoclassical growth model where profits are interpreted as a surplus over costs of production. Adjusting the model to the experience of the US economy, I show that joint variations in labor institutions and technology are required to provide reasonable explanations for the behavior of income shares, capital returns, unemployment, and the big ratios in macroeconomics. The model offers new perspectives on recent trends by showing that they can be analyzed by the interrelation between the profit-making capacity of capitalist economies and the political environment determining labor institutions.
